IDEAS home Printed from https://ideas.repec.org/e/c/pgi20.html

Raffaella Giacomini

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.

    Mentioned in:

    1. Forecast comparisons in unstable environments (Journal of Applied Econometrics 2010) in ReplicationWiki ()

Working papers

  1. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.

    Cited by:

    1. Mathias Krogh & Giovanni Pellegrino, 2024. "Real Activity and Uncertainty Shocks: The Long and the Short of It," "Marco Fanno" Working Papers 0310, Dipartimento di Scienze Economiche "Marco Fanno".
    2. Marco Stenborg Petterson & David Seim & Jesse M. Shapiro, 2023. "Bounds on a Slope from Size Restrictions on Economic Shocks," American Economic Journal: Microeconomics, American Economic Association, vol. 15(3), pages 552-572, August.
    3. Emanuele Bacchiocchi & Toru Kitagawa, 2021. "On global identification in structural vector autoregressions," Papers 2102.04048, arXiv.org, revised Jul 2026.
    4. Yoshida, Yushi & Zhai, Weiyang, 2025. "Can exchange rate pass-throughs be perverse? A robust multiple-prior Bayesian SVAR approach," Journal of International Money and Finance, Elsevier, vol. 154(C).
    5. Annika Camehl & Malte Rieth, 2023. "Disentangling COVID-19, Economic Mobility, and Containment Policy Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(4), pages 217-248, October.
    6. Matthew Read, 2021. "Algorithms for Inference in SVARs Identified with Sign and Zero Restrictions," Papers 2109.10676, arXiv.org, revised Jan 2022.
    7. Matthew Read, 2026. "Shock-percentile Restrictions for SVARs," RBA Research Discussion Papers rdp2026-01, Reserve Bank of Australia.
    8. Andreasen, Martin M. & Caggiano, Giovanni & Castelnuovo, Efrem & Pellegrino, Giovanni, 2024. "Does risk matter more in recessions than in expansions? Implications for monetary policy," Journal of Monetary Economics, Elsevier, vol. 143(C).
    9. FUJITA, Shumpei & IIBOSHI, Hirokuni & SHINTANI, Mototsugu, 2026. "Macroeconomic Effects of Unconventional Monetary Policy in Japan : Analysis Using Narrative Sign Restrictions," Discussion paper series HIAS-E-156, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    10. Régis Barnichon & Geert Mesters, 2025. "Innovations Meet Narratives -Improving the Power-Credibility Trade-off in Macro," Working Papers 1475, Barcelona School of Economics.
    11. Zhai, Weiyang, 2025. "Gasoline price pass-through into CPI inflation: Evidence from a structural VAR," International Economics, Elsevier, vol. 184(C).
    12. Thorsten Drautzburg & Jonathan H. Wright, 2021. "Refining Set-Identification in VARs through Independence," Working Papers 21-31, Federal Reserve Bank of Philadelphia.
    13. Andrea Carriero & Alessio Volpicella, 2022. "Generalizing the Max Share Identification to multiple shocks identification: an Application to Uncertainty," School of Economics Discussion Papers 0322, School of Economics, University of Surrey.
    14. Efrem Castelnuovo & Giovanni Pellegrino & Laust L. Saerkjaer, 2025. "Monetary Policy Shocks and Narrative Restrictions: Rules Matter," "Marco Fanno" Working Papers 0328, Dipartimento di Scienze Economiche "Marco Fanno".
    15. Lukas Berend & Jan Pruser, 2025. "Sharpening Identification in Large Structural VARs Using Narrative Restrictions," Papers 2505.19244, arXiv.org, revised May 2026.
    16. Emanuele Bacchiocchi & Toru Kitagawa, 2021. "A note on global identi?cation in structural vector autoregressions," CeMMAP working papers CWP03/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Camehl, Annika & Rieth, Malte, 2023. "Disentangling COVID-19, Economic Mobility, and Containment Policy Shocks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(4), pages 217-248.
    18. Carriero, Andrea & Marcellino, Massimiliano & Tornese, Tommaso, 2024. "Blended identification in structural VARs," Journal of Monetary Economics, Elsevier, vol. 146(C).
    19. Herwartz, Helmut & Wang, Shu, 2023. "Point estimation in sign-restricted SVARs based on independence criteria with an application to rational bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).

  2. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2019. "Robust Bayesian Inference in Proxy SVARs," CeMMAP working papers CWP38/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Christian Hansen & Lingwei Kong & Weining Wang, 2025. "Plausible GMM: a quasi-bayesian approach," CeMMAP working papers 14/25, Institute for Fiscal Studies.
    2. Hilde C. Bjørnland & Jamie L. Cross & Jonas Hölz, 2025. "Re-visiting the Relationship Between Oil Prices and Monetary Policy," Working Papers No 04/2025, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Robin Braun & Ralf Brüggemann, 2020. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2020-01, Department of Economics, University of Konstanz.
    4. Matthew Read, 2023. "Estimating the Effects of Monetary Policy in Australia Using Sign‐restricted Structural Vector Autoregressions," The Economic Record, The Economic Society of Australia, vol. 99(326), pages 329-358, September.
    5. Martin Bruns & Helmut Lütkepohl, 2026. "Review of Proxy Vector Autoregressive Analysis," Discussion Papers of DIW Berlin 2155, DIW Berlin, German Institute for Economic Research.
    6. Breitenlechner, Max & Georgiadis, Georgios & Schumann, Ben, 2022. "What goes around comes around: How large are spillbacks from US monetary policy?," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 45-60.
    7. Jacobi Liana & Kwok Chun Fung & Ramírez-Hassan Andrés & Nghiem Nhung, 2024. "Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 403-434, April.
    8. Arias, Jonas E. & Rubio-Ramírez, Juan F. & Waggoner, Daniel F., 2021. "Inference in Bayesian Proxy-SVARs," Journal of Econometrics, Elsevier, vol. 225(1), pages 88-106.
    9. Angelini, Giovanni & Cavaliere, Giuseppe & Fanelli, Luca, 2024. "An identification and testing strategy for proxy-SVARs with weak proxies," Journal of Econometrics, Elsevier, vol. 238(2).
    10. Martin Bruns & Michele Piffer, 2021. "Monetary policy shocks over the business cycle: Extending the Smooth Transition framework," University of East Anglia School of Economics Working Paper Series 2021-07, School of Economics, University of East Anglia, Norwich, UK..
    11. Budnik, Katarzyna & Rünstler, Gerhard, 2020. "Identifying structural VARs from sparse narrative instruments: dynamic effects of U.S. macroprudential policies," Working Paper Series 2353, European Central Bank.
    12. Fierro, Luca Eduardo & Martinoli, Mario, 2025. "An empirical inquiry into the distributional consequences of energy price shocks," Journal of International Money and Finance, Elsevier, vol. 159(C).
    13. van Dijk Herman K., 2024. "Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 155-176, April.
    14. Yang Yang & Ren Zhang, 2025. "Combining Proxies and Narrative Sign Restrictions: Revisiting the Effects of Technology Shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(5), pages 554-568, August.
    15. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.
    16. Hernández-Román, Luis G., 2026. "Monetary policy trade-offs amid global supply chain disruptions," Journal of Monetary Economics, Elsevier, vol. 160(C).
    17. Yang, Yang & Tang, Yanling & Cheng, Kai, 2023. "Spillback effects of US unconventional monetary policy," Finance Research Letters, Elsevier, vol. 53(C).
    18. Martin Bruns & Helmut Lütkepohl, 2026. "Review of Proxy Vector and Autoregressive Analysis," University of East Anglia School of Economics Working Paper Series 2026-01, School of Economics, University of East Anglia, Norwich, UK..
    19. Paul Ho, 2020. "Global Robust Bayesian Analysis in Large Models," Working Paper 20-07, Federal Reserve Bank of Richmond.
    20. Matthew Read, 2022. "Algorithms for inference in SVARs identified with sign and zero restrictions [Identification and inference with ranking restrictions]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 699-718.
    21. Emanuele Bacchiocchi & Toru Kitagawa, 2020. "Locally- but not globally-identified SVARs," CeMMAP working papers CWP40/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Martin Bruns & Helmut Lutkepohl & James McNeil, 2024. "Avoiding Unintentionally Correlated Shocks in Proxy Vector Autoregressive Analysis," University of East Anglia School of Economics Working Paper Series 2024-05, School of Economics, University of East Anglia, Norwich, UK..
    23. Martínez-Hernández, Catalina, 2020. "Disentangling the effects of multidimensional monetary policy on inflation and inflation expectations in the euro area," Discussion Papers 2020/18, Free University Berlin, School of Business & Economics.
    24. Allan W. Gregory & James McNeil & Gregor W. Smith, 2022. "US Fiscal Policy Shocks: Proxy-SVAR Overidentification via GMM," Working Paper 1461, Economics Department, Queen's University.
    25. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    26. Georgiadis, Georgios & Müller, Gernot J. & Schumann, Ben, 2021. "Global risk and the dollar," Working Paper Series 2628, European Central Bank.
    27. Sascha A. Keweloh & Mathias Klein & Jan Pruser, 2023. "Estimating Fiscal Multipliers by Combining Statistical Identification with Potentially Endogenous Proxies," Papers 2302.13066, arXiv.org, revised Aug 2025.
    28. Martin Bruns & Sascha A. Keweloh, 2023. "Testing for Strong Exogeneity in Proxy-VARS," University of East Anglia School of Economics Working Paper Series 2023-07, School of Economics, University of East Anglia, Norwich, UK..
    29. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2022. "Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    30. Fanelli, Luca & Marsi, Antonio, 2022. "Sovereign spreads and unconventional monetary policy in the Euro area: A tale of three shocks," European Economic Review, Elsevier, vol. 150(C).
    31. Bruns, Martin & Lütkepohl, Helmut, 2025. "Comparing external and internal instruments for vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 177(C).
    32. Nguyen, Lam, 2025. "Bayesian inference in proxy SVARs with incomplete identification: Re-evaluating the validity of monetary policy instruments," Journal of Monetary Economics, Elsevier, vol. 155(C).

  3. Gaglianone, Wagner & Giacomini, Raffaella & Issler, Joao & Skreta, Vasiliki, 2019. "Incentive-driven Inattention," CEPR Discussion Papers 13619, Centre for Economic Policy Research.

    Cited by:

    1. Roc Armenter & Michèle Müller-Itten & Zachary Strangebye, 2021. "Geometric Methods for Finite Rational Inattention," Working Papers 21-30, Federal Reserve Bank of Philadelphia.
    2. Wagner Piazza Gaglianone & João Victor Issler & Silvia Maria Matos, 2017. "Applying a microfounded-forecasting approach to predict Brazilian inflation," Empirical Economics, Springer, vol. 53(1), pages 137-163, August.
    3. Qiu, Yajie & Deschamps, Bruno, 2025. "Peer influence in macroeconomic predictions," Journal of Economic Behavior & Organization, Elsevier, vol. 236(C).
    4. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    5. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," Sciences Po Economics Publications (main) hal-03878692, HAL.
    6. Roc Armenter & Michèle Müller-Itten & Zachary Stangebye, 2020. "Rational Inattention via Ignorance Equivalence," Working Papers 20-24, Federal Reserve Bank of Philadelphia.
    7. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    8. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
    9. Isaac Baley & Javier Turen, 2024. "Lumpy forecasts," Economics Working Papers 1898, Department of Economics and Business, Universitat Pompeu Fabra.
    10. Marta Baltar Moreira Areosa & Wagner Piazza Gaglianone, 2023. "Anchoring Long-term VAR Forecasts Based On Survey Data and State-space Models," Working Papers Series 574, Central Bank of Brazil, Research Department.
    11. Zidong An & Salem Abo‐Zaid & Xuguang Simon Sheng, 2023. "Inattention and the impact of monetary policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 623-643, June.
    12. Wagner Piazza Gaglianone, 2017. "Empirical Findings on Inflation Expectations in Brazil: a survey," Working Papers Series 464, Central Bank of Brazil, Research Department.

  4. Raffaella Giacomini & Toru Kitagawa & Harald Uhlig, 2019. "Estimation Under Ambiguity," CeMMAP working papers CWP24/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    2. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," Papers 2011.03153, arXiv.org, revised Dec 2020.
    3. Xinyu Dai & Daniel Chen & Yian Qian, 2026. "Dynamic Decision-Making under Model Misspecification: A Stochastic Stability Approach," Papers 2602.17086, arXiv.org, revised Jul 2026.
    4. Paul Ho, 2020. "Global Robust Bayesian Analysis in Large Models," Working Paper 20-07, Federal Reserve Bank of Richmond.
    5. Shikun Ke & José Luis Montiel Olea & James Nesbit, 2024. "Robust machine learning algorithms for text analysis," Quantitative Economics, Econometric Society, vol. 15(4), pages 939-970, November.
    6. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    7. Martin Bruns & Michele Piffer, 2023. "A new posterior sampler for Bayesian structural vector autoregressive models," Quantitative Economics, Econometric Society, vol. 14(4), pages 1221-1250, November.
    8. Timothy Christensen & Benjamin Connault, 2023. "Counterfactual Sensitivity and Robustness," Econometrica, Econometric Society, vol. 91(1), pages 263-298, January.

  5. Laurent Ferrara & Menzie Chinn & Raffaella Giacomini, 2018. "Impact of uncertainty shocks on the global economy," Post-Print hal-01635944, HAL.

    Cited by:

    1. Laurent Ferrara & Stéphane Lhuissier & Fabien Tripier, 2017. "Uncertainty Fluctuations: Measures, Effects and Macroeconomic Policy Challenges," CEPII Policy Brief 2017-20, CEPII research center.
    2. Bianconi, Marcelo & Esposito, Federico & Sammon, Marco, 2021. "Trade policy uncertainty and stock returns," Journal of International Money and Finance, Elsevier, vol. 119(C).
    3. Lastauskas, Povilas & Nguyen, Anh Dinh Minh, 2023. "Global impacts of US monetary policy uncertainty shocks," Journal of International Economics, Elsevier, vol. 145(C).
    4. Lastauskas, Povilas & Nguyen, Anh Dinh Minh, 2024. "Spillover effects of US monetary policy on emerging markets amidst uncertainty," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 92(C).
    5. Hammoudeh, Shawkat & Uddin, Gazi Salah & Sousa, Ricardo M. & Wadström, Christoffer & Sharmi, Rubaiya Zaman, 2022. "Do pandemic, trade policy and world uncertainties affect oil price returns?," Resources Policy, Elsevier, vol. 77(C).
    6. Costantini, Mauro & Sousa, Ricardo M., 2022. "What uncertainty does to euro area sovereign bond markets: Flight to safety and flight to quality," Journal of International Money and Finance, Elsevier, vol. 122(C).
    7. Ömer YALÇINKAYA & Ali Kemal ÇELİK, 2021. "The Impact of Global Uncertainties on Economic Growth: Evidence from the US Economy (1996: Q1-2018: Q4)," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 35-54, June.
    8. Wang, Jingyi & Liu, Weijiang & Wang, Shu & Zhou, Baicheng, 2026. "Waves of uncertainty: Time-varying spillovers of China’s economic policies on belt and road economies," Economic Analysis and Policy, Elsevier, vol. 90(C), pages 238-265.

  6. Raffaella Giacomini & Toru Kitagawa, 2018. "Robust Bayesian inference for set-identified models," CeMMAP working papers CWP61/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2023. "Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP012023, School of Economics, University of Queensland, Australia.
    2. Martin Geiger & Jochen Güntner, 2019. "How are oil supply shocks transmitted to the U.S. economy?," Economics working papers 2019-13, Department of Economics, Johannes Kepler University Linz, Austria.
    3. Inoue, Atsushi & Kilian, Lutz, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, Centre for Economic Policy Research.
    4. Mathias Krogh & Giovanni Pellegrino, 2024. "Real Activity and Uncertainty Shocks: The Long and the Short of It," "Marco Fanno" Working Papers 0310, Dipartimento di Scienze Economiche "Marco Fanno".
    5. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    6. Cheaheon Lim, 2026. "Partially Identified Ambiguity," Papers 2602.07634, arXiv.org, revised Jun 2026.
    7. Arias, Jonas & Fernández-Villaverde, Jesús & Rubio-Ramírez, Juan Francisco & Shin, Minchul, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CEPR Discussion Papers 15951, Centre for Economic Policy Research.
    8. Emanuele Bacchiocchi & Toru Kitagawa, 2021. "On global identification in structural vector autoregressions," Papers 2102.04048, arXiv.org, revised Jul 2026.
    9. Philippe Andrade & Filippo Ferroni & Leonardo Melosi, 2025. "Higher-order Moment Inequality Restrictions for SVARs," Working Papers 25-3, Federal Reserve Bank of Boston.
    10. Francesco Fusari, 2023. "Identifying Monetary Policy Shocks Through External Variable Constraints," School of Economics Discussion Papers 0123, School of Economics, University of Surrey.
    11. Yoshida, Yushi & Zhai, Weiyang, 2025. "Can exchange rate pass-throughs be perverse? A robust multiple-prior Bayesian SVAR approach," Journal of International Money and Finance, Elsevier, vol. 154(C).
    12. Annika Camehl & Malte Rieth, 2023. "Disentangling COVID-19, Economic Mobility, and Containment Policy Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(4), pages 217-248, October.
    13. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2020. "Robust Bayesian Inference in Proxy SVARs," CEPR Discussion Papers 14626, Centre for Economic Policy Research.
    14. Francesco Zanetti & Xiwen Bai & Jesús Fernández-Villaverde & Yiliang Li, 2026. "State Dependence of Monetary Policy During Global Supply Chain Disruptions," CIGS Working Paper Series 26-007E, The Canon Institute for Global Studies.
    15. Stéphane Bonhomme & Martin Weidner, 2020. "Minimizing Sensitivity to Model Misspecification," CeMMAP working papers CWP37/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Jarociński, Marek & Karadi, Peter, 2026. "Monetary policy surprises with imperfect information," Working Paper Series 3292, European Central Bank.
    17. Matthew Read, 2021. "Algorithms for Inference in SVARs Identified with Sign and Zero Restrictions," Papers 2109.10676, arXiv.org, revised Jan 2022.
    18. Jonas E. Arias & Juan F. Rubio-Ramirez & Daniel F. Waggoner, 2020. "Uniform Priors for Impulse Responses," Working Papers 22-30, Federal Reserve Bank of Philadelphia.
    19. Ferreira, Leonardo N., 2022. "Forward guidance matters: Disentangling monetary policy shocks," Journal of Macroeconomics, Elsevier, vol. 73(C).
    20. Mikkel Plagborg-Møller & Christian K. Wolf, 2020. "Local Projections and VARs Estimate the Same Impulse Responses," Working Papers 2020-16, Princeton University. Economics Department..
    21. Inoue, Atsushi & Kilian, Lutz, 2020. "Joint Bayesian inference about impulse responses in VAR models," CFS Working Paper Series 650, Center for Financial Studies (CFS).
    22. Kitagawa, Toru & Montiel Olea, José Luis & Payne, Jonathan & Velez, Amilcar, 2020. "Posterior distribution of nondifferentiable functions," Journal of Econometrics, Elsevier, vol. 217(1), pages 161-175.
    23. Jonas E. Arias & Juan F. Rubio-Ram'irez & Daniel Rudolf & Minchul Shin, 2025. "Large SVARs," Papers 2505.23542, arXiv.org, revised Apr 2026.
    24. Jarociński, Marek, 2021. "Estimating the Fed’s Unconventional Policy Shocks," Working Paper Series 20210, European Central Bank.
    25. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," Papers 2011.03153, arXiv.org, revised Dec 2020.
    26. Bianco, Timothy & Herrera, Ana María, 2025. "Monetary policy and credit flows: A tale of two effective lower bounds," Journal of Economic Dynamics and Control, Elsevier, vol. 175(C).
    27. Matthew Read, 2022. "Estimating the Effects of Monetary Policy in Australia Using Sign-restricted Structural Vector Autoregressions," RBA Research Discussion Papers rdp2022-09, Reserve Bank of Australia.
    28. Zhai, Weiyang, 2025. "Gasoline price pass-through into CPI inflation: Evidence from a structural VAR," International Economics, Elsevier, vol. 184(C).
    29. Andrea Carriero & Alessio Volpicella, 2022. "Generalizing the Max Share Identification to multiple shocks identification: an Application to Uncertainty," School of Economics Discussion Papers 0322, School of Economics, University of Surrey.
    30. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2020. "Uncertain Identification," CeMMAP working papers CWP33/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    31. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    32. Hyeon-seung Huh & David Kim, 2026. "Exact identification, robust inference, and shock masquerading in sign-restricted SVARs," Working papers 2026rwp-293, Yonsei University, Yonsei Economics Research Institute.
    33. Fusari, Francesco, 2025. "Identifying monetary policy shocks through external constraints," Journal of Macroeconomics, Elsevier, vol. 85(C).
    34. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.
    35. Emanuele Bacchiocchi & Toru Kitagawa, 2021. "A note on global identi?cation in structural vector autoregressions," CeMMAP working papers CWP03/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    36. Xiwen Bai & Jesús Fernández-Villaverde & Yiliang Li & Francesco Zanetti, 2024. "The Causal Effects of Global Supply Chain Disruptions on Macroeconomic Outcomes: Evidence and Theory," CESifo Working Paper Series 10930, CESifo.
    37. Matthew Read, 2022. "The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions," RBA Research Discussion Papers rdp2022-04, Reserve Bank of Australia.
    38. Jean-Pierre Florens & Anna Simoni, 2021. "Revisiting Identification Concepts in Bayesian Analysis," Post-Print hal-03504692, HAL.
    39. Paul Ho, 2020. "Global Robust Bayesian Analysis in Large Models," Working Paper 20-07, Federal Reserve Bank of Richmond.
    40. Raffaella Giacomini & Toru Kitagawa & Harald Uhlig, 2019. "Estimation Under Ambiguity," CeMMAP working papers CWP24/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    41. Paul Levine & Joseph Pearlman & Alessio Volpicella & Bo Yang, 2022. "The Use and Mis-Use of SVARs for Validating DSGE Models," School of Economics Discussion Papers 0522, School of Economics, University of Surrey.
    42. Camehl, Annika & Rieth, Malte, 2023. "Disentangling COVID-19, Economic Mobility, and Containment Policy Shocks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(4), pages 217-248.
    43. Diegel, Max & Nautz, Dieter, 2021. "Long-term inflation expectations and the transmission of monetary policy shocks: Evidence from a SVAR analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
    44. Martin Geiger & Jochen Güntner, 2022. "The Chronology of Brexit and UK Monetary Policy," Economics working papers 2022-06, Department of Economics, Johannes Kepler University Linz, Austria.
    45. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    46. Emanuele Bacchiocchi & Toru Kitagawa, 2020. "Locally- but not globally-identified SVARs," CeMMAP working papers CWP40/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    47. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    48. Hiroaki Kaido & Yi Zhang, 2023. "Applications of Choquet expected utility to hypothesis testing with incompleteness," The Japanese Economic Review, Springer, vol. 74(4), pages 551-572, October.
    49. Jonas E. Arias & Jesús Fernández-Villaverde & Juan Rubio Ramírez & Minchul Shin, 2021. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," NBER Working Papers 28617, National Bureau of Economic Research, Inc.
    50. Patel, Nikhil & Peralta-Alva, Adrian, 2025. "High public debts: Are shocks or discretionary fiscal policy to blame?," Journal of International Economics, Elsevier, vol. 158(C).
    51. Eric Vansteenberghe, 2026. "Seventy Years of Identifying the Phillips Curve and the Policy Rule," Papers 2610.06007, arXiv.org.
    52. Fusari, Francesco & Marlow, Joe & Volpicella, Alessio, 2026. "Estimation and inference of the forecast error variance decomposition for set-identified SVARs," Journal of Econometrics, Elsevier, vol. 255(C).
    53. Elie Tamer & Christopher D. Walker, 2026. "Nonparametric Bayesian Inference for Partially Identified Discrete Response Models," Papers 2608.25814, arXiv.org.
    54. Drautzburg, Thorsten & Wright, Jonathan H., 2023. "Refining set-identification in VARs through independence," Journal of Econometrics, Elsevier, vol. 235(2), pages 1827-1847.
    55. Fisher, Lance A. & Huh, Hyeon-seung, 2023. "Systematic monetary policy in a SVAR for Australia," Economic Modelling, Elsevier, vol. 128(C).
    56. Jonas E. Arias & Juan F. Rubio-Ramirez & Minchul Shin, 2025. "A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs," Working Papers 25-19, Federal Reserve Bank of Philadelphia.
    57. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2022. "Double Robust Bayesian Inference on Average Treatment Effects," Papers 2211.16298, arXiv.org, revised Feb 2025.
    58. Nguyen, Lam, 2025. "Bayesian inference in proxy SVARs with incomplete identification: Re-evaluating the validity of monetary policy instruments," Journal of Monetary Economics, Elsevier, vol. 155(C).
    59. Alvarez, Luis Antonio, 2023. "Approximate Bayesian Computation for Partially Identified Models," MPRA Paper 117339, University Library of Munich, Germany.

  7. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2017. "Uncertain identification," CeMMAP working papers CWP18/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Wu, Ping & Koop, Gary, 2023. "Estimating the ordering of variables in a VAR using a Plackett–Luce prior," Economics Letters, Elsevier, vol. 230(C).
    2. Nguyen, Lam, 2025. "Bayesian inference in proxy SVARs with incomplete identification: Re-evaluating the validity of monetary policy instruments," Journal of Monetary Economics, Elsevier, vol. 155(C).

  8. Rhys M. Bidder & Raffaella Giacomini & Andrew McKenna, 2016. "Stress Testing with Misspecified Models," Working Paper Series 2016-26, Federal Reserve Bank of San Francisco.

    Cited by:

    1. Michael W. McCracken & Joseph McGillicuddy, 2017. "An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts," Working Papers 2017-40, Federal Reserve Bank of St. Louis.
    2. Bidder, R.M. & Smith, M.E., 2018. "Doubts and variability: A robust perspective on exotic consumption series," Journal of Economic Theory, Elsevier, vol. 175(C), pages 689-712.
    3. Colesanti Senni, Chiara & Goel, Skand, 2025. "Nature scenario plausibility: A dynamic Bayesian network approach," Ecological Economics, Elsevier, vol. 236(C).
    4. Paul Ho, 2019. "Global Robust Bayesian Analysis in Large Models," 2019 Meeting Papers 390, Society for Economic Dynamics.
    5. Jose Fique, 2017. "The MacroFinancial Risk Assessment Framework (MFRAF), Version 2.0," Technical Reports 111, Bank of Canada.
    6. Kupiec, Paul H., 2018. "On the accuracy of alternative approaches for calibrating bank stress test models," Journal of Financial Stability, Elsevier, vol. 38(C), pages 132-146.

  9. Raffaella Giacomini & Vasiliki Skreta & Javier Turen, 2015. "Models, Inattention and Expectation Updates," Discussion Papers 1602, Centre for Macroeconomics (CFM).

    Cited by:

    1. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, University of Reading.
    2. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    3. Ran Spiegler, 2016. "Can Agents with Causal Misperceptions be Systemically Fooled?," Discussion Papers 1619, Centre for Macroeconomics (CFM).

  10. Raffaella Giacomini & Toru Kitagawa, 2014. "Inference about Non-Identi?ed SVARs," CeMMAP working papers CWP45/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Thorsten Klug & Eric Mayer & Tobias Schuler, 2018. "The Corporate Saving Glut and the Current Account in Germany," ifo Working Paper Series 280, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    2. Thorsten Drautzburg & Pooyan Amir-Ahmadi, 2017. "Identification through Heterogeneity," 2017 Meeting Papers 1087, Society for Economic Dynamics.
    3. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.

  11. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers CWP41/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Ca’ Zorzi, Michele & Kolasa, Marcin & Rubaszek, Michał, 2017. "Exchange rate forecasting with DSGE models," Journal of International Economics, Elsevier, vol. 107(C), pages 127-146.
    2. Fritz Breuss, 2016. "Would DSGE Models have Predicted the Great Recession in Austria?," WIFO Working Papers 530, WIFO.
    3. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
    4. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    5. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    6. EMERSON Abraham Jackson, 2018. "Comparison Between Static And Dynamic Forecast In Autoregressive Integrated Moving Average For Seasonally Adjusted Headline Consumer Price Index," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 70(1), pages 53-65, August.
    7. Hasanov, Fakhri J. & Javid, Muhammad & Mikayilov, Jeyhun I. & Shabaneh, Rami & Darandary, Abdulelah & Alyamani, Ryan, 2025. "Macroeconomic and sectoral effects of natural gas price: Policy insights from a macroeconometric model," Energy Economics, Elsevier, vol. 143(C).
    8. Choy Keen Meng, 2016. "The inflation process and expectations in Singapore," BIS Papers chapters, in: Bank for International Settlements (ed.), Inflation mechanisms, expectations and monetary policy, volume 89, pages 335-343, Bank for International Settlements.
    9. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    10. Job Nmadu & Ezekiel Yisa & Usman Mohammed & Halima Sallawu & Yebosoko Nmadu & Sokoyami Nmadu, 2022. "Structural Analysis and Forecast of Nigerian Monthly Inflation Movement between 1996 and 2022," RAIS Conference Proceedings 2022-2026 0211, Research Association for Interdisciplinary Studies.
    11. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    12. Fritz Breuss, 2018. "Would DSGE Models Have Predicted the Great Recession in Austria?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 105-126, April.
    13. Hanif, Muhammad Nadim & Malik, Muhammad Jahanzeb, 2015. "Evaluating Performance of Inflation Forecasting Models of Pakistan," MPRA Paper 66843, University Library of Munich, Germany.
    14. Ginanneschi, Marco, 2021. "Long-term strategic thinking, the Themis method and the future of food," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    15. Daniel H. Cooper & Giovanni P. Olivei & Hannah Rhodenhiser, 2025. "Forecasting U.S. Economic Activity with a Small Information Set," Working Papers 25-4, Federal Reserve Bank of Boston.
    16. Lillian R. Gaeto & Sandeep Mazumder, 2019. "Measuring the Accuracy of Federal Reserve Forecasts," Southern Economic Journal, John Wiley & Sons, vol. 85(3), pages 960-984, January.
    17. Luca Fanelli & Marco M. Sorge, 2015. "Indeterminacy, Misspecification and Forecastability: Good Luck in Bad Policy?," CSEF Working Papers 402, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    18. Ozana Nadoveza Jelić & Rafael Ravnik, 2021. "Introducing Policy Analysis Croatian MAcroecoNometric Model (PACMAN)," Surveys 41, The Croatian National Bank, Croatia.
    19. Elshurafa, Amro M. & Alatawi, Hatem & Hasanov, Fakhri J. & Algahtani, Goblan J. & Felder, Frank A., 2022. "Cost, emission, and macroeconomic implications of diesel displacement in the Saudi agricultural sector: Options and policy insights," Energy Policy, Elsevier, vol. 168(C).
    20. Lake, A., 2020. "Optimal Feasible Expectations in Economics and Finance," Cambridge Working Papers in Economics 20105, Faculty of Economics, University of Cambridge.
    21. Fakhri J. Hasanov & Noha Razek, 2023. "Oil and Non-Oil Determinants of Saudi Arabia’s International Competitiveness: Historical Analysis and Policy Simulations," Sustainability, MDPI, vol. 15(11), pages 1-39, June.
    22. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    23. Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.
    24. Tae-Hwy Lee & He Wang & Zhou Xi & Ru Zhang, 2021. "Density Forecast of Financial Returns Using Decomposition and Maximum Entropy," Working Papers 202115, University of California at Riverside, Department of Economics.
    25. Ran Spiegler, 2021. "A Simple Model of Monetary Policy under Phillips-Curve Causal Disagreements," Papers 2105.08988, arXiv.org.
    26. Fanelli, Luca & Sorge, Marco M., 2017. "Indeterminate forecast accuracy under indeterminacy," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 57-70.

  12. Raffaella Giacomini & Barbara Rossi, 2014. "Forecasting in nonstationary environments: What works and what doesn't in reduced-form and structural models," Economics Working Papers 1476, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2019. "Forecasting with instabilities: an application to DSGE models with financial frictions," Temi di discussione (Economic working papers) 1234, Bank of Italy, Economic Research and International Relations Area.
    2. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org, revised Aug 2024.
    3. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    4. Jan Čapek & Jesús Crespo Cuaresma & Jakub Chalmovianský & Vlastimil Reichel, 2025. "Real‐Time Data, Revisions and the Predictive Ability of DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 87(6), pages 1059-1080, December.
    5. Elliott, Graham & Timmermann, Allan, 2016. "Forecasting in Economics and Finance," CEPR Discussion Papers 11354, Centre for Economic Policy Research.

  13. Carlo Altavilla & Riccardo Costantini & Raffaella Giacomini, 2013. "Bond returns and market expectations," CeMMAP working papers CWP20/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Davide Pettenuzzo & Antonio Gargano & Allan Timmermann, 2014. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Working Papers 75R, Brandeis School of Business and Economics, revised Jul 2016.
    2. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    3. Christiane Baumeister, 2021. "Measuring Market Expectations," Working Papers 202163, University of Pretoria, Department of Economics.
    4. Vieira, Fausto & Fernandes, Marcelo & Chague, Fernando, 2017. "Forecasting the Brazilian yield curve using forward-looking variables," International Journal of Forecasting, Elsevier, vol. 33(1), pages 121-131.
    5. Davide Pettenuzzo & Konstantinos Metaxoglou & Aaron Smith, 2016. "Option-Implied Equity Premium Predictions via Entropic TiltinG," Working Papers 99, Brandeis School of Business and Economics.
    6. Cordeiro, Werley & Caldeira, João F. & Moura, Guilherme V., 2025. "Forecasting the Brazilian yield curve using macroeconomics expectations and time-varying volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 104(C).
    7. Jonas Nygaard Eriksen, 2015. "Expected Business Conditions and Bond Risk Premia," CREATES Research Papers 2015-44, Department of Economics and Business Economics, Aarhus University.
    8. Cristiano Salvagnin & Aldo Glielmo & Maria Elena De Giuli & Antonietta Mira, 2024. "Investigating the price determinants of the European Emission Trading System: a non-parametric approach," Papers 2406.05094, arXiv.org.
    9. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    10. Fausto Vieira & Fernando Chague, Marcelo Fernandes, 2016. "A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US," Working Papers, Department of Economics 2016_31, University of São Paulo (FEA-USP).
    11. Maryam Movahedifar & Hossein Hassani & Masoud Yarmohammadi & Mahdi Kalantari & Rangan Gupta, 2021. "A robust approach for outlier imputation: Singular Spectrum Decomposition," Working Papers 202164, University of Pretoria, Department of Economics.
    12. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

  14. Ron Gallant & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Generalized method of moments with latent variables," CeMMAP working papers CWP50/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Drew Creal & Siem Jan Koopman & André Lucas & Marcin Zamojski, 2015. "Generalized Autoregressive Method of Moments," Tinbergen Institute Discussion Papers 15-138/III, Tinbergen Institute, revised 06 Jul 2018.
    2. Jesus Fernandez-Villaverde & Juan Rubio-Ramírez & Frank Schorfheide, 2015. "Solution and Estimation Methods for DSGE Models," PIER Working Paper Archive 15-042, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2015.
    3. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.

  15. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Anchoring the yield curve using survey expectations," CeMMAP working papers CWP52/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Montes, Gabriel Caldas & Maia, João Pedro Neves, 2023. "Who speaks louder, financial instruments or credit rating agencies? Analyzing the effects of different sovereign risk measures on interest rates in Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    2. Philip Hans Franses, 2021. "Modeling Judgment in Macroeconomic Forecasts," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 401-417, December.
    3. Gary Koop & Stuart McIntyre & James Mitchell, 2018. "UK Regional Nowcasting using a Mixed Frequency Vector Autoregressive Model," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers (2017-2023 papers hosted by The National Institute of Economic and Social Research) ESCoE DP-2018-07, Economic Statistics Centre of Excellence (ESCoE).
    4. Alberto Caruso & Laura Coroneo, 2023. "Does Real‐Time Macroeconomic Information Help to Predict Interest Rates?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2027-2059, December.
    5. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    6. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    7. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.
    8. Atsushi Inoue & Barbara Rossi, 2019. "A New Approach to Measuring Economic Policy Shocks, with an Application to Conventional and Unconventional Monetary Policy," Working Papers 1082, Barcelona School of Economics.
    9. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
    10. Altug, Sumru & Çakmaklı, Cem, 2015. "Forecasting Inflation using Survey Expectations and Target Inflation: Evidence for Brazil and Turkey," CEPR Discussion Papers 10419, Centre for Economic Policy Research.
    11. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," MPRA Paper 63844, University Library of Munich, Germany.
    12. Marco Giacoletti & Kristoffer T. Laursen & Kenneth J. Singleton, 2021. "Learning From Disagreement in the U.S. Treasury Bond Market," Journal of Finance, American Finance Association, vol. 76(1), pages 395-441, February.
    13. Vieira, Fausto & Fernandes, Marcelo & Chague, Fernando, 2017. "Forecasting the Brazilian yield curve using forward-looking variables," International Journal of Forecasting, Elsevier, vol. 33(1), pages 121-131.
    14. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    15. Evangelos Salachas & Georgios P. Kouretas & Nikiforos T. Laopodis, 2024. "The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 1018-1041, July.
    16. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    17. Alberto Caruso & Laura Coroneo, 2019. "Predicting interest rates in real-time," Discussion Papers 19/18, Department of Economics, University of York.
    18. Cifuentes, Sebastián & Cortazar, Gonzalo & Ortega, Hector & Schwartz, Eduardo S., 2020. "Expected prices, futures prices and time-varying risk premiums: The case of copper," Resources Policy, Elsevier, vol. 69(C).
    19. Krüger, Fabian & Clark, Todd E. & Ravazzolo, Francesco, 2015. "Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113077, Verein für Socialpolitik / German Economic Association.
    20. Cordeiro, Werley & Caldeira, João F. & Moura, Guilherme V., 2025. "Forecasting the Brazilian yield curve using macroeconomics expectations and time-varying volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 104(C).
    21. Raviv, Eran, 2015. "Prediction bias correction for dynamic term structure models," Economics Letters, Elsevier, vol. 129(C), pages 112-115.
    22. Chen, Jiazi & Hong, Zhiwu & Niu, Linlin, 2025. "Forecasting interest rates with shifting endpoints: The role of the functional demographic age distribution," International Journal of Forecasting, Elsevier, vol. 41(1), pages 153-174.
    23. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    24. Rodrigo Guimarães, 2014. "Expectations, risk premia and information spanning in dynamic term structure model estimation," Bank of England Staff Working Paper series 489, Bank of England.
    25. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    26. Kang, Kyu Ho, 2015. "The predictive density simulation of the yield curve with a zero lower bound," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 51-66.
    27. Clements, Michael P., 2016. "Long-run restrictions and survey forecasts of output, consumption and investment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 614-628.
    28. Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021. "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series 2543, European Central Bank.
    29. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    30. Zhiyuan Pan & Jun Zhang & Yudong Wang & Juan Huang, 2024. "Modeling and forecasting stock return volatility using the HARGARCH model with VIX information," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(8), pages 1383-1403, August.
    31. Vieira, Fausto José Araújo & Chague, Fernando Daniel & Fernandes, Marcelo, 2017. "A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US," Textos para discussão 445, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    32. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    33. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.
    34. Cem Cakmakli & Hamza Demircan, 2020. "Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic," Koç University-TUSIAD Economic Research Forum Working Papers 2016, Koc University-TUSIAD Economic Research Forum.
    35. Cortazar, Gonzalo & Ortega, Hector & Rojas, Maximiliano & Schwartz, Eduardo S., 2021. "Commodity index risk premium," Journal of Commodity Markets, Elsevier, vol. 22(C).
    36. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    37. Shin, Minchul & Zhong, Molin, 2017. "Does realized volatility help bond yield density prediction?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 373-389.

  16. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Jan Babecky & Michal Franta & Jakub Rysanek, 2016. "Effects of Fiscal Policy in the DSGE-VAR Framework: The Case of the Czech Republic," Working Papers 2016/09, Czech National Bank, Research and Statistics Department.
    2. Babecký, Jan & Franta, Michal & Ryšánek, Jakub, 2018. "Fiscal policy within the DSGE-VAR framework," Economic Modelling, Elsevier, vol. 75(C), pages 23-37.
    3. Adrian Pagan & Tim Robinson, 2019. "Implications of Partial Information for Applied Macroeconomic Modelling," Melbourne Institute Working Paper Series wp2019n12, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    4. Stephen Morris, 2014. "The Statistical Implications of Common Identifying Restrictions for DSGE Models," 2014 Meeting Papers 738, Society for Economic Dynamics.
    5. Minford, Lucy & Meenagh, David, 2019. "Testing a model of UK growth: A role for R&D subsidies," Economic Modelling, Elsevier, vol. 82(C), pages 152-167.
    6. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    7. Minford, Lucy & Meenagh, David, 2018. "Testing a model of UK growth - a causal role for R&D subsidies," Cardiff Economics Working Papers E2018/3, Cardiff University, Cardiff Business School, Economics Section.
    8. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    9. Adrian Pagan & Tim Robinson, 2016. "Investigating the Relationship Between DSGE and SVAR Models," NCER Working Paper Series 112, National Centre for Econometric Research.
    10. Efrem Castelnuovo, 2016. "Modest Macroeconomic Effects of Monetary Policy Shocks during the Great Moderation: An Alternative Interpretation," Melbourne Institute Working Paper Series wp2016n30, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    12. Bernd Funovits & Alexander Braumann, 2021. "Identifiability of structural singular vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 431-441, July.
    13. Tomasz Wozniak, 2016. "Rare Events and Risk Perception: Evidence from Fukushima Accident," Department of Economics - Working Papers Series 2021, The University of Melbourne.
    14. Nikolay Iskrev, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Papers REM 2018/33, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    15. Michael W. McCracken & Joseph McGillicuddy, 2017. "An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts," Working Papers 2017-40, Federal Reserve Bank of St. Louis.
    16. Martinoli, Mario & Moneta, Alessio & Pallante, Gianluca, 2024. "Calibration and validation of macroeconomic simulation models by statistical causal search," Journal of Economic Behavior & Organization, Elsevier, vol. 228(C).
    17. Maddalena Cavicchioli, 2020. "Invertibility and VAR Representations of Time-Varying Dynamic Stochastic General Equilibrium Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 61-86, January.
    18. Canova, Fabio & Hamidi Sahneh, Mehdi, 2016. "Are small scale VARs useful for business cycle analysis? Revisiting Non-Fundamentalness," CEPR Discussion Papers 11041, Centre for Economic Policy Research.
    19. Meenagh, David & Minford, Patrick & Yang, Xiaoliang, 2018. "A heterogeneous-agent model of growth and inequality for the UK," Cardiff Economics Working Papers E2018/17, Cardiff University, Cardiff Business School, Economics Section.
    20. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    21. Anna Watson, 2019. "Financial Frictions, the Great Trade Collapse and International Trade over the Business Cycle," Open Economies Review, Springer, vol. 30(1), pages 19-64, February.
    22. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.
    23. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    24. Luca Sala & Luca Gambetti & Mario Forni, 2016. "VAR Information and the Empirical Validation of DSGE Models," 2016 Meeting Papers 260, Society for Economic Dynamics.
    25. Thomas Glinnan, 2026. "Design-Based Inference for Time-Series GMM," Papers 2606.31685, arXiv.org.
    26. Härtl, Tilmann, 2022. "Identifying Proxy VARs with Restrictions on the Forecast Error Variance," VfS Annual Conference 2022 (Basel): Big Data in Economics 264071, Verein für Socialpolitik / German Economic Association.
    27. Klodiana Istrefi & B. Vonnak, 2015. "Delayed Overshooting Puzzle in Structural Vector Autoregression Models," Working papers 576, Banque de France.
    28. Mario Forni & Luca Gambetti & Luca Sala, 2018. "Fundamentalness, Granger Causality and Aggregation," Center for Economic Research (RECent) 139, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    29. Fabio Canova & Filippo Ferroni, 2018. "Mind the gap! Stylized dynamic facts and structural models," Working Papers No 13/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    30. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    31. Artur Sharafutdinov, 2023. "Forecasting Russian GDP, Inflation, Interest Rate, and Exchange Rate Using DSGE-VAR Model," Russian Journal of Money and Finance, Bank of Russia, vol. 82(3), pages 62-86, September.
    32. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2023. "Imperfect Information and Hidden Dynamics," School of Economics Discussion Papers 1223, School of Economics, University of Surrey.
    33. Claire A. Reicher, 2016. "A Note on the Identification of Dynamic Economic Models with Generalized Shock Processes," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(3), pages 412-423, June.
    34. Pop, Raluca-Elena, 2017. "A small-scale DSGE-VAR model for the Romanian economy," Economic Modelling, Elsevier, vol. 67(C), pages 1-9.
    35. Zviadadze, Irina, 2018. "Term Structure of Risk in Expected Returns," CEPR Discussion Papers 13414, Centre for Economic Policy Research.
    36. Greenwood-Nimmo, Matthew & Nguyen, Viet Hoang & Shin, Yongcheol, 2021. "Measuring the Connectedness of the Global Economy," International Journal of Forecasting, Elsevier, vol. 37(2), pages 899-919.
    37. Mariana García-Schmidt & Javier Garcia-Cicco, 2018. "Revisiting the Exchange Rate Pass Through: A General Equilibrium Perspective," Working Papers Central Bank of Chile 826, Central Bank of Chile.
    38. Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, September.
    39. Chunyeung Kwok, 2022. "Estimating Structural Shocks with the GVAR-DSGE Model: Pre- and Post-Pandemic," Mathematics, MDPI, vol. 10(10), pages 1-32, May.
    40. Sergio Peláez, 2018. "Ciclo de recursos naturales y política fiscal bajo preferencias inconsistentes," Coyuntura Económica, Fedesarrollo, vol. 48(1-2), pages 13-78.

  17. Raffaella Giacomini & Dimitris N. Politis & Halbert White, 2012. "A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators," CeMMAP working papers CWP11/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. E. Bothma & J. S. Allison & I. J. H. Visagie, 2022. "New classes of tests for the Weibull distribution using Stein’s method in the presence of random right censoring," Computational Statistics, Springer, vol. 37(4), pages 1751-1770, September.
    2. Seojeong Lee, 2014. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators," Discussion Papers 2014-02, School of Economics, The University of New South Wales.
    3. Lamy, Laurent & Patnam, Manasa & Visser, Michael, 2023. "Distinguishing incentive from selection effects in auction-determined contracts," Journal of Econometrics, Elsevier, vol. 235(2), pages 1172-1202.
    4. Royer, Julien, 2023. "Conditional asymmetry in Power ARCH(∞) models," Journal of Econometrics, Elsevier, vol. 234(1), pages 178-204.
    5. M. Dolores Jiménez-Gamero & Marina Valdora & Daniela Rodríguez, 2025. "Testing homoscedasticity of a large number of populations," Statistical Papers, Springer, vol. 66(2), pages 1-32, February.
    6. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2021. "Testing serial independence with functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 603-629, September.
    7. Campbell R. Harvey & Yan Liu, 2022. "Luck versus Skill in the Cross Section of Mutual Fund Returns: Reexamining the Evidence," Journal of Finance, American Finance Association, vol. 77(3), pages 1921-1966, June.
    8. Hongyi Jiang & Zhenting Sun & Shiyun Hu, 2023. "A Nonparametric Test of $m$th-degree Inverse Stochastic Dominance," Papers 2306.12271, arXiv.org, revised Jul 2023.
    9. Negera Wakgari Deresa & Ingrid Van Keilegom, 2025. "Semiparametric transformation models for survival data with dependent censoring," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(3), pages 425-457, June.
    10. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    11. Francq, Christian & Jiménez Gamero, Maria Dolores & Meintanis, Simos, 2015. "Tests for sphericity in multivariate garch models," MPRA Paper 67411, University Library of Munich, Germany.
    12. Perera, Indeewara & Silvapulle, Mervyn J., 2021. "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, vol. 221(1), pages 1-24.
    13. Sophocles Mavroeidis, 2021. "Identification at the Zero Lower Bound," Econometrica, Econometric Society, vol. 89(6), pages 2855-2885, November.
    14. Karsten Schweikert, 2021. "Bootstrap Confidence Intervals and Hypothesis Testing for Market Information Shares [Price Discovery and Common Factor Models]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 934-959.
    15. Hušková, Marie & Meintanis, Simos G. & Pretorius, Charl, 2020. "Tests for validity of the semiparametric heteroskedastic transformation model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    16. L. Ndwandwe & J. S. Allison & L. Santana & I. J. H. Visagie, 2023. "Testing for the Pareto type I distribution: a comparative study," METRON, Springer;Sapienza Università di Roma, vol. 81(2), pages 215-256, August.
    17. Igor Kheifets & Carlos Velasco, 2013. "New Goodness-of-fit Diagnostics for Conditional Discrete Response Models," Cowles Foundation Discussion Papers 1924, Cowles Foundation for Research in Economics, Yale University.
    18. Haiqi Li Author-Name-First: Haiqi & Jing Zhang & Chaowen Zheng, 2023. "Estimating and Testing for Functional Coefficient Quantile Cointegrating Regression," Economics Discussion Papers em-dp2023-07, Department of Economics, University of Reading.
    19. Simos G. Meintanis & Joseph Ngatchou-Wandji & Šárka Hudecová, 2025. "Omnibus diagnostic procedures for vector multiplicative errors models," Statistical Papers, Springer, vol. 66(2), pages 1-44, February.
    20. Gilles Crommen & Jean-Pierre Florens & Ingrid Van Keilegom, 2025. "Tests of exogeneity in duration models with censored data," Papers 2510.26613, arXiv.org, revised Dec 2025.
    21. Fresoli, Diego E. & Ruiz, Esther, 2016. "The uncertainty of conditional returns, volatilities and correlations in DCC models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 170-185.
    22. Audrey Sallenave & Jean-Pierre Allegret & Tolga Omay, 2024. "Can governments sleep more soundly when holding international reserves? A banking and financial vulnerabilities perspective," Post-Print hal-03945433, HAL.
    23. Jiménez-Gamero, M.D. & Alba-Fernández, M.V., 2021. "A test for the geometric distribution based on linear regression of order statistics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 186(C), pages 103-123.
    24. Francq, C. & Jiménez-Gamero, M.D. & Meintanis, S.G., 2017. "Tests for conditional ellipticity in multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 196(2), pages 305-319.
    25. Kheifets, Igor L., 2018. "Multivariate specification tests based on a dynamic Rosenblatt transform," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 1-14.
    26. Dukpa Kim & Tatsushi Oka & Francisco Estrada & Pierre Perron, 2017. "Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures," Boston University - Department of Economics - Working Papers Series WP2018-015, Boston University - Department of Economics, revised Apr 2018.
    27. Patrick Richard, 2014. "Bootstrap tests in linear models with many regressors," Cahiers de recherche 14-06, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    28. Corradi, Valentina & Llorens-Terrazas, Jordi, 2026. "Monitoring joint tail risks: An application to growth and inflation," Journal of Econometrics, Elsevier, vol. 255(C).
    29. Hongyi Jiang & Zhenting Sun, 2023. "Testing Partial Instrument Monotonicity," Papers 2308.08390, arXiv.org, revised Aug 2023.
    30. Perera, Indeewara & Silvapulle, Mervyn J., 2023. "Bootstrap specification tests for dynamic conditional distribution models," Journal of Econometrics, Elsevier, vol. 235(2), pages 949-971.
    31. Cazals, Catherine & Florens, Jean-Pierre & Simar, Léopold, 2025. "Single Index Models for Nonparametric Conditional Frontiers," LIDAM Discussion Papers ISBA 2025022, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    32. Grigory Franguridi & Lidia Kosenkova, 2024. "Closed-form estimation and inference for panels with attrition and refreshment samples," Papers 2410.11263, arXiv.org, revised May 2026.
    33. Lorenzo Tedesco & Jad Beyhum & Ingrid Van Keilegom, 2023. "Instrumental variable estimation of the proportional hazards model by presmoothing," Papers 2309.02183, arXiv.org.
    34. Laurent Lamy & Manasa Patnam & Michael Visser, 2023. "Distinguishing incentive from selection effects in auction-determined contracts," Post-Print hal-04382099, HAL.
    35. Lee, Sangyeol & Meintanis, Simos G. & Pretorius, Charl, 2022. "Monitoring procedures for strict stationarity based on the multivariate characteristic function," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    36. Komarova, Tatiana & Hidalgo, Javier, 2023. "Testing nonparametric shape restrictions," LSE Research Online Documents on Economics 121410, London School of Economics and Political Science, LSE Library.
    37. Žikica Lukić & Bojana Milošević, 2024. "A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(5), pages 797-820, October.
    38. Li, Haiqi & Zhou, Jin & Hong, Yongmiao, 2024. "Estimating and testing for smooth structural changes in moment condition models," Journal of Econometrics, Elsevier, vol. 246(1).
    39. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2022. "Moment tests of independent components," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 429-474, May.
    40. Karsten Schweikert, 2019. "Asymmetric price transmission in the US and German fuel markets: a quantile autoregression approach," Empirical Economics, Springer, vol. 56(3), pages 1071-1095, March.
    41. Simos G. Meintanis & Joseph Ngatchou-Wandji & James Allison, 2018. "Testing for serial independence in vector autoregressive models," Statistical Papers, Springer, vol. 59(4), pages 1379-1410, December.
    42. Šárka Hudecová & Marie Hušková & Simos G. Meintanis, 2021. "Goodness–of–Fit Tests for Bivariate Time Series of Counts," Econometrics, MDPI, vol. 9(1), pages 1-20, March.
    43. Hoderlein, Stefan & Su, Liangjun & White, Halbert & Yang, Thomas Tao, 2016. "Testing for monotonicity in unobservables under unconfoundedness," Journal of Econometrics, Elsevier, vol. 193(1), pages 183-202.
    44. Fiorentini, Gabriele & Sentana, Enrique, 2021. "New testing approaches for mean–variance predictability," Journal of Econometrics, Elsevier, vol. 222(1), pages 516-538.
    45. Sun, Yixiao & Kim, Min Seong, 2009. "k-step Bootstrap Bias Correction for Fixed Effects Estimators in Nonlinear Panel Models," University of California at San Diego, Economics Working Paper Series qt9gn6n5mr, Department of Economics, UC San Diego.
    46. Donghang Luo & Ke Zhu & Huan Gong & Dong Li, 2020. "Testing error distribution by kernelized Stein discrepancy in multivariate time series models," Papers 2008.00747, arXiv.org.
    47. Norbert Henze & María Dolores Jiménez-Gamero, 2019. "A new class of tests for multinormality with i.i.d. and garch data based on the empirical moment generating function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 499-521, June.
    48. Jiménez-Gamero, M.D. & Alba-Fernández, M.V., 2019. "Testing for the Poisson–Tweedie distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 164(C), pages 146-162.
    49. Takashi Matsuki, 2016. "Linear and nonlinear comovement in Southeast Asian local currency bond markets: a stepwise multiple testing approach," Empirical Economics, Springer, vol. 51(2), pages 591-619, September.
    50. Simos G. Meintanis & John P. Nolan & Charl Pretorius, 2024. "Specification procedures for multivariate stable-Paretian laws for independent and for conditionally heteroskedastic data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(2), pages 517-539, June.
    51. Seojeong Lee, 2018. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Empirical Likelihood Estimators," Papers 1806.00953, arXiv.org, revised Jun 2018.
    52. Giuseppe Cavaliere & Indeewara Perera & Anders Rahbek, 2021. "Specification tests for GARCH processes," Papers 2105.14081, arXiv.org.
    53. Annastiina Silvennoinen & Timo Terasvirta, 2015. "Testing constancy of unconditional variance in volatility models by misspecification and specification tests," NCER Working Paper Series 108, National Centre for Econometric Research.
    54. Marcus J. Chambers, 2015. "A Jackknife Correction to a Test for Cointegration Rank," Econometrics, MDPI, vol. 3(2), pages 1-21, May.
    55. M. Cockeran & S. G. Meintanis & L. Santana & J. S. Allison, 2021. "Goodness-of-fit testing of survival models in the presence of Type–II right censoring," Computational Statistics, Springer, vol. 36(2), pages 977-1010, June.
    56. Lorenzo Tedesco & Ingrid Van Keilegom, 2023. "Comparison of quantile regression curves with censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 829-864, September.
    57. Igor Kheifets, 2014. "Specification Tests for Nonlinear Dynamic Models," Cowles Foundation Discussion Papers 1937, Cowles Foundation for Research in Economics, Yale University, revised Oct 2014.
    58. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    59. Tatiana Komarova & Javier Hidalgo, 2019. "Testing nonparametric shape restrictions," Papers 1909.01675, arXiv.org, revised Jun 2020.
    60. Lapenta, Elia & Lavergne, Pascal, 2022. "Encompassing Tests for Nonparametric Regressions," TSE Working Papers 22-1332, Toulouse School of Economics (TSE).
    61. Moneta, Alessio & Pallante, Gianluca, 2022. "Identification of Structural VAR Models via Independent Component Analysis: A Performance Evaluation Study," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    62. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    63. Clara Augustin & Daniel Gutknecht & Cenchen Liu, 2025. "Staggered Adoption DiD Designs with Misclassification and Anticipation," Papers 2507.20415, arXiv.org, revised Dec 2025.
    64. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Predictive ability tests with possibly overlapping models," Journal of Econometrics, Elsevier, vol. 241(1).
    65. Rachida Ouysse, 2014. "On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models," Computational Statistics, Springer, vol. 29(1), pages 233-261, February.
    66. Lavergne, Pascal & Bertail, Patrice, 2020. "Bootstrapping Quasi Likelihood Ratio Tests under Misspecification," TSE Working Papers 20-1102, Toulouse School of Economics (TSE).
    67. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Stephan Smeekes, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 398-415, May.
    68. Simos G. Meintanis & Christos K. Papadimitriou, 2022. "Goodness--of--fit tests for stochastic frontier models based on the characteristic function," Journal of Productivity Analysis, Springer, vol. 57(3), pages 285-296, June.
    69. Christian H. Weiß & Esmeralda Gonçalves & Nazaré Mendes Lopes, 2017. "Testing the compounding structure of the CP-INARCH model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(5), pages 571-603, July.
    70. Apostolos Batsidis & María Dolores Jiménez-Gamero & Artur J. Lemonte, 2020. "On goodness-of-fit tests for the Bell distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(3), pages 297-319, April.
    71. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    72. Moon, Seongman & Velasco, Carlos, 2013. "Tests for m-dependence based on sample splitting methods," Journal of Econometrics, Elsevier, vol. 173(2), pages 143-159.
    73. Sangyeol Lee & Simos G. Meintanis & Minyoung Jo, 2019. "Inferential procedures based on the integrated empirical characteristic function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 357-386, September.
    74. Šárka Hudecová & Marie Hušková & Simos G. Meintanis, 2017. "Tests for Structural Changes in Time Series of Counts," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 843-865, December.
    75. Simos G. Meintanis & Joseph Ngatchou-Wandji & Leonard Santana & Marius Smuts, 2025. "Goodness-of-fit tests for discrete response models with covariates," Statistical Papers, Springer, vol. 66(3), pages 1-36, April.
    76. Davidson, Russell & Trokić, Mirza, 2020. "The fast iterated bootstrap," Journal of Econometrics, Elsevier, vol. 218(2), pages 451-475.
    77. Karl-Heinz Schild & Karsten Schweikert, 2019. "On the Validity of Tests for Asymmetry in Residual-Based Threshold Cointegration Models," Econometrics, MDPI, vol. 7(1), pages 1-13, March.
    78. Hidalgo, Javier & Lee, Jungyoon & Seo, Myung Hwan, 2019. "Robust inference for threshold regression models," LSE Research Online Documents on Economics 100333, London School of Economics and Political Science, LSE Library.
    79. Xingyu Li & Yan Shen & Qiankun Zhou, 2022. "Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects," Papers 2202.12078, arXiv.org.
    80. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    81. Jiaying Gu & Roger Koenker & Stanislav Volgushev, 2017. "Testing for homogeneity in mixture models," CeMMAP working papers CWP39/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    82. J. S. Allison & M. Hušková & S. G. Meintanis, 2018. "Testing the adequacy of semiparametric transformation models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 70-94, March.
    83. Su, Jen-Je & Cheung, Adrian (Wai-Kong) & Roca, Eduardo, 2014. "Does Purchasing Power Parity hold? New evidence from wild-bootstrapped nonlinear unit root tests in the presence of heteroskedasticity," Economic Modelling, Elsevier, vol. 36(C), pages 161-171.
    84. David Kang & Seojeong Lee, 2025. "Misspecification-Robust Asymptotic and Bootstrap Inference for Nonsmooth GMM," Working Papers 423284005, Lancaster University Management School, Economics Department.
    85. Javier Hidalgo & Jungyoon Lee & Myung Hwan Seo, 2017. "Robust Inference and Testing of Continuity in Threshold Regression Models," STICERD - Econometrics Paper Series 590, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    86. Žikica Lukić & Bojana Milošević, 2024. "Change-point analysis for matrix data: the empirical Hankel transform approach," Statistical Papers, Springer, vol. 65(9), pages 5955-5980, December.
    87. Lucas Girard & Elia Lapenta, 2026. "Average Marginal Effects in One-Step Partially Linear Instrumental Regressions," Papers 2604.11393, arXiv.org.
    88. Royer, Julien, 2021. "Conditional asymmetry in Power ARCH($\infty$) models," MPRA Paper 109118, University Library of Munich, Germany.
    89. Weiß, Christian H. & Steuer, Detlef & Jentsch, Carsten & Testik, Murat Caner, 2018. "Guaranteed conditional ARL performance in the presence of autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 367-379.
    90. Myung Hwan Seo & Sueyoul Kim & Young-Joo Kim, 2019. "Estimation of Dynamic Panel Threshold Model using Stata," Papers 1902.10318, arXiv.org.
    91. Hidalgo, Javier & Seo, Myung Hwan, 2015. "Specification tests for lattice processes," LSE Research Online Documents on Economics 66104, London School of Economics and Political Science, LSE Library.
    92. Andrés González & Timo Teräsvirta & Dick van Dijk & Yukai Yang, 2017. "Panel Smooth Transition Regression Models," CREATES Research Papers 2017-36, Department of Economics and Business Economics, Aarhus University.
    93. Morine Delhelle & Ingrid Van Keilegom, 2025. "Copula based dependent censoring in cure models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 34(2), pages 361-382, June.
    94. Yang Wang & Baoying Yang, 2025. "A Self-Normalized Online Monitoring Method Based on the Characteristic Function," Mathematics, MDPI, vol. 13(5), pages 1-16, February.
    95. Marc Hallin & Simos Meintanis & Klaus Nordhausen, 2024. "Consistent Distribution–Free Affine–Invariant Tests for the Validity of Independent Component Models," Working Papers ECARES 2024-04, ULB -- Universite Libre de Bruxelles.
    96. Seo, Juwon, 2018. "Tests of stochastic monotonicity with improved power," Journal of Econometrics, Elsevier, vol. 207(1), pages 53-70.
    97. Jiaying Gu & Roger Koenker & Stanislav Volgushev, 2017. "Testing for homogeneity in mixture models," CeMMAP working papers 39/17, Institute for Fiscal Studies.
    98. Wang, Shaochen & Weiß, Christian H., 2023. "New characterizations of the (discrete) Lindley distribution and their applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 310-322.
    99. Christian Francq & Baye Matar Kandji & Jean-Michel Zakoian, 2022. "Inference on Multiplicative Component GARCH without any Small-Order Moment," Working Papers 2022-09, Center for Research in Economics and Statistics.
    100. Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2022. "Instrumental variable quantile regression under random right censoring," Papers 2209.01429, arXiv.org, revised Feb 2023.
    101. Jean-Pierre Florens & Elia Lapenta, 2022. "Partly Linear Instrumental Variables Regressions without Smoothing on the Instruments," Papers 2212.11012, arXiv.org, revised Oct 2023.
    102. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 244(2).
    103. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    104. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.
    105. Jiménez-Gamero, M.D. & Alba-Fernández, M.V. & Jodrá, P. & Barranco-Chamorro, I., 2015. "An approximation to the null distribution of a class of Cramér–von Mises statistics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 118(C), pages 258-272.
    106. Simos G. Meintanis & Bojana Milošević & Marko Obradović, 2020. "Goodness-of-fit tests in conditional duration models," Statistical Papers, Springer, vol. 61(1), pages 123-140, February.
    107. Bruno Ebner & Bernhard Klar & Simos G. Meintanis, 2018. "Fourier inference for stochastic volatility models with heavy-tailed innovations," Statistical Papers, Springer, vol. 59(3), pages 1043-1060, September.
    108. Gilles Crommen & Jad Beyhum & Ingrid Van Keilegom, 2025. "Estimation of the complier causal hazard ratio under dependent censoring," Papers 2504.02096, arXiv.org.

  18. Raffaella Giacomini, 2012. "Incorporating theoretical restrictions into forecasting by projection methods," 2012 Meeting Papers 548, Society for Economic Dynamics.

    Cited by:

    1. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2014. "No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," CEPR Discussion Papers 9848, Centre for Economic Policy Research.
    2. Nicholas Fawcett & Lena Koerber & Riccardo Masolo & Matthew Waldron, 2015. "Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis," Bank of England Staff Working Paper series 538, Bank of England.
    3. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    4. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers 21/13, Institute for Fiscal Studies.

  19. Andrea Carriero & Raffaella Giacomini, 2011. "How useful are no-arbitrage restrictions for forecasting the term structure of interest rates?," Post-Print hal-00844809, HAL.

    Cited by:

    1. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    2. Andrea Carriero & Lorenzo Ricci & Elisabetta Vangelista, 2022. "Expectations and term premia in EFSF bond yields," Working Papers 54, European Stability Mechanism.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," Working Papers 20-27, Federal Reserve Bank of Cleveland.
    4. Giacomini, Raffaella & Ragusa, Giuseppe, 2011. "Incorporating theoretical restrictions into forecasting by projection methods," CEPR Discussion Papers 8604, Centre for Economic Policy Research.
    5. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," MPRA Paper 63844, University Library of Munich, Germany.
    6. Tommaso Tornese, 2023. "A Euro Area Term Structure Model with Time Varying Exposures," BAFFI CAREFIN Working Papers 23199, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    7. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, Centre for Economic Policy Research.
    8. Fabricio Tourrucôo & João F. Caldeira & Guilherme V. Moura & André A. P. Santos, 2016. "Forecasting The Yield Curve With The Arbitrage-Free Dynamic Nelson-Siegel Model: Brazilian Evidence," Anais do XLII Encontro Nacional de Economia [Proceedings of the 42nd Brazilian Economics Meeting] 028, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    9. Cao, Shuo & Crump, Richard K. & Eusepi, Stefano & Moench, Emanuel, 2020. "Fundamental Disagreement about Monetary Policy and the Term Structure of Interest Rates," CEPR Discussion Papers 15122, Centre for Economic Policy Research.
    10. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    11. Chen, Jiazi & Hong, Zhiwu & Niu, Linlin, 2025. "Forecasting interest rates with shifting endpoints: The role of the functional demographic age distribution," International Journal of Forecasting, Elsevier, vol. 41(1), pages 153-174.
    12. Carlo A. Favero & Arie E. Gozluklu & Haoxi Yang, 2016. "Demographics and the Behavior of Interest Rates," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 64(4), pages 732-776, November.
    13. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2014. "Forecasting the oil-gasoline price relationship: should we care about the Rockets and the Feathers?," Working Papers 269, University of Milano-Bicocca, Department of Economics, revised Mar 2014.
    14. Eo, Yunjong & Kang, Kyu Ho, 2020. "The effects of conventional and unconventional monetary policy on forecasting the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    15. Martin Beraja & Erik Hurst & Juan Ospina, 2016. "The Aggregate Implications of Regional Business Cycles," NBER Working Papers 21956, National Bureau of Economic Research, Inc.
    16. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    17. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    18. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    19. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    20. Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Theory-coherent forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 145-155.
    21. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    22. Vieira, Fausto José Araújo & Chague, Fernando Daniel & Fernandes, Marcelo, 2017. "A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US," Textos para discussão 445, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    23. Guidolin, Massimo & Pedio, Manuela, 2019. "Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    24. Wali ULLAH & Khadija Malik BARI, 2018. "The Term Structure of Government Bond Yields in an Emerging Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-28, September.
    25. Caio Almeida & Axel Simonsen & José Valentim Vicente, 2012. "Forecasting Bond Yields with Segmented Term Structure Models," Working Papers Series 288, Central Bank of Brazil, Research Department.
    26. Frank J. Fabozzi & Francesco A. Fabozzi & Diana Tunaru, 2023. "A comparison of multi-factor term structure models for interbank rates," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 323-356, July.
    27. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.

  20. Raffaella Giacomini & Barbara Rossi, 2009. "Model Comparisons in Unstable Environments," Working Papers 09-10, Duke University, Department of Economics.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Rossi, José Luiz Júnior, 2013. "Liquidity and Exchange Rates," Insper Working Papers wpe_325, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    3. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    4. Chollete, Loran & Schmeidler, David, 2014. "Extreme Events and the Origin of Central Bank Priors," UiS Working Papers in Economics and Finance 2014/15, University of Stavanger.
    5. Chang Liu & Biqian Zhang & Xuefei Wang & Min Guo, 2022. "Account-level analytic hierarchical mixing modeling for credit risk of Chinese Government financing vehicle portfolios," Empirical Economics, Springer, vol. 62(6), pages 2771-2798, June.
    6. José Luiz Rossi Júnior, 2014. "The Usefulness of Financial Variables in Predicting Exchange Rate Movements," Business and Economics Working Papers 198, Unidade de Negocios e Economia, Insper.
    7. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    8. Ana Beatriz Galvão & Liudas Giraitis & George Kapetanios & Katerina Petrova, 2015. "A Bayesian Local Likelihood Method for Modelling Parameter Time Variation in DSGE Models," Working Papers 770, Queen Mary University of London, School of Economics and Finance.
    9. José Luiz Rossi Júnior, 2013. "Liquidity and Exchange Rates," Business and Economics Working Papers 181, Unidade de Negocios e Economia, Insper.
    10. Rossi, José Luiz Júnior, 2014. "The Usefulness of Financial Variables in Predicting Exchange Rate Movements," Insper Working Papers wpe_332, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    11. Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
    12. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    13. Alexandra Horobet & Irina Mnohoghitnei & Emanuela Marinela Luminita Zlatea & Lucian Belascu, 2022. "The Interplay between Digitalization, Education and Financial Development: A European Case Study," JRFM, MDPI, vol. 15(3), pages 1-23, March.
    14. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    15. Leandro M. Magnusson & Sophocles Mavroeidis, 2011. "Identification Using Stability Restrictions," Working Papers 1116, Tulane University, Department of Economics.
    16. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, Centre for Economic Policy Research.
    17. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    18. Weber, Enzo & Zika, Gerd, 2013. "Labour market forecasting : is disaggregation useful?," IAB-Discussion Paper 201314, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    19. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2015. "Forecasting with Instabilities: an Application to DSGE Models with Financial Frictions," Working Papers 201523, School of Economics, University College Dublin.
    20. William J. Procasky & Anwen Yin, 2022. "Forecasting high‐yield equity and CDS index returns: Does observed cross‐market informational flow have predictive power?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1466-1490, August.

  21. Giacomini, Raffaella & Rossi, Barbara, 2008. "Forecast Comparisons in Unstable Environments," Working Papers 08-04, Duke University, Department of Economics.

    Cited by:

    1. Juan Carlos Pérez-Velasco Pavón, 2009. "Determinantes de la demanda por la denominación promedio de billete: el caso de México," Monetaria, CEMLA, vol. 0(4), pages 523-548, octubre-d.
    2. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
    3. Bańbura, Marta & Bobeica, Elena, 2020. "Does the Phillips curve help to forecast euro area inflation?," Working Paper Series 2471, European Central Bank.
    4. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    5. El-Shagi, Makram & Tochkov, Kiril, 2022. "Divisia monetary aggregates for Russia: Money demand, GDP nowcasting and the price puzzle," Economic Systems, Elsevier, vol. 46(4).
    6. Benchimol, Jonathan & El-Shagi, Makram, 2020. "Forecast performance in times of terrorism," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 91, pages 386-402.
    7. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    8. Constantin Bürgi, 2023. "How to Deal With Missing Observations in Surveys of Professional Forecasters," CESifo Working Paper Series 10203, CESifo.
    9. Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2021. "Emotions in Macroeconomic News and their Impact on the European Bond Market," Papers 2106.15698, arXiv.org.
    10. Liu, Xiaochun, 2015. "Modeling time-varying skewness via decomposition for out-of-sample forecast," International Journal of Forecasting, Elsevier, vol. 31(2), pages 296-311.
    11. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    12. Ivan Petrella & Davide Delle Monache, 2016. "Adaptive models and heavy tails," Bank of England Staff Working Paper series 577, Bank of England.
    13. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    14. Barbara Rossi, 2014. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 510-514, October.
    15. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    16. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    17. Crespo-Cuaresma, Jesus & Fortin, Ines & Hlouskova, Jaroslava & Obersteiner, Michael, 2021. "Regime-dependent commodity price dynamics: A predictive analysis," IHS Working Paper Series 28, Institute for Advanced Studies.
    18. Yuchen Zhang & Shigeyuki Hamori, 2020. "The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models," JRFM, MDPI, vol. 13(3), pages 1-16, March.
    19. David I. Harvey & Stephen J. Leybourne & Yang Zu, 2024. "Tests for equal forecast accuracy under heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 850-869, August.
    20. El-Shagi, Makram & Giesen, Sebastian & Jung, Alexander, 2016. "Revisiting the relative forecast performances of Fed staff and private forecasters: A dynamic approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 313-323.
    21. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    22. Hännikäinen, Jari, 2017. "When does the yield curve contain predictive power? Evidence from a data-rich environment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1044-1064.
    23. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    24. Congressional Budget Office, 2022. "A Markov-Switching Model of the Unemployment Rate: Working Paper 2022-05," Working Papers 57582, Congressional Budget Office.
    25. Jonathan Benchimol & Makram El-Shagi & Yossi Saadon, 2020. "Do Expert Experience and Characteristics Affect Inflation Forecasts?," Bank of Israel Working Papers 2020.11, Bank of Israel.
    26. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    27. El-Shagi, Makram, 2019. "Rationality tests in the presence of instabilities in finite samples," Economic Modelling, Elsevier, vol. 79(C), pages 242-246.
    28. Guarin, Alexander & Lozano, Ignacio, 2017. "Credit funding and banking fragility: A forecasting model for emerging economies," Emerging Markets Review, Elsevier, vol. 32(C), pages 168-189.
    29. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    30. Tobias Fissler & Yannick Hoga, 2021. "Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability," Papers 2104.10673, arXiv.org, revised Feb 2022.
    31. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    32. Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2020. "Comparing Forecast Performance with State Dependence," CEPR Discussion Papers 15217, Centre for Economic Policy Research.
    33. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    34. Camacho, Maximo & Martinez-Martin, Jaime, 2015. "Monitoring the world business cycle," Economic Modelling, Elsevier, vol. 51(C), pages 617-625.
    35. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2019. "Forecasting with instabilities: an application to DSGE models with financial frictions," Temi di discussione (Economic working papers) 1234, Bank of Italy, Economic Research and International Relations Area.
    36. Semenov, Andrei, 2024. "Overreaction and underreaction to new information and the directional forecast of exchange rates," International Review of Economics & Finance, Elsevier, vol. 96(PC).
    37. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "Macroeconomic Data Transformations Matter," Papers 2008.01714, arXiv.org, revised Mar 2021.
    38. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    39. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    40. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Anchoring the yield curve using survey expectations," CeMMAP working papers CWP52/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    41. Breen, John David & Hu, Liang, 2021. "The predictive content of oil price and volatility: New evidence on exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    42. Cars Hommes & Mario He & Sebastian Poledna & Melissa Siqueira & Yang Zhang, 2022. "CANVAS: A Canadian Behavioral Agent-Based Model," Staff Working Papers 22-51, Bank of Canada.
    43. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," NBER Working Papers 19469, National Bureau of Economic Research, Inc.
    44. Wu, Jyh-Lin & Wang, Yi-Chiuan, 2013. "Fundamentals, forecast combinations and nominal exchange-rate predictability," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 129-145.
    45. Ayse Kabukcuoglu & Enrique Martínez-García, 2016. "What Helps Forecast U.S. Inflation?—Mind the Gap!," Koç University-TUSIAD Economic Research Forum Working Papers 1615, Koc University-TUSIAD Economic Research Forum.
    46. Kleppe, Tore Selland & Liesenfeld, Roman & Moura, Guilherme Valle & Oglend, Atle, 2022. "Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility," Econometrics and Statistics, Elsevier, vol. 23(C), pages 105-127.
    47. Mayer, Alexander & Wied, Dominik & Troster, Victor, 2025. "Quantile Granger causality in the presence of instability," Journal of Econometrics, Elsevier, vol. 249(PB).
    48. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
    49. Barbara Rossi, 2011. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 25-29, August.
    50. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    51. Shovon Sengupta & Bhanu Pratap & Amit Pawar, 2025. "Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning," Papers 2504.05350, arXiv.org.
    52. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    53. Nonejad, Nima, 2021. "Predicting equity premium using news-based economic policy uncertainty: Not all uncertainty changes are equally important," International Review of Financial Analysis, Elsevier, vol. 77(C).
    54. Tu, Yundong & Wang, Siwei, 2025. "Quantile prediction with factor-augmented regression: Structural instability and model uncertainty," Journal of Econometrics, Elsevier, vol. 249(PB).
    55. Luis Filipe Martins & Pierre Perron, 2016. "Improved Tests for Forecast Comparisons in the Presence of Instabilities," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 650-659, September.
    56. Gonçalves, Sílvia & McCracken, Michael W. & Yao, Yongxu, 2025. "Bootstrapping out-of-sample predictability tests with real-time data," Journal of Econometrics, Elsevier, vol. 247(C).
    57. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    58. Nonejad, Nima, 2019. "Forecasting aggregate equity return volatility using crude oil price volatility: The role of nonlinearities and asymmetries," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    59. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    60. Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
    61. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.
    62. Augustus J. Panton, 2020. "Climate Hysteresis and Monetary Policy," CAMA Working Papers 2020-76, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    63. Hambuckers, J. & Ulm, M., 2023. "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, vol. 129(C).
    64. Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2016. "Dynamic Factor Model with Infinite Dimensional Factor Space: Forecasting," Working Papers ECARES ECARES 2016-16, ULB -- Universite Libre de Bruxelles.
    65. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    66. Procasky, William J. & Yin, Anwen, 2025. "Evolution of the relative efficiency of CDS and equity markets in Japan: Does one market have a long-term informational advantage over the other?," Pacific-Basin Finance Journal, Elsevier, vol. 92(C).
    67. Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, 2021. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 202109, University of Liverpool, Department of Economics.
    68. Florens Odendahl & Barbara Rossi & Tatevik Sekhposyan, 2021. "Evaluating Forecast Performance with State Dependence," Working Papers 1295, Barcelona School of Economics.
    69. Wang, Rudan & Morley, Bruce & Stamatogiannis, Michalis P., 2019. "Forecasting the exchange rate using nonlinear Taylor rule based models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 429-442.
    70. Alessio Brini, 2026. "Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks," Papers 2607.05291, arXiv.org.
    71. Yu‐Sheng Lai, 2022. "Use of high‐frequency data to evaluate the performance of dynamic hedging strategies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 104-124, January.
    72. Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Neural forecasting of the Italian sovereign bond market with economic news," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 197-224, December.
    73. Douglas, Christopher C. & Kolar, Marek, 2009. "Capturing the time dynamics of central bank intervention," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(5), pages 950-968, December.
    74. Giacomini, Raffaella & Ragusa, Giuseppe, 2011. "Incorporating theoretical restrictions into forecasting by projection methods," CEPR Discussion Papers 8604, Centre for Economic Policy Research.
    75. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
    76. Emilio Colombo & Matteo Pelagatti, 2019. "Statistical Learning and Exchange Rate Forecasting," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1901, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    77. El-Shagi, Makram, 2011. "Inflation expectations: Does the market beat econometric forecasts?," The North American Journal of Economics and Finance, Elsevier, vol. 22(3), pages 298-319.
    78. Feng, Wenjun & Zhang, Zhengjun, 2023. "Currency exchange rate predictability: The new power of Bitcoin prices," Journal of International Money and Finance, Elsevier, vol. 132(C).
    79. Cai, Zongwu & Juhl, Ted, 2023. "The distribution of rolling regression estimators," Journal of Econometrics, Elsevier, vol. 235(2), pages 1447-1463.
    80. Chiara Casoli & Riccardo Lucchetti, 2026. "A rotated Dynamic Factor Model for the yield curve: squeezing out information when it matters," Working Papers 2026.03, Fondazione Eni Enrico Mattei.
    81. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Papers 2305.19921, arXiv.org.
    82. Wei Kuang, 2024. "High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-35, May.
    83. Rodrigo Sekkel, 2014. "Balance Sheets of Financial Intermediaries: Do They Forecast Economic Activity?," Staff Working Papers 14-40, Bank of Canada.
    84. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    85. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    86. Francesco Ravazzolo & Philip Rothman, 2011. "Oil and US GDP: A Real-Time out-of Sample Examination," Working Papers No 2/2011, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    87. Kruse-Becher, Robinson & Letixerant, Philip, 2025. "Oil price expectations in explosive phases," Energy Economics, Elsevier, vol. 152(C).
    88. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.
    89. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    90. Wei Kuang, 2021. "Conditional covariance matrix forecast using the hybrid exponentially weighted moving average approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1398-1419, December.
    91. Ravazzolo, Francesco & Vespignani, Joaquin, 2015. "A new monthly indicator of global real economic activity," Working Papers 2015-07, University of Tasmania, Tasmanian School of Business and Economics.
    92. Marta Crispino & Vincenzo Mariani, 2025. "A Tool to Nowcast Tourist Overnight Stays with Payment Data and Complementary Indicators," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 11(1), pages 285-312, March.
    93. Ferraro, Domenico & Rogoff, Kenneth & Rossi, Barbara, 2015. "Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates," Journal of International Money and Finance, Elsevier, vol. 54(C), pages 116-141.
    94. Tomás Marinozzi, 2023. "Forecasting Inflation in Argentina: A Probabilistic Approach," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(81), pages 81-110, May.
    95. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    96. Garrón, Ignacio & Rodríguez-Caballero, Vladimir & Ruiz, Esther, 2026. "International factors and inflation risks," International Journal of Forecasting, Elsevier, vol. 42(3), pages 889-908.
    97. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    98. Catherine Chen & Chen Gao & Jonathon Hazell & Lihua Lei & Chen Lian, 2026. "Forecasting Inflation with Microdata: An Adaptive Machine Learning Approach," Papers 2607.12345, arXiv.org.
    99. Christopher S. Sutherland, 2023. "Forward guidance and expectation formation: A narrative approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 222-241, March.
    100. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2021. "Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1618-1639, October.
    101. Anwen Yin, 2024. "Predictive model averaging with parameter instability and heteroskedasticity," Bulletin of Economic Research, Wiley Blackwell, vol. 76(2), pages 418-442, April.
    102. Laura Carabotta & Peter Claeys, 2024. "Combine to compete: Improving fiscal forecast accuracy over time," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 948-982, July.
    103. Patrick Minford & Yongdeng Xu & Peng Zhou, 2015. "How Good are Out of Sample Forecasting Tests on DSGE Models?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 1(3), pages 333-351, November.
    104. El-Shagi, Makram & Kelly, Logan, 2019. "What can we learn from country-level liquidity in the EMU?," Journal of Financial Stability, Elsevier, vol. 42(C), pages 75-83.
    105. Emilio Zanetti Chini, 2017. "Generalizing Smooth Transition Autoregressions," DEM Working Papers Series 138, University of Pavia, Department of Economics and Management.
    106. Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024. "What drives the European carbon market? Macroeconomic factors and forecasts," Working Papers 2024.02, Fondazione Eni Enrico Mattei.
    107. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    108. Régis Barnichon & Christopher J. Nekarda, 2013. "The ins and outs of forecasting unemployment: Using labor force flows to forecast the labor market," Finance and Economics Discussion Series 2013-19, Board of Governors of the Federal Reserve System (U.S.).
    109. Angela Capolongo & Claudia Pacella, 2021. "Forecasting inflation in the euro area: countries matter!," Empirical Economics, Springer, vol. 61(5), pages 2477-2499, November.
    110. Reichenbacher, Michael & Schuster, Philipp, 2022. "Size-adapted bond liquidity measures and their asset pricing implications," Journal of Financial Economics, Elsevier, vol. 146(2), pages 425-443.
    111. Zhu, Xiaoneng & Zhu, Jie, 2013. "Predicting stock returns: A regime-switching combination approach and economic links," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4120-4133.
    112. Wensheng Kang & Ronald A. Ratti & Joaquin L. Vespignani, 2016. "The Implications of Liquidity Expansion in China for the US Dollar," CAMA Working Papers 2016-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    113. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    114. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    115. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    116. Zhu, Yanjian & Zhu, Xiaoneng, 2014. "European business cycles and stock return predictability," Finance Research Letters, Elsevier, vol. 11(4), pages 446-453.
    117. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    118. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2016. "The implications of monetary expansion in China for the US dollar," Journal of Asian Economics, Elsevier, vol. 46(C), pages 71-84.
    119. Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2019. "Exploiting ergodicity in forecasts of corporate profitability," BERG Working Paper Series 147, Bamberg University, Bamberg Economic Research Group.
    120. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.
    121. Nima Zarrabi & Stuart Snaith & Jerry Coakley, 2022. "Exchange rate forecasting using economic models and technical trading rules," The European Journal of Finance, Taylor & Francis Journals, vol. 28(10), pages 997-1018, July.
    122. Richard Ashley & Randal Verbrugge, 2025. "The intermittent Phillips curve: Finding a stable (but persistence‐dependent) Phillips curve model specification," Economic Inquiry, Western Economic Association International, vol. 63(3), pages 926-944, July.
    123. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    124. Adriana Fernandez & Evan F. Koenig & Alex Nikolsko-Rzhevskyy, 2008. "The relative performance of alternative Taylor rule specifications," Staff Papers, Federal Reserve Bank of Dallas, issue Jun.
    125. Sophie Béreau & Faubert Violaine & Katia Schmidt, 2018. "Explaining and Forecasting Euro Area Inflation: the Role of Domestic and Global Factors," Working papers 663, Banque de France.
    126. Olivier Fortin‐Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "A large Canadian database for macroeconomic analysis," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(4), pages 1799-1833, November.
    127. Andriantomanga, Zo, 2023. "The role of survey-based expectations in real-time forecasting of US inflation," MPRA Paper 119904, University Library of Munich, Germany.
    128. Zhou, Zhongbao & Fu, Zhangyan & Jiang, Yong & Zeng, Ximei & Lin, Ling, 2020. "Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 34(C).
    129. Gargallo, Pilar & Lample, Luis & Miguel, Jesús & Salvador, Manuel, 2022. "Dynamic comparison of portfolio risk: Clean vs dirty energy," Finance Research Letters, Elsevier, vol. 47(PA).
    130. Heinisch, Katja & Scheufele, Rolf, 2017. "Should forecasters use real-time data to evaluate leading indicator models for GDP prediction? German evidence," IWH Discussion Papers 5/2017, Halle Institute for Economic Research (IWH).
    131. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    132. Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022. "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, vol. 106(C).
    133. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, Centre for Economic Policy Research.
    134. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    135. Boriss Siliverstovs, 2015. "Dissecting Models' Forecasting Performance," KOF Working papers 15-397, KOF Swiss Economic Institute, ETH Zurich.
    136. Alessandro Casini & Taosong Deng & Pierre Perron, 2021. "Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference," Papers 2103.01604, arXiv.org, revised Sep 2024.
    137. Uluc Aysun & Melanie Guldi, 2026. "Revisiting exchange rate predictability: Can machine learning with theoretical filtering outperform canonical models?," Working Papers 2026-01, University of Central Florida, Department of Economics.
    138. Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile 514, Central Bank of Chile.
    139. Hännikäinen, Jari, 2014. "The mortgage spread as a predictor of real-time economic activity," MPRA Paper 58360, University Library of Munich, Germany.
    140. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    141. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    142. Aubry, Mathilde & Renou-Maissant, Patricia, 2014. "Semiconductor industry cycles: Explanatory factors and forecasting," Economic Modelling, Elsevier, vol. 39(C), pages 221-231.
    143. Manzan, Sebastiano & Zerom, Dawit, 2013. "Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?," International Journal of Forecasting, Elsevier, vol. 29(3), pages 469-478.
    144. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2014. "On the Sources of Uncertainty in Exchange Rate Predictability," Working Papers 2014_16, Business School - Economics, University of Glasgow.
    145. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
    146. Christopher S Sutherland, 2022. "Forward guidance and expectation formation: A narrative approach," BIS Working Papers 1024, Bank for International Settlements.
    147. Carstensen, Kai & Wohlrabe, Klaus & Ziegler, Christina, 2010. "Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production," Discussion Papers in Economics 11442, University of Munich, Department of Economics.
    148. Bhanu Pratap & Amit Pawar & Shovon Sengupta, 2026. "Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 67(3), pages 2301-2344, March.
    149. Norman R. Swanson, 2016. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 348-353, July.
    150. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
    151. Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2025. "Predictive Density Combination Using Bayesian Machine Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 66(3), pages 1287-1315, August.
    152. Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024. "Forecasting the Price of Carbon with Macroeconomic and Financial variables," Papers 2402.04828, arXiv.org, revised Oct 2026.
    153. Nonejad, Nima, 2021. "Predicting equity premium by conditioning on macroeconomic variables: A prediction selection strategy using the price of crude oil," Finance Research Letters, Elsevier, vol. 41(C).
    154. Michael W. McCracken, 2019. "Tests of Conditional Predictive Ability: Some Simulation Evidence," Working Papers 2019-11, Federal Reserve Bank of St. Louis.
    155. Ghulame Rubbaniy & Ali Awais Khalid & Stathis Polyzos & Balqees Naser Almessabi, 2022. "Cyclicality of capital adequacy ratios in heterogeneous environment: A nonlinear panel smooth transition regression explanation," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 1960-1979, September.
    156. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.
    157. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    158. Hännikäinen, Jari, 2014. "Multi-step forecasting in the presence of breaks," MPRA Paper 55816, University Library of Munich, Germany.
    159. Bae, Juhee, 2024. "Factor-augmented forecasting in big data," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1660-1688.
    160. David Alaminos & M. Belén Salas & Manuel Á. Fernández-Gámez, 2023. "Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-21, December.
    161. Hotta, Luiz Koodi & Trucíos Maza, Carlos César & Pereira, Pedro L. Valls & Zevallos Herencia, Mauricio Henrique, 2024. "Forecasting VaR and ES through Markov-switching GARCH models: does the specication matter?," Textos para discussão 567, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    162. Brooks, Chris & Burke, Simon P. & Stanescu, Silvia, 2016. "Finite sample weighting of recursive forecast errors," International Journal of Forecasting, Elsevier, vol. 32(2), pages 458-474.
    163. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    164. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    165. Emilio Zanetti Chini, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," CREATES Research Papers 2018-13, Department of Economics and Business Economics, Aarhus University.
    166. Jung, Alexander & El-Shagi, Makram & Giesen, Sebastian, 2013. "Does Central Bank Staff Beat Private Forecasters?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79925, Verein für Socialpolitik / German Economic Association.
    167. Casini, Alessandro & Perron, Pierre, 2024. "Prewhitened long-run variance estimation robust to nonstationarity," Journal of Econometrics, Elsevier, vol. 242(1).
    168. Christian Grimme & Robert Lehmann & Marvin Noeller, 2019. "Forecasting Imports with Information from Abroad," ifo Working Paper Series 294, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    169. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    170. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
    171. Pantelis Promponas & David Alan Peel, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    172. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    173. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    174. Ribeiro, Pinho J., 2017. "Selecting exchange rate fundamentals by bootstrap," International Journal of Forecasting, Elsevier, vol. 33(4), pages 894-914.
    175. Ciner, Cetin, 2017. "Predicting white metal prices by a commodity sensitive exchange rate," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 309-315.
    176. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    177. Delle Chiaie, Simona & Ferrara, Laurent & Giannone, Domenico, 2018. "Common Factors of Commodity Prices," CEPR Discussion Papers 12767, Centre for Economic Policy Research.
    178. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    179. Laura Coroneo & Fabrizio Iacone, 2015. "Comparing predictive accuracy in small samples," Discussion Papers 15/15, Department of Economics, University of York.
    180. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    181. Jung, Alexander & El-Shagi, Makram & Giesen, Sebastian, 2014. "Does the federal reserve staff still beat private forecasters?," Working Paper Series 1635, European Central Bank.
    182. Daniel Borup & Martin Thyrsgaard, 2017. "Statistical tests for equal predictive ability across multiple forecasting methods," CREATES Research Papers 2017-19, Department of Economics and Business Economics, Aarhus University.
    183. Hännikäinen, Jari, 2014. "Zero lower bound, unconventional monetary policy and indicator properties of interest rate spreads," MPRA Paper 56737, University Library of Munich, Germany.
    184. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    185. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    186. Sebastiano Manzan, 2015. "Forecasting the Distribution of Economic Variables in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 144-164, January.
    187. Ba Chu & Shafiullah Qureshi, 2021. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers 21-12, Carleton University, Department of Economics.
    188. Moramarco, Graziano, 2024. "Financial-cycle ratios and medium-term predictions of GDP: Evidence from the United States," International Journal of Forecasting, Elsevier, vol. 40(2), pages 777-795.
    189. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
    190. Duc Do, Nguyen, 2024. "Money/asset ratio as a predictor of inflation," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
    191. N. Kundan Kishor, 2024. "Does the Zillow rent measure help predict CPI rent inflation?," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 59(4), pages 220-226, October.
    192. Ubilava, David, 2016. "The Role of El Niño Southern Oscillation in Commodity Price Movement and Predictability," Working Papers 2016-10, University of Sydney, School of Economics.
    193. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    194. Nonejad, Nima, 2023. "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 91-122.
    195. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.
    196. Nonejad, Nima, 2020. "Crude oil price changes and the United Kingdom real gross domestic product growth rate: An out-of-sample investigation," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    197. Paolo Andreini & Donato Ceci, 2019. "A Horse Race in High Dimensional Space," CEIS Research Paper 452, Tor Vergata University, CEIS, revised 14 Feb 2019.
    198. Barbara Rossi, 2012. "The changing relationship between commodity prices and equity prices in commodity exporting," Economics Working Papers 1405, Department of Economics and Business, Universitat Pompeu Fabra.
    199. Joscha Beckmann & Gary Koop & Dimitris Korobilis & Rainer Alexander Schüssler, 2020. "Exchange rate predictability and dynamic Bayesian learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 410-421, June.
    200. Marine Carrasco & Barbara Rossi, 2016. "In-Sample Inference and Forecasting in Misspecified Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 313-338, July.
    201. Cheng, Xian & Wu, Peng & Liao, Stephen Shaoyi & Wang, Xuelian, 2023. "An integrated model for crude oil forecasting: Causality assessment and technical efficiency," Energy Economics, Elsevier, vol. 117(C).
    202. Ravazzolo, Francesco & Vespignani, Joaquin, 2017. "World steel production: A new monthly indicator of global real economic activity," Working Papers 2017-08, University of Tasmania, Tasmanian School of Business and Economics.
    203. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
    204. Domenico Ferraro & Kenneth S. Rogoff & Barbara Rossi, 2011. "Can oil prices forecast exchange rates?," Working Papers 11-34, Federal Reserve Bank of Philadelphia.
    205. Nonejad, Nima, 2023. "Modeling the out-of-sample predictive relationship between equity premium, returns on the price of crude oil and economic policy uncertainty using multivariate time-varying dimension models," Energy Economics, Elsevier, vol. 126(C).
    206. Rudan Wang & Bruce Morley & Javier Ordóñez, 2015. "The Taylor Rule, Wealth Effects and the Exchange Rate," Working Papers 2015/08, Economics Department, Universitat Jaume I, Castellón (Spain).
    207. Ouysse, Rachida, 2016. "Bayesian model averaging and principal component regression forecasts in a data rich environment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 763-787.
    208. Juan Díaz Maureira & Gustavo Leyva Jiménez, 2009. "Proyección de la inflación chilena en tiempos difíciles," Monetaria, CEMLA, vol. 0(4), pages 491-522, octubre-d.
    209. Nonejad, Nima, 2020. "An observation regarding Hamilton’s recent criticisms of Kilian’s global real economic activity index," Economics Letters, Elsevier, vol. 196(C).
    210. Nicholas Fawcett & Lena Koerber & Riccardo Masolo & Matthew Waldron, 2015. "Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis," Bank of England Staff Working Paper series 538, Bank of England.
    211. Pacheco & Riquelme, 2021. "¿Cómo se siente el presidente?," Asociación Argentina de Economía Política: Working Papers 4500, Asociación Argentina de Economía Política.
    212. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    213. Christopher S. Sutherland, 2020. "Forward Guidance and Expectation Formation: A Narrative Approach," Staff Working Papers 20-40, Bank of Canada.
    214. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    215. Boot, Tom & Pick, Andreas, 2020. "Does modeling a structural break improve forecast accuracy?," Journal of Econometrics, Elsevier, vol. 215(1), pages 35-59.
    216. Lillian R. Gaeto & Sandeep Mazumder, 2019. "Measuring the Accuracy of Federal Reserve Forecasts," Southern Economic Journal, John Wiley & Sons, vol. 85(3), pages 960-984, January.
    217. Gargallo, Pilar & Lample, Luis & Miguel, Jesús A. & Salvador, Manuel, 2024. "Sequential management of energy and low-carbon portfolios," Research in International Business and Finance, Elsevier, vol. 69(C).
    218. Solat, Karo & Tsang, Kwok Ping, 2021. "Forecasting exchange rates with elliptically symmetric principal components," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1085-1091.
    219. Raffaella Giacomini & Barbara Rossi, 2016. "Model Comparisons In Unstable Environments," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(2), pages 369-392, May.
    220. Liu, Xiaochun, 2019. "On tail fatness of macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 62(C).
    221. Procasky, William J. & Yin, Anwen, 2023. "The impact of COVID-19 on the relative market efficiency and forecasting ability of credit derivative and equity markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    222. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    223. Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Theory-coherent forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 145-155.
    224. Zongwu Cai & Ted Juhl, 2020. "The Distribution Of Rolling Regression Estimators," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202218, University of Kansas, Department of Economics, revised Dec 2022.
    225. Lukas Hoesch & Barbara Rossi & Tatevik Sekhposyan, 2020. "Has the Information Channel of Monetary Policy Disappeared? Revisiting the Empirical Evidence," Working Papers 1158, Barcelona School of Economics.
    226. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    227. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
    228. Sengupta, Shovon & Chakraborty, Tanujit & Singh, Sunny Kumar, 2025. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," International Journal of Forecasting, Elsevier, vol. 41(3), pages 953-981.
    229. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
    230. Papenfuß, Patric & Schischke, Amelie & Rathgeber, Andreas, 2025. "Factors of predictive power for metal commodities," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
    231. Barnichon, Regis & Garda, Paula, 2016. "Forecasting unemployment across countries: The ins and outs," European Economic Review, Elsevier, vol. 84(C), pages 165-183.
    232. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2024. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Post-Print hal-05056934, HAL.
    233. Calice, Giovanni & Chen, Jing & Williams, Julian, 2013. "Liquidity spillovers in sovereign bond and CDS markets: An analysis of the Eurozone sovereign debt crisis," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 122-143.
    234. Adriana Cornea‐Madeira & João Madeira, 2022. "Econometric Analysis of Switching Expectations in UK Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(3), pages 651-673, June.
    235. Nonejad, Nima, 2020. "A comprehensive empirical analysis of the predictive impact of the price of crude oil on aggregate equity return volatility," Journal of Commodity Markets, Elsevier, vol. 20(C).
    236. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    237. Sebastiano Manzan & Dawit Zerom, 2015. "Asymmetric Quantile Persistence and Predictability: the Case of US Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(2), pages 297-318, April.
    238. Martin M. Bojaj, 2025. "Dynamic effects of blockchain on financial markets: evidence from TVP-Bayesian VAR with a connectedness approach," Empirical Economics, Springer, vol. 68(5), pages 2159-2197, May.
    239. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2025. "Density forecasts of inflation: A quantile regression forest approach," European Economic Review, Elsevier, vol. 178(C).
    240. Djorno, Candice & Santillana, Mauricio & Yang, Shihao, 2026. "Restoring the forecasting power of Google Trends with statistical preprocessing," International Journal of Forecasting, Elsevier, vol. 42(3), pages 1104-1122.
    241. Andrea Carriero & Raffaella Giacomini, 2011. "How useful are no-arbitrage restrictions for forecasting the term structure of interest rates?," Post-Print hal-00844809, HAL.
    242. Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras & Rangan Gupta, 2015. "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," Working Papers 201548, University of Pretoria, Department of Economics.
    243. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2015. "Forecasting Consumption: The Role of Consumer Confidence in Real Time with many Predictors," Working Papers 2015-02, Towson University, Department of Economics, revised Jul 2015.
    244. Liu, Li & Tan, Siming & Wang, Yudong, 2020. "Can commodity prices forecast exchange rates?," Energy Economics, Elsevier, vol. 87(C).
    245. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
    246. Ricardo Gimeno & José Manuel Marqués-Sevillano, 2009. "Incertidumbre y el precio del riesgo en un proceso de convergencia nominal," Monetaria, CEMLA, vol. 0(4), pages 451-489, octubre-d.
    247. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    248. Jordà, Òscar & Taylor, Alan M., 2012. "The carry trade and fundamentals: Nothing to fear but FEER itself," Journal of International Economics, Elsevier, vol. 88(1), pages 74-90.
    249. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    250. Andrés Schneider, 2009. "Regímenes de flotación administrada: un enfoque de cartera," Monetaria, CEMLA, vol. 0(4), pages 549-584, octubre-d.
    251. Minchul Shin & Nathan Schor, 2026. "ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics," Papers 2603.07458, arXiv.org.
    252. Caldeira, João F. & Cordeiro, Werley C., 2026. "Decomposing nominal and real yield curves and inflation forecasting: Evidence from Brazil," Economics Letters, Elsevier, vol. 258(C).
    253. Nonejad, Nima, 2022. "Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    254. Constantin Anghelache & Madalina-Gabriela Anghel & Alina-Georgiana Solomon, 2017. "National Accounts System: Source of Information in Macroeconomic Forecast," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 7(2), pages 76-82, April.
    255. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, Centre for Economic Policy Research.
    256. Martínez-Martin, Jaime & Morris, Richard & Onorante, Luca & Piersanti, Fabio M., 2019. "Merging structural and reduced-form models for forecasting: opening the DSGE-VAR box," Working Paper Series 2335, European Central Bank.
    257. Dur, Ayşe & Martínez García, Enrique, 2020. "Mind the gap!—A monetarist view of the open-economy Phillips curve," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    258. Christos Tzomakas, 2026. "Monetary policy transmission and the yield curve: the role of external market factors against the backdrop of Brexit," Empirical Economics, Springer, vol. 70(1), pages 1-42, January.
    259. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    260. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
    261. Clements, Michael P., 2021. "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 634-646.
    262. Fabrizio Iacone & Luca Rossini & Andrea Viselli, 2024. "Comparing predictive ability in presence of instability over a very short time," Papers 2405.11954, arXiv.org.
    263. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
    264. Eric Hillebrand & Jakob Guldbæk Mikkelsen & Lars Spreng & Giovanni Urga, 2023. "Exchange rates and macroeconomic fundamentals: Evidence of instabilities from time‐varying factor loadings," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 857-877, September.
    265. Yen H. Lok, 2018. "On the backtesting of trading strategies," 2018 Papers plo493, Job Market Papers.
    266. Martin McCarthy & Stephen Snudden, 2025. "Forecasts of Period-average Exchange Rates: Insights from Real-time Daily Data," RBA Research Discussion Papers rdp2025-09, Reserve Bank of Australia.
    267. Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.
    268. Pawel M. Krolikowski & Kurt G. Lunsford, 2024. "Advance layoff notices and aggregate job loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 462-480, April.
    269. Vermeulen, Philip, 2014. "An evaluation of business survey indices for short-term forecasting: Balance method versus Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 30(4), pages 882-897.
    270. Francis X. Diebold, 2012. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," PIER Working Paper Archive 12-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    271. Cotton, Christopher D., 2026. "Understanding CPI shelter inflation: The importance of the new-tenant/all-tenant rent gap," Journal of Housing Economics, Elsevier, vol. 71(C).
    272. Coroneo, Laura, 2026. "Forecasting for monetary policy," International Journal of Forecasting, Elsevier, vol. 42(1), pages 22-33.
    273. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    274. Francisco Lasso-Valderrama & Héctor M. Zárate-Solano, 2019. "Forecasting the Colombian Unemployment Rate Using Labour Force Flows," Borradores de Economia 1073, Banco de la Republica de Colombia.
    275. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.
    276. Jamali, Ibrahim & Yamani, Ehab, 2019. "Out-of-sample exchange rate predictability in emerging markets: Fundamentals versus technical analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 241-263.
    277. Rossi, Barbara & Sekhposyan, Tatevik, 2016. "Forecast Rationality Tests in the Presence of Instabilities, With Applications to Federal Reserve and Survey Forecasts," CEPR Discussion Papers 11391, Centre for Economic Policy Research.
    278. Dean Croushore, 2012. "Forecast bias in two dimensions," Working Papers 12-9, Federal Reserve Bank of Philadelphia.
    279. Pavel Yaskov, 2010. "Testing for predictive ability in the presence of structural breaks (in Russian)," Quantile, Quantile, issue 8, pages 127-135, July.
    280. De Pace, Pierangelo & Weber, Kyle D., 2016. "The time-varying leading properties of the high yield spread in the United States," International Journal of Forecasting, Elsevier, vol. 32(1), pages 203-230.
    281. Jackson, Karen & Magkonis, Georgios, 2024. "Exchange rate predictability: Fact or fiction?," Journal of International Money and Finance, Elsevier, vol. 142(C).
    282. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    283. Bing Xu, 2015. "Oil prices and UK industry-level stock returns," Applied Economics, Taylor & Francis Journals, vol. 47(25), pages 2608-2627, May.
    284. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    285. Dimos Kambouroudis & David McMillan & Katerina Tsakou, 2019. "Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility," Working Papers 2019-03, Swansea University, School of Management.
    286. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-switching three-pass regression filter," Working Papers 1748, Banco de España.
    287. Lukas Bauer & Ekaterina Kazak, 2025. "Conditional Method Confidence Set," Papers 2505.21278, arXiv.org.
    288. Michael W. McCracken, 2019. "Diverging Tests of Equal Predictive Ability," Working Papers 2019-018, Federal Reserve Bank of St. Louis, revised 09 Mar 2020.
    289. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.
    290. Nonejad, Nima, 2020. "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, vol. 71(C).
    291. William J. Procasky & Anwen Yin, 2022. "Forecasting high‐yield equity and CDS index returns: Does observed cross‐market informational flow have predictive power?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1466-1490, August.
    292. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    293. Sidney M. Caetano & João H. G. Mazzeu, 2026. "Short-term inflation expectations evaluation in the presence of instabilities," Empirical Economics, Springer, vol. 70(2), pages 1-27, February.

  22. Giacomini, Raffaella & Gottschling, Andreas & Haefke, Christian & White, Halbert, 2007. "Mixtures of t-distributions for Finance and Forecasting," Economics Series 216, Institute for Advanced Studies.

    Cited by:

    1. Karl Härdle, Wolfgang & López-Cabrera, Brenda & Teng, Huei-Wen, 2015. "State price densities implied from weather derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 106-125.
    2. Yanlong Wang & Jian Xu & Shao-Lun Huang & Danny Dongning Sun & Xiao-Ping Zhang, 2025. "Assessing Uncertainty in Stock Returns: A Gaussian Mixture Distribution-Based Method," Papers 2503.06929, arXiv.org.
    3. Martínez-Ovando Juan Carlos & Walker Stephen G., 2011. "Time-series Modelling, Stationarity and Bayesian Nonparametric Methods," Working Papers 2011-08, Banco de México.
    4. Chang, Kuang-Liang, 2012. "Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market," Energy Economics, Elsevier, vol. 34(1), pages 294-306.
    5. Shen Peng & Navnit Yadav & Abdel Lisser & Vikas Vikram Singh, 2021. "Chance-constrained games with mixture distributions," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 94(1), pages 71-97, August.
    6. Farbmacher, Helmut & Löw, Leander & Spindler, Martin, 2022. "An explainable attention network for fraud detection in claims management," Journal of Econometrics, Elsevier, vol. 228(2), pages 244-258.
    7. Haas, Markus & Liu, Ji-Chun, 2015. "Theory for a Multivariate Markov--switching GARCH Model with an Application to Stock Markets," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112855, Verein für Socialpolitik / German Economic Association.

  23. Rossi, Barbara & Giacomini, Raffaella, 2006. "Detecting and Predicting Forecast Breakdowns," Working Papers 06-01, Duke University, Department of Economics.

    Cited by:

    1. Pesaran, M.H. & Pick, A. & Pranovich, M., 2011. "Optimal Forecasts in the Presence of Structural Breaks (Updated 14 November 2011)," Cambridge Working Papers in Economics 1163, Faculty of Economics, University of Cambridge.
    2. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    3. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    4. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    5. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    6. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
    7. Barbara Rossi, 2014. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 510-514, October.
    8. Chad Fulton & Kirstin Hubrich, 2021. "Forecasting US Inflation in Real Time," Econometrics, MDPI, vol. 9(4), pages 1-20, October.
    9. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2025. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Information Systems Research, INFORMS, vol. 36(1), pages 552-571, March.
    10. Pettenuzzo, Davide & Timmermann, Allan, 2016. "Forecasting Macroeconomic Variables under Model Instability," CEPR Discussion Papers 11355, Centre for Economic Policy Research.
    11. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    12. Michael Dotsey & Shigeru Fujita & Tom Stark, 2017. "Do Phillips Curves Conditionally Help to Forecast Inflation?," Working Papers 17-26, Federal Reserve Bank of Philadelphia.
    13. Giovanni Calice & Christos Ioannidis & Julian Williams, 2012. "Credit Derivatives and the Default Risk of Large Complex Financial Institutions," Journal of Financial Services Research, Springer;Western Finance Association, vol. 42(1), pages 85-107, October.
    14. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    15. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    16. Klinkowska, Olga & Zadorozhna, Olha, 2026. "The yield curve strikes back: New evidence of its predictive power for economic activity and inflation," International Review of Financial Analysis, Elsevier, vol. 113(C).
    17. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    18. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2016. "Goodness-of-fit test for specification of semiparametric copula dependence models," Journal of Econometrics, Elsevier, vol. 193(1), pages 215-233.
    19. Chollete, Loran & Schmeidler, David, 2014. "Extreme Events and the Origin of Central Bank Priors," UiS Working Papers in Economics and Finance 2014/15, University of Stavanger.
    20. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    21. Procasky, William J. & Yin, Anwen, 2025. "Evolution of the relative efficiency of CDS and equity markets in Japan: Does one market have a long-term informational advantage over the other?," Pacific-Basin Finance Journal, Elsevier, vol. 92(C).
    22. Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019. "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, vol. 35(2), pages 616-633.
    23. Camila Figueroa & Jorge Fornero & Pablo García, 2019. "Hindsight vs. Real time measurement of the output gap: Implications for the Phillips curve in the Chilean Case," Working Papers Central Bank of Chile 854, Central Bank of Chile.
    24. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org, revised Aug 2024.
    25. Casini, Alessandro, 2023. "Theory of evolutionary spectra for heteroskedasticity and autocorrelation robust inference in possibly misspecified and nonstationary models," Journal of Econometrics, Elsevier, vol. 235(2), pages 372-392.
    26. Eric Ghysels & Andros Kourtellos & Elena Andreou, 2012. "Should macroeconomic forecasters use daily financial data and how?," 2012 Meeting Papers 1196, Society for Economic Dynamics.
    27. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    28. Kevin J. Lansing & Adam Hale Shapiro, 2026. "Measuring Inflation Shock Momentum," Working Paper Series 2026-10, Federal Reserve Bank of San Francisco.
    29. Tom Boot & Andreas Pick, 2017. "A near optimal test for structural breaks when forecasting under square error loss," Tinbergen Institute Discussion Papers 17-039/III, Tinbergen Institute.
    30. Argyropoulos, Efthymios & Tzavalis, Elias, 2015. "Real term structure forecasts of consumption growth," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 208-222.
    31. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    32. Salisu, Afees A. & Adekunle, Wasiu & Alimi, Wasiu A. & Emmanuel, Zachariah, 2019. "Predicting exchange rate with commodity prices: New evidence from Westerlund and Narayan (2015) estimator with structural breaks and asymmetries," Resources Policy, Elsevier, vol. 62(C), pages 33-56.
    33. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    34. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    35. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    36. Benjamin Beckers, 2015. "The Real-Time Predictive Content of Asset Price Bubbles for Macro Forecasts," Discussion Papers of DIW Berlin 1496, DIW Berlin, German Institute for Economic Research.
    37. Carstensen, Kai, 2007. "Is core money growth a good and stable inflation predictor in the euro area?," Kiel Working Papers 1318, Kiel Institute for the World Economy.
    38. Jannik Kreye & Philipp Sibbertsen, 2024. "Testing for a Forecast Accuracy Breakdown under Long Memory," Papers 2409.07087, arXiv.org.
    39. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    40. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2023. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Papers 2303.01887, arXiv.org, revised May 2024.
    41. Salisu, Afees A. & Swaray, Raymond & Oloko, Tirimisiyu F., 2019. "Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables," Economic Modelling, Elsevier, vol. 76(C), pages 153-171.
    42. Jakob Krause, 2019. "A convergence-speed-dependent data quantity definition and its effect on risk estimation," Journal of Asset Management, Palgrave Macmillan, vol. 20(6), pages 469-475, October.
    43. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, Centre for Economic Policy Research.
    44. Valentina Corradi & Sainan Jin & Norman R. Swanson, 2023. "Robust forecast superiority testing with an application to assessing pools of expert forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 596-622, June.
    45. Alessandro Casini & Taosong Deng & Pierre Perron, 2021. "Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference," Papers 2103.01604, arXiv.org, revised Sep 2024.
    46. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    47. Döhrn, Roland & Schmidt, Christoph M. & Zimmermann, Tobias, 2008. "Inflation Forecasting with Inflation Sentiment Indicators," Ruhr Economic Papers 80, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    48. Raffaella Giacomini & Barbara Rossi, 2006. "How Stable is the Forecasting Performance of the Yield Curve for Output Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 783-795, December.
    49. Michael W. McCracken, 2019. "Tests of Conditional Predictive Ability: Some Simulation Evidence," Working Papers 2019-11, Federal Reserve Bank of St. Louis.
    50. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    51. Li, Haixi & Sheng, Xuguang Simon & Yang, Jingyun, 2021. "Monitoring recessions: A Bayesian sequential quickest detection method," International Journal of Forecasting, Elsevier, vol. 37(2), pages 500-510.
    52. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    53. Christopher G. Gibbs & Andrey L. Vasnev, 2017. "Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts," Discussion Papers 2017-10, School of Economics, The University of New South Wales.
    54. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    55. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    56. Eric Vansteenberghe, 2026. "Insurance supervision under climate change: a pioneer detection method," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 51(1), pages 176-207, January.
    57. Casini, Alessandro & Perron, Pierre, 2024. "Prewhitened long-run variance estimation robust to nonstationarity," Journal of Econometrics, Elsevier, vol. 242(1).
    58. Argyropoulos, Efthymios & Tzavalis, Elias, 2016. "Forecasting economic activity from yield curve factors," The North American Journal of Economics and Finance, Elsevier, vol. 36(C), pages 293-311.
    59. Sainan Jin & Valentina Corradi & Norman Swanson, 2015. "Robust Forecast Comparison," Departmental Working Papers 201502, Rutgers University, Department of Economics.
    60. Chiu, Ching-Wai (Jeremy) & Hayes, Simon & Kapetanios, George & Theodoridis, Konstantinos, 2019. "A new approach for detecting shifts in forecast accuracy," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1596-1612.
    61. mamatzakis, e & Christodoulakis, G, 2013. "Behavioural Asymmetries in the G7 Foreign Exchange Market," MPRA Paper 51615, University Library of Munich, Germany.
    62. Pincheira-Brown, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2019. "Forecasting inflation in Latin America with core measures," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1060-1071.
    63. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    64. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    65. Mwasi Paza Mboya & Philipp Sibbertsen, 2023. "Optimal forecasts in the presence of discrete structural breaks under long memory," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1889-1908, November.
    66. Michael W. McCracken, 2020. "Tests of Conditional Predictive Ability: Existence, Size, and Power," Working Papers 2020-050, Federal Reserve Bank of St. Louis.
    67. Christopher G. Gibbs, 2015. "Overcoming the Forecast Combination Puzzle: Lessons from the Time-Varying Effciency of Phillips Curve Forecasts of U.S. Inflation," Discussion Papers 2015-09, School of Economics, The University of New South Wales.
    68. Peter Reinhard Hansen & Allan Timmermann, 2015. "Comment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 17-21, January.
    69. Jiahan Li & Ilias Tsiakas & Wei Wang, 2014. "Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?," Working Paper series 05_14, Rimini Centre for Economic Analysis.
    70. Travis J. Berge, 2011. "Forecasting disconnected exchange rates," Research Working Paper RWP 11-12, Federal Reserve Bank of Kansas City.
    71. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    72. Li, Luyang & Yin, Ximing & Yu, Deshui, 2025. "On the time-varying relation between monetary policy uncertainty and bond risk premia," International Review of Financial Analysis, Elsevier, vol. 106(C).
    73. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    74. Boot, Tom & Pick, Andreas, 2020. "Does modeling a structural break improve forecast accuracy?," Journal of Econometrics, Elsevier, vol. 215(1), pages 35-59.
    75. David C. Wheelock & Mark E. Wohar, 2009. "Can the term spread predict output growth and recessions? a survey of the literature," Review, Federal Reserve Bank of St. Louis, vol. 91(Sep), pages 419-440.
    76. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
    77. Procasky, William J. & Yin, Anwen, 2023. "The impact of COVID-19 on the relative market efficiency and forecasting ability of credit derivative and equity markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    78. Li, Jia & Patton, Andrew J., 2018. "Asymptotic inference about predictive accuracy using high frequency data," Journal of Econometrics, Elsevier, vol. 203(2), pages 223-240.
    79. Mamatzakis, Emmanuel & Tsionas, Mike G., 2015. "How are market preferences shaped? The case of sovereign debt of stressed euro-area countries," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 106-116.
    80. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    81. KUROZUMI, Eiji & 黒住, 英司, 2016. "Monitoring Parameter Constancy with Endogenous Regressors," Discussion Papers 2016-01, Graduate School of Economics, Hitotsubashi University.
    82. E. Mamatzakis, 2014. "Revealing asymmetries in the loss function of WTI oil futures market," Empirical Economics, Springer, vol. 47(2), pages 411-426, September.
    83. John Cotter & Emmanuel Eyiah-Donkor & Valerio Potì, 2020. "Commodity Futures Return Predictability and Intertemporal Asset Pricing," Working Papers 202011, Geary Institute, University College Dublin.
    84. PERRON, Pierre & YAMAMOTO, Yohei & 山本, 庸平, 2018. "Testing for Changes in Forecasting Performance," Discussion Papers 2018-03, Graduate School of Economics, Hitotsubashi University.
    85. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    86. Chollete, Lor & Schmeidler, David, 2014. "Misspecification Aversion and Selection of Initial Priors," UiS Working Papers in Economics and Finance 2014/13, University of Stavanger.
    87. Pesaran, M. Hashem & Pick, Andreas & Pranovich, Mikhail, 2013. "Optimal forecasts in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 134-152.
    88. Afees A. Salisu & Wasiu Adekunle & Zachariah Emmanuel & Wasiu A. Alimi, 2018. "Predicting exchange rate with commodity prices: The role of structural breaks and asymmetries," Working Papers 055, Centre for Econometric and Allied Research, University of Ibadan.
    89. Alisa Yusupova & Nicos G. Pavlidis & Efthymios G. Pavlidis, 2019. "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting," Papers 1912.04661, arXiv.org.
    90. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
    91. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.
    92. Mike Buckle & Jing Chen & Julian Williams, 2014. "How Predictable Are Equity Covariance Matrices? Evidence from High‐Frequency Data for Four Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(7), pages 542-557, November.
    93. Rossi, Barbara & Inoue, Atsushi & Jin, Lu, 2014. "Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," CEPR Discussion Papers 10168, Centre for Economic Policy Research.
    94. Clements, Michael P., 2021. "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 634-646.
    95. Salisu, Afees A. & Ndako, Umar B. & Oloko, Tirimisiyu F., 2019. "Assessing the inflation hedging of gold and palladium in OECD countries," Resources Policy, Elsevier, vol. 62(C), pages 357-377.
    96. Federico Belotti & Alessandro Casini & Leopoldo Catania & Stefano Grassi & Pierre Perron, 2021. "Simultaneous Bandwidths Determination for DK-HAC Estimators and Long-Run Variance Estimation in Nonparametric Settings," Papers 2103.00060, arXiv.org.
    97. Mamatzakis, E. & Koutsomanoli-Filippaki, A., 2014. "Testing the rationality of DOE's energy price forecasts under asymmetric loss preferences," Energy Policy, Elsevier, vol. 68(C), pages 567-575.
    98. Mariia Artemova & Francisco Blasques & Siem Jan Koopman & Zhaokun Zhang, 2021. "Forecasting in a changing world: from the great recession to the COVID-19 pandemic," Tinbergen Institute Discussion Papers 21-006/III, Tinbergen Institute.
    99. Yin, Xiao-Cui & Li, Xin & Wang, Min-Hui & Qin, Meng & Shao, Xue-Feng, 2021. "Do economic policy uncertainty and its components predict China's housing returns?," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    100. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
    101. Rossi, Barbara & Sekhposyan, Tatevik, 2016. "Forecast Rationality Tests in the Presence of Instabilities, With Applications to Federal Reserve and Survey Forecasts," CEPR Discussion Papers 11391, Centre for Economic Policy Research.
    102. Langfeng Zhou & Huaguan Li, 2026. "Leveraging Structural Insights: Enhancing Inflation Forecasting With Dynamic Stochastic General Equilibrium‐Informed Long Short‐Term Memory Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(6), pages 3011-3029, September.
    103. Doyle, Matthew, 2006. "Empirical Phillips Curves in OECD Countries: Has There Been A Common Breakdown?," Staff General Research Papers Archive 12684, Iowa State University, Department of Economics.
    104. De Pace, Pierangelo & Weber, Kyle D., 2016. "The time-varying leading properties of the high yield spread in the United States," International Journal of Forecasting, Elsevier, vol. 32(1), pages 203-230.
    105. Todd E. Clark & Michael W. McCracken, 2008. "Tests of equal predictive ability with real-time data," Working Papers 2008-029, Federal Reserve Bank of St. Louis.
    106. Elliott, Graham & Timmermann, Allan, 2016. "Forecasting in Economics and Finance," CEPR Discussion Papers 11354, Centre for Economic Policy Research.
    107. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2025. "MLOps Monitoring at Scale for Digital Platforms," Papers 2504.16789, arXiv.org.
    108. Prasad S Bhattacharya & Dimitrios D Thomakos, 2011. "Improving Forecasting Performance by Window and Model Averaging," CAMA Working Papers 2011-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    109. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.
    110. William J. Procasky & Anwen Yin, 2022. "Forecasting high‐yield equity and CDS index returns: Does observed cross‐market informational flow have predictive power?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1466-1490, August.
    111. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2024. "Improving models and forecasts after equilibrium-mean shifts," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1085-1100.
    112. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.

  24. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers ubs0504, University of Brescia, Department of Economics.

    Cited by:

    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 15-084/III, Tinbergen Institute, revised 03 Jul 2017.
    2. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    3. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Time-varying Combinations of Predictive Densities using Nonlinear Filtering," Tinbergen Institute Discussion Papers 12-118/III, Tinbergen Institute.
    4. Wojciech CHAREMZA & Carlos DÍAZ & Svetlana MAKAROVA, 2019. "Conditional Term Structure of Inflation Forecast Uncertainty: The Copula Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 5-18, March.
    5. Szabolcs Blazsek & Marco Villatoro, 2015. "Is Beta- t -EGARCH(1,1) superior to GARCH(1,1)?," Applied Economics, Taylor & Francis Journals, vol. 47(17), pages 1764-1774, April.
    6. Enzo D'Innocenzo & André Lucas & Anne Opschoor & Xingmin Zhang, 2024. "Heterogeneity and dynamics in network models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 150-173, January.
    7. Caporin, M. & McAleer, M.J., 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Econometric Institute Research Papers EI 2011-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
    9. Ivan Petrella & Davide Delle Monache, 2016. "Adaptive models and heavy tails," Bank of England Staff Working Paper series 577, Bank of England.
    10. Andrea Monticini & Francesco Ravazzolo, 2014. "Forecasting the intraday market price of money," DISCE - Working Papers del Dipartimento di Economia e Finanza def010, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    11. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    12. David I. Harvey & Stephen J. Leybourne & Yang Zu, 2024. "Tests for equal forecast accuracy under heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 850-869, August.
    13. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    14. Huber, Florian, 2017. "Structural breaks in Taylor rule based exchange rate models — Evidence from threshold time varying parameter models," Economics Letters, Elsevier, vol. 150(C), pages 48-52.
    15. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
    16. Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
    17. Massimiliano Caporin & Juliusz Pres, 2010. "Modelling and forecasting wind speed intensity for weather risk management," "Marco Fanno" Working Papers 0106, Dipartimento di Scienze Economiche "Marco Fanno".
    18. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2014. "Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities," Tinbergen Institute Discussion Papers 14-090/III, Tinbergen Institute.
    19. Davide Pettenuzzo & Konstantinos Metaxoglou & Aaron Smith, 2016. "Option-Implied Equity Premium Predictions via Entropic TiltinG," Working Papers 99R, Brandeis School of Business and Economics, revised Aug 2016.
    20. Marcus P. A. Cobb, 2020. "Aggregate density forecasting from disaggregate components using Bayesian VARs," Empirical Economics, Springer, vol. 58(1), pages 287-312, January.
    21. Gloria Gonzalez-Rivera & Yingying Sun, 2014. "Density Forecast Evaluation in Unstable Environments," Working Papers 201428, University of California at Riverside, Department of Economics.
    22. Szabolcs Blazsek & Vicente Mendoza, 2016. "QARMA-Beta- t -EGARCH versus ARMA-GARCH: an application to S&P 500," Applied Economics, Taylor & Francis Journals, vol. 48(12), pages 1119-1129, March.
    23. Shackleton, Mark B. & Taylor, Stephen J. & Yu, Peng, 2010. "A multi-horizon comparison of density forecasts for the S&P 500 using index returns and option prices," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2678-2693, November.
    24. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    25. Barbara Rossi & Tatevik Sekhposyan, 2015. "Alternative Tests for Correct Specification of Conditional Predictive Densities," Working Papers 758, Barcelona School of Economics.
    26. Mackowiak, Bartosz & Jarocinski, Marek, 2013. "Granger-Causal-Priority and Choice of Variables in Vector Autoregressions," CEPR Discussion Papers 9686, Centre for Economic Policy Research.
    27. Kuo‐Hsuan Chin, 2022. "Forecast evaluation of DSGE models: Linear and nonlinear likelihood," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1099-1130, September.
    28. Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2020. "Comparing Forecast Performance with State Dependence," CEPR Discussion Papers 15217, Centre for Economic Policy Research.
    29. Diks, Cees & Panchenko, Valentyn & Sokolinskiy, Oleg & van Dijk, Dick, 2014. "Comparing the accuracy of multivariate density forecasts in selected regions of the copula support," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 79-94.
    30. Alanya-Beltran, Willy, 2022. "Modelling stock returns volatility with dynamic conditional score models and random shifts," Finance Research Letters, Elsevier, vol. 45(C).
    31. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2019. "Forecasting with instabilities: an application to DSGE models with financial frictions," Temi di discussione (Economic working papers) 1234, Bank of Italy, Economic Research and International Relations Area.
    32. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis School of Business and Economics.
    33. Ayala, Astrid & Blazsek, Szabolcs & Escribano, Álvaro, 2017. "Dynamic conditional score models with time-varying location, scale and shape parameters," UC3M Working papers. Economics 25043, Universidad Carlos III de Madrid. Departamento de Economía.
    34. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    35. Christopher McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016. "Assessing the Economic Value of Probabilistic Forecasts in the Presence of an Inflation Target," CAMA Working Papers 2016-40, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    36. Tingting Cheng & Jiti Gao & Xibin Zhang, 2016. "Nonparametric Localized Bandwidth Selection for Kernel Density Estimation," Monash Econometrics and Business Statistics Working Papers 7/16, Monash University, Department of Econometrics and Business Statistics.
    37. Pettenuzzo, Davide & Timmermann, Allan, 2016. "Forecasting Macroeconomic Variables under Model Instability," CEPR Discussion Papers 11355, Centre for Economic Policy Research.
    38. Guglielmo Maria Caporale & Luca Onorante & Paolo Paesani, 2009. "Inflation and Inflation Uncertainty in the Euro Area," CESifo Working Paper Series 2720, CESifo.
    39. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Anchoring the yield curve using survey expectations," CeMMAP working papers CWP52/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    40. Ilya Archakov & Peter Reinhard Hansen & Asger Lunde, 2020. "A Multivariate Realized GARCH Model," Papers 2012.02708, arXiv.org, revised Feb 2025.
    41. Davide Pettenuzzo & Francesco Ravazzolo, 2015. "Optimal Portfolio Choice under Decision-Based Model Combinations," Working Papers No 9/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    42. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    43. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," JRFM, MDPI, vol. 9(3), pages 1-25, July.
    44. Da Fonseca José & Grasselli Martino & Ielpo Florian, 2014. "Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 253-289, May.
    45. Maheu, John M & Yang, Qiao & Song, Yong, 2018. "Oil Price Shocks and Economic Growth: The Volatility Link," MPRA Paper 83779, University Library of Munich, Germany.
    46. Anne Opschoor & André Lucas, 2019. "Time-varying tail behavior for realized kernels," Tinbergen Institute Discussion Papers 19-051/IV, Tinbergen Institute.
    47. Borowska, Agnieszka & Hoogerheide, Lennart & Koopman, Siem Jan & van Dijk, Herman K., 2020. "Partially censored posterior for robust and efficient risk evaluation," Journal of Econometrics, Elsevier, vol. 217(2), pages 335-355.
    48. Alex Tagliabracci, 2020. "Asymmetry in the conditional distribution of euro-area inflation," Temi di discussione (Economic working papers) 1270, Bank of Italy, Economic Research and International Relations Area.
    49. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    50. Ke, Rui & Yin, Man & Jia, Jing, 2026. "Forecasting intraday risk incorporating the higher-order moments," The Quarterly Review of Economics and Finance, Elsevier, vol. 106(C).
    51. Ramon de Punder & Timo Dimitriadis & Rutger-Jan Lange, 2024. "Expected Kullback-Leibler-based characterizations of score-driven updates," Papers 2408.02391, arXiv.org, revised Mar 2026.
    52. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    53. Rossi, Barbara & Sekhposyan, Tatevik, 2013. "Conditional predictive density evaluation in the presence of instabilities," Journal of Econometrics, Elsevier, vol. 177(2), pages 199-212.
    54. Buncic, Daniel, 2009. "Understanding forecast failure in ESTAR models of real exchange rates," MPRA Paper 13121, University Library of Munich, Germany.
    55. Joshua C.C. Chan, 2015. "The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling," CAMA Working Papers 2015-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    56. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an Uncertain Economic Environment," Tinbergen Institute Discussion Papers 14-152/III, Tinbergen Institute.
    57. Mathieu Gatumel & Florian Ielpo, 2011. "The Number of Regimes Across Asset Returns: Identification and Economic Value," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00658540, HAL.
    58. Gian Piero Aielli, 2011. "Dynamic Conditional Correlation: On properties and estimation," "Marco Fanno" Working Papers 0142, Dipartimento di Scienze Economiche "Marco Fanno".
    59. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2019. "Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods," Journal of Asian Economics, Elsevier, vol. 60(C), pages 45-68.
    60. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    61. Laura Liu, 2018. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," Papers 1805.04178, arXiv.org, revised Oct 2021.
    62. Randal J. Verbrugge & Saeed Zaman, 2022. "Improving Inflation Forecasts Using Robust Measures," Working Papers 22-23R, Federal Reserve Bank of Cleveland, revised 30 May 2023.
    63. Francesco Ravazzolo & Shaun P. Vahey, 2010. "Forecast densities for economic aggregates from disaggregate ensembles," Working Paper 2010/02, Norges Bank.
    64. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    65. Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018. "A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile," International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
    66. Algieri, Bernardina & Leccadito, Arturo & Tunaru, Diana, 2021. "Risk premia in electricity derivatives markets," Energy Economics, Elsevier, vol. 100(C).
    67. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
    68. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    69. Del Brio, Esther B. & Ñíguez, Trino-Manuel & Perote, Javier, 2011. "Multivariate semi-nonparametric distributions with dynamic conditional correlations," International Journal of Forecasting, Elsevier, vol. 27(2), pages 347-364, April.
    70. Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.
    71. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," Working Papers 20-27, Federal Reserve Bank of Cleveland.
    72. Szabolcs Blazsek & Hector Hernández, 2018. "Analysis of electricity prices for Central American countries using dynamic conditional score models," Empirical Economics, Springer, vol. 55(4), pages 1807-1848, December.
    73. Florens Odendahl & Barbara Rossi & Tatevik Sekhposyan, 2021. "Evaluating Forecast Performance with State Dependence," Working Papers 1295, Barcelona School of Economics.
    74. Marco J. Lombardi & Francesco Ravazzolo, 2012. "Oil price density forecasts: exploring the linkages with stock markets," Working Paper 2012/24, Norges Bank.
    75. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    76. Giacomini, Raffaella & Ragusa, Giuseppe, 2011. "Incorporating theoretical restrictions into forecasting by projection methods," CEPR Discussion Papers 8604, Centre for Economic Policy Research.
    77. Matthieu Garcin & Clément Goulet, 2017. "Non-parametric news impact curve: a variational approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01244292, HAL.
    78. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010. "Option pricing for GARCH-type models with generalized hyperbolic innovations," Post-Print halshs-00469529, HAL.
    79. Matei Demetrescu & Mu-Chun Wang, 2014. "Incorporating Asymmetric Preferences into Fan Charts and Path Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 287-297, April.
    80. Onno Kleen, 2024. "Scaling and measurement error sensitivity of scoring rules for distribution forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 833-849, August.
    81. Čapek, Jan & Crespo Cuaresma, Jesús & Hauzenberger, Niko & Reichel, Vlastimil, 2023. "Macroeconomic forecasting in the euro area using predictive combinations of DSGE models," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1820-1838.
    82. Chiara Casoli & Riccardo Lucchetti, 2026. "A rotated Dynamic Factor Model for the yield curve: squeezing out information when it matters," Working Papers 2026.03, Fondazione Eni Enrico Mattei.
    83. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    84. Barbara Rossi & Tatevik Sekhposyan, 2013. "Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set," Economics Working Papers 1370, Department of Economics and Business, Universitat Pompeu Fabra.
    85. Marcin Kolasa & Michal Rubaszek, 2015. "How Frequently Should We Reestimate DSGE Models?," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 279-305, December.
    86. Hilde C. Bjørnland & Francesco Ravazzolo & Leif Anders Thorsrud, 2015. "Forecasting GDP with global components. This time is different," Working Papers No 1/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    87. Alanya-Beltran Willy, 2023. "Modelling volatility dependence with score copula models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(5), pages 649-668, December.
    88. Paulo Mauricio S�nchez Beltr�n & Luis Fernando Melo Velandia, 2013. "Combinaci�n de brechas del producto colombiano," Borradores de Economia 10973, Banco de la Republica.
    89. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org, revised Dec 2022.
    90. Ricardo Crisostomo & Lorena Couso, 2018. "Financial density forecasts: A comprehensive comparison of risk-neutral and historical schemes," Papers 1801.08007, arXiv.org, revised May 2018.
    91. Amisano, Gianni & Fagan, Gabriel, 2013. "Money growth and inflation: A regime switching approach," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 118-145.
    92. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    93. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-172/4, Tinbergen Institute.
    94. Angela Capolongo & Claudia Pacella, 2019. "Forecasting inflation in the euro area: countries matter!," Temi di discussione (Economic working papers) 1224, Bank of Italy, Economic Research and International Relations Area.
    95. Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CIRJE F-Series CIRJE-F-742, CIRJE, Faculty of Economics, University of Tokyo.
    96. Matthew C. Johnson & Matteo Luciani & Minzhengxiong Zhang & Kenichiro McAlinn, 2026. "Predictive Synthesis under Sporadic Participation: Evidence from Inflation Density Surveys," Papers 2602.05226, arXiv.org.
    97. Niango Ange Joseph Yapi, 2020. "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers 2020-16, University of Paris Nanterre, EconomiX.
    98. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    99. Philippe de Peretti & Oren Tapiero, 2014. "A GARCH analysis of dark-pool trades," Post-Print hal-00984834, HAL.
    100. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    101. Chiang, I-Hsuan Ethan & Liao, Yin & Zhou, Qing, 2021. "Modeling the cross-section of stock returns using sensible models in a model pool," Journal of Empirical Finance, Elsevier, vol. 60(C), pages 56-73.
    102. Ellis W. Tallman & Saeed Zaman, 2026. "A New Model of Trend Inflation Using Disaggregates, Survey Expectations, and Uncertainty," Working Papers 26-08r, Federal Reserve Bank of Cleveland, revised 02 Nov 2026.
    103. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    104. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    105. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    106. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Common drifting volatility in large Bayesian VARs," Working Papers (Old Series) 1206, Federal Reserve Bank of Cleveland.
    107. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2024. "Nowcasting Norwegian household consumption with debit card transaction data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1220-1244, November.
    108. Cheng, Tingting & Gao, Jiti & Phillips, Peter C.B., 2018. "A frequentist approach to Bayesian asymptotics," Journal of Econometrics, Elsevier, vol. 206(2), pages 359-378.
    109. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    110. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.
    111. Cristina Conflitti & Christine De Mol & Domenico Giannone, 2012. "Optimal Combination of Survey Forecasts," Working Papers ECARES ECARES 2012-023, ULB -- Universite Libre de Bruxelles.
    112. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Papers No 01/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    113. Ioannis Anagnostou & Drona Kandhai, 2019. "Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model," Risks, MDPI, vol. 7(2), pages 1-22, June.
    114. Zhang, Feipeng & Gao, Hongfu & Yuan, Di, 2024. "The asymmetric effect of G7 stock market volatility on predicting oil price volatility: Evidence from quantile autoregression model," Journal of Commodity Markets, Elsevier, vol. 35(C).
    115. Michael S. O’Doherty & N. E. Savin & Ashish Tiwari, 2016. "Evaluating Hedge Funds with Pooled Benchmarks," Management Science, INFORMS, vol. 62(1), pages 69-89, January.
    116. Niko Hauzenberger & Florian Huber & Luca Onorante, 2021. "Combining shrinkage and sparsity in conjugate vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 304-327, April.
    117. Georgios Anastasiades & Patrick McSharry, 2013. "Quantile Forecasting of Wind Power Using Variability Indices," Energies, MDPI, vol. 6(2), pages 1-34, February.
    118. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
    119. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2018. "Does a financial accelerator improve forecasts during financial crises?: Evidence from Japan with Prediction Pool Methods," MPRA Paper 85523, University Library of Munich, Germany.
    120. Jie Cheng, 2024. "Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3617-3643, December.
    121. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    122. Emilio Zanetti Chini, 2017. "Generalizing Smooth Transition Autoregressions," DEM Working Papers Series 138, University of Pavia, Department of Economics and Management.
    123. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.
    124. David Harris & Gael M. Martin & Indeewara Perera & Don S. Poskitt, 2017. "Construction and visualization of optimal confidence sets for frequentist distributional forecasts," Monash Econometrics and Business Statistics Working Papers 9/17, Monash University, Department of Econometrics and Business Statistics.
    125. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    126. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial conditions and density forecasts for US output and inflation," CReMFi Discussion Papers 1, CReMFi, School of Economics and Finance, QMUL.
    127. Ta-Chung Chi & Ting-Han Fan & Raffaele M. Ghigliazza & Domenico Giannone & Zixuan & Wang, 2025. "Macroeconomic Forecasting and Machine Learning," Papers 2510.11008, arXiv.org.
    128. C. Alexander & M. Coulon & Y. Han & X. Meng, 2024. "Evaluating the discrimination ability of proper multi-variate scoring rules," Annals of Operations Research, Springer, vol. 334(1), pages 857-883, March.
    129. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    130. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014. "Testing for Leverage Effects in the Returns of US Equities," Documents de travail du Centre d'Economie de la Sorbonne 14022r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jan 2017.
    131. Dovern, Jonas & Feldkircher, Martin & Huber, Florian, 2016. "Does joint modelling of the world economy pay off? Evaluating global forecasts from a Bayesian GVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 70(C), pages 86-100.
    132. Krystian Jaworski, 2019. "Sentiment-induced regime switching in density forecasts of emerging markets’ exchange rates. Calibrated simulation trumps estimated autoregression," Bank i Kredyt, Narodowy Bank Polski, vol. 50(1), pages 83-106.
    133. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    134. Michael B. Gordy & Pawel J. Szerszen, 2015. "Bayesian Estimation of Time-Changed Default Intensity Models," Finance and Economics Discussion Series 2015-2, Board of Governors of the Federal Reserve System (U.S.).
    135. Szabolcs Blazsek & Luis Antonio Monteros, 2017. "Dynamic conditional score models of degrees of freedom: filtering with score-driven heavy tails," Applied Economics, Taylor & Francis Journals, vol. 49(53), pages 5426-5440, November.
    136. Manzan, Sebastiano & Zerom, Dawit, 2009. "Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?," MPRA Paper 14387, University Library of Munich, Germany.
    137. Daan Schoemaker & André Lucas & Anne Opschoor, 2025. "Conditional Fat Tails and Scale Dynamics for Intraday Discrete Price Changes," Tinbergen Institute Discussion Papers 25-039/III, Tinbergen Institute.
    138. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, Department of Economics and Business Economics, Aarhus University.
    139. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-003/4, Tinbergen Institute.
    140. Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020. "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series 2501, European Central Bank.
    141. Wolden Bache, Ida & Sofie Jore, Anne & Mitchell, James & Vahey, Shaun P., 2011. "Combining VAR and DSGE forecast densities," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1659-1670, October.
    142. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    143. Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca, 2022. "Forecasting in GARCH models with polynomially modified innovations," International Journal of Forecasting, Elsevier, vol. 38(1), pages 117-141.
    144. Fernando Eguren-Martin & Sevim Kösem & Guido Maia & Andrej Sokol, 2024. "Targeted financial conditions indices and growth-at-risk," Bank of England Staff Working Paper series 1084, Bank of England.
    145. Koop, Gary & Onorante, Luca, 2011. "Estimating Phillips Curves in Turbulent Times using the ECB’s Survey of Professional Forecasters," SIRE Discussion Papers 2011-19, Scottish Institute for Research in Economics (SIRE).
    146. Elena Andreou & Andros Kourtellos, 2018. "Scoring rules for simple forecasting models: The case of Cyprus GDP and its sectors," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(1), pages 59-73, June.
    147. Garratt, Anthony & Mise, Emi, 2014. "Forecasting exchange rates using panel model and model averaging," Economic Modelling, Elsevier, vol. 37(C), pages 32-40.
    148. Chin, Kuo-Hsuan & Li, Xue, 2019. "Bayesian forecast combination in VAR-DSGE models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 278-298.
    149. Paulo Mauricio Sánchez Beltrán & Luis Fernando Melo Velandia, 2013. "Combinación de brechas del producto colombiano," Borradores de Economia 775, Banco de la Republica de Colombia.
    150. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: a multi-country BVAR benchmark for the Eurosystem projections," Working Paper Series 2227, European Central Bank.
    151. Nicholas Fawcett & George Kapetanios & James Mitchell & Simon Price, 2014. "Generalised Density Forecast Combinations," CAMA Working Papers 2014-24, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    152. Gian Piero Aielli & Massimiliano Caporin, 2015. "Dynamic Principal Components: a New Class of Multivariate GARCH Models," "Marco Fanno" Working Papers 0193, Dipartimento di Scienze Economiche "Marco Fanno".
    153. Cenesizoglu, Tolga & Timmermann, Allan, 2012. "Do return prediction models add economic value?," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2974-2987.
    154. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    155. Kenichiro McAlinn & Kosaku Takanashi, 2019. "Mean-shift least squares model averaging," Papers 1912.01194, arXiv.org.
    156. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    157. Mathieu Gatumel & Florian Ielpo, 2014. "Commodity Markets through the business cycle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01302479, HAL.
    158. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1596-1609, September.
    159. Knut Are Aastveit & Jamie Cross & Herman K. van Dijk, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Tinbergen Institute Discussion Papers 21-053/III, Tinbergen Institute.
    160. Hoogerheide, Lennart F. & Ardia, David & Corré, Nienke, 2012. "Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?," Economics Letters, Elsevier, vol. 116(3), pages 322-325.
    161. Tommaso Proietti & Martyna Marczak & Gianluigi Mazzi, 2015. "EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area," CEIS Research Paper 340, Tor Vergata University, CEIS, revised 10 Apr 2015.
    162. Janine Balter & Alexander J. McNeil, 2024. "Multivariate Spectral Backtests of Forecast Distributions under Unknown Dependencies," Risks, MDPI, vol. 12(1), pages 1-15, January.
    163. Luiz Renato Regis de Oliveira Lima & Wagner Piazza Gaglianone, 2012. "Constructing Optimal Density Forecasts from Point Forecast Combinations," Série Textos para Discussão (Working Papers) 5, Programa de Pós-Graduação em Economia - PPGE, Universidade Federal da Paraíba.
    164. Emilio Zanetti Chini, 2018. "Forecasters’ utility and forecast coherence," CREATES Research Papers 2018-23, Department of Economics and Business Economics, Aarhus University.
    165. Carlo Altavilla & Riccardo Costantini & Raffaella Giacomini, 2013. "Bond returns and market expectations," CeMMAP working papers 20/13, Institute for Fiscal Studies.
    166. Geir D. Berentsen & Jan Bulla & Antonello Maruotti & Bård Støve, 2022. "Modelling clusters of corporate defaults: Regime‐switching models significantly reduce the contagion source," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 698-722, June.
    167. Hallam, Mark & Olmo, Jose, 2014. "Forecasting daily return densities from intraday data: A multifractal approach," International Journal of Forecasting, Elsevier, vol. 30(4), pages 863-881.
    168. Peter McAdam & Anders Warne, 2024. "Density forecast combinations: The real‐time dimension," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1153-1172, August.
    169. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.
    170. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    171. Caporin, Massimiliano & McAleer, Michael, 2014. "Robust ranking of multivariate GARCH models by problem dimension," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 172-185.
    172. M. Mogliani & Thomas Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    173. Conlon, Thomas & Cotter, John & Kovalenko, Illia & Post, Thierry, 2023. "A financial modeling approach to industry exchange-traded funds selection," Journal of Empirical Finance, Elsevier, vol. 74(C).
    174. Diego Chicana & Rafael Nivin, 2021. "Evaluating Growth-at-Risk as a tool for monitoring macro-financial risks in the Peruvian economy," IHEID Working Papers 07-2021, Economics Section, The Graduate Institute of International Studies.
    175. Marcin Kolasa & Michał Rubaszek, 2018. "Does the foreign sector help forecast domestic variables in DSGE models?," NBP Working Papers 282, Narodowy Bank Polski.
    176. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    177. Aslanidis, Nektarios & Martinez, Oscar, 2021. "Correlation regimes in international equity and bond returns," Economic Modelling, Elsevier, vol. 97(C), pages 397-410.
    178. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2012. "Multivariate Rotated ARCH Models," Economics Papers 2012-W01, Economics Group, Nuffield College, University of Oxford.
    179. Michael B. Gordy & Alexander J. McNeil, 2017. "Spectral backtests of forecast distributions with application to risk management," Papers 1708.01489, arXiv.org, revised Jul 2019.
    180. Emilio Zanetti Chini, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," CREATES Research Papers 2018-13, Department of Economics and Business Economics, Aarhus University.
    181. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    182. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    183. Warne, Anders, 2023. "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series 2768, European Central Bank.
    184. Yan, Cheng & Cheng, Tingting, 2019. "In search of the optimal number of fund subgroups," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 78-92.
    185. Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    186. Jaqueline Terra Moura Marins, 2024. "Predictability of Exchange Rate Density Forecasts for Emerging Economies in the Short Run," Working Papers Series 588, Central Bank of Brazil, Research Department.
    187. Kolasa, Marcin & Rubaszek, Michał, 2014. "Forecasting with DSGE models with financial frictions," Dynare Working Papers 40, CEPREMAP.
    188. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Documents de travail du Centre d'Economie de la Sorbonne 17006, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    189. Cees Diks & Valentyn Panchenko & Oleg Sokolinskiy, & Dick van Dijk, 2013. "Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support," Tinbergen Institute Discussion Papers 13-061/III, Tinbergen Institute.
    190. Ali Dib & Mohamed Gammoudi & Kevin Moran, 2008. "Forecasting Canadian time series with the New Keynesian model," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 138-165, February.
    191. Matthieu Garcin & Clément Goulet, 2015. "A fully non-parametric heteroskedastic model," Documents de travail du Centre d'Economie de la Sorbonne 15086, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    192. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    193. Todd E. Clark & Taeyoung Doh, 2011. "A Bayesian evaluation of alternative models of trend inflation," Working Papers (Old Series) 1134, Federal Reserve Bank of Cleveland.
    194. Wagner Piazza Gaglianone & Jaqueline Terra Moura Marins, 2014. "Risk Assessment of the Brazilian FX Rate," Working Papers Series 344, Central Bank of Brazil, Research Department.
    195. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2012. "Prior selection for vector autoregressions," Working Paper Series 1494, European Central Bank.
    196. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    197. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
    198. Foltas, Alexander & Pierdzioch, Christian, 2020. "On the efficiency of German growth forecasts: An empirical analysis using quantile random forests," Working Papers 21, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    199. Philippe de Peretti & Oren Tapiero, 2014. "A GARCH analysis of dark-pool trades," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00984834, HAL.
    200. Dovern, Jonas & Feldkircher, Martin & Huber, Florian, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Multivariate Forecasts from a Bayesian GVAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112999, Verein für Socialpolitik / German Economic Association.
    201. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
    202. Andres Trujillo-Barrera & Philip Garcia & Mindy L Mallory, 2018. "Short-term price density forecasts in the lean hog futures market," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(1), pages 121-142.
    203. Zdeněk Zmeškal & Dana Dluhošová & Karolina Lisztwanová & Antonín Pončík & Iveta Ratmanová, 2023. "Distribution Prediction of Decomposed Relative EVA Measure with Levy-Driven Mean-Reversion Processes: The Case of an Automotive Sector of a Small Open Economy," Forecasting, MDPI, vol. 5(2), pages 1-19, May.
    204. Sebastiano Manzan, 2015. "Forecasting the Distribution of Economic Variables in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 144-164, January.
    205. Valentina Aprigliano & Simone Emiliozzi & Gabriele Guaitoli & Andrea Luciani & Juri Marcucci & Libero Monteforte, 2021. "The power of text-based indicators in forecasting the Italian economic activity," Temi di discussione (Economic working papers) 1321, Bank of Italy, Economic Research and International Relations Area.
    206. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003; see Bao et al. (2004)," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.
    207. Matthieu Garcin & Clément Goulet, 2017. "Non-parametric news impact curve: a variational approach," Post-Print halshs-01244292, HAL.
    208. Mr. Ananthakrishnan Prasad & Mr. Selim A Elekdag & Mr. Phakawa Jeasakul & Romain Lafarguette & Mr. Adrian Alter & Alan Xiaochen Feng & Changchun Wang, 2019. "Growth at Risk: Concept and Application in IMF Country Surveillance," IMF Working Papers 2019/036, International Monetary Fund.
    209. Anne Opschoor & Dewi Peerlings & Luca Rossini & Andre Lucas, 2024. "Density Forecasting for Electricity Prices under Tail Heterogeneity with the t-Riesz Distribution," Tinbergen Institute Discussion Papers 24-049/III, Tinbergen Institute.
    210. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    211. Kyungchul Song, 2011. "Testing Predictive Ability and Power Robustification," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 288-296, October.
    212. Rehab OSMAN, 2010. "SADC EPAs with the EU: the Right or a Blight Way for Development," EcoMod2010 259600127, EcoMod.
    213. Fabio Busetti, 2017. "Quantile Aggregation of Density Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 495-512, August.
    214. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    215. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    216. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
    217. Anthony Garratt & James Mitchell & Shaun P. Vahey, 2009. "Measuring output gap uncertainty," Reserve Bank of New Zealand Discussion Paper Series DP2009/15, Reserve Bank of New Zealand.
    218. Liu, Yi & Liu, Huifang & Zhang, Lei, 2019. "Modeling and forecasting return jumps using realized variation measures," Economic Modelling, Elsevier, vol. 76(C), pages 63-80.
    219. Jie Cheng, 2023. "Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies," Empirical Economics, Springer, vol. 65(2), pages 899-924, August.
    220. Yves Dominicy & Harry-Paul Vander Elst, 2015. "Macro-Driven VaR Forecasts: From Very High to Very Low Frequency Data," Working Papers ECARES ECARES 2015-41, ULB -- Universite Libre de Bruxelles.
    221. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
    222. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    223. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
    224. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    225. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    226. Gelfer, Sacha, 2024. "Examining business cycles and optimal monetary policy in a regional DSGE model," Economic Modelling, Elsevier, vol. 136(C).
    227. Ilmari Juutilainen & Juha Roning, 2010. "How to compare interpretatively different models for the conditional variance function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 983-997.
    228. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2012. "Option Pricing for GARCH-type Models with Generalized Hyperbolic Innovations," PSE-Ecole d'économie de Paris (Postprint) hal-00511965, HAL.
    229. Anthony Garratt & Ivan Petrella, 2022. "Commodity prices and inflation risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 392-414, March.
    230. Lukasz Gatarek & Lennart Hoogerheide & Koen Hooning & Herman K. van Dijk, 2013. "Censored Posterior and Predictive Likelihood in Left-Tail Prediction for Accurate Value at Risk Estimation," Tinbergen Institute Discussion Papers 13-060/III, Tinbergen Institute, revised 06 Mar 2014.
    231. Agosto, Arianna & Cavaliere, Giuseppe & Kristensen, Dennis & Rahbek, Anders, 2016. "Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 640-663.
    232. Diks, Cees & Fang, Hao, 2020. "Comparing density forecasts in a risk management context," International Journal of Forecasting, Elsevier, vol. 36(2), pages 531-551.
    233. Ouysse, Rachida, 2016. "Bayesian model averaging and principal component regression forecasts in a data rich environment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 763-787.
    234. Tingting Cheng & Jiti Gao & Peter CB Phillips, 2016. "A Frequency Approach to Bayesian Asymptotics," Monash Econometrics and Business Statistics Working Papers 5/16, Monash University, Department of Econometrics and Business Statistics.
    235. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    236. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    237. Anne Opschoor & André Lucas & Istvan Barra & Dick van Dijk, 2019. "Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings," Tinbergen Institute Discussion Papers 19-013/IV, Tinbergen Institute, revised 23 Oct 2019.
    238. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    239. Yoshibumi Makabe & Yoshihiko Norimasa, 2022. "The Term Structure of Inflation at Risk: A Panel Quantile Regression Approach," Bank of Japan Working Paper Series 22-E-4, Bank of Japan.
    240. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    241. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    242. Alexander Tsyplakov, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 115-132.
    243. Brownlees, Christian & Souza, André B.M., 2021. "Backtesting global Growth-at-Risk," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 312-330.
    244. Trujillo-Barrera, Andres Trujillo- & Garcia, Philip, 2012. "Density Forecasts of Lean Hog Futures Price," 2012 Conference, April 16-17, 2012, St. Louis, Missouri 285767, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    245. Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Theory-coherent forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 145-155.
    246. Alessandra Canepa, & Karanasos, Menelaos & Paraskevopoulos, Athanasios & Chini, Emilio Zanetti, 2022. "Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202212, University of Turin.
    247. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    248. Valentina Aprigliano, 2020. "A large Bayesian VAR with a block‐specific shrinkage: A forecasting application for Italian industrial production," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1291-1304, December.
    249. Kaeck, Andreas & Rodrigues, Paulo & Seeger, Norman J., 2018. "Model Complexity and Out-of-Sample Performance: Evidence from S&P 500 Index Returns," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 1-29.
    250. Nesvold, Erik & Bratvold, Reidar B., 2022. "Debiasing probabilistic oil production forecasts," Energy, Elsevier, vol. 258(C).
    251. Francisco Blasques & Vladimir Holy & Petra Tomanova, 2019. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Tinbergen Institute Discussion Papers 19-004/III, Tinbergen Institute.
    252. Caraiani, Petre, 2016. "The role of money in DSGE models: a forecasting perspective," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 315-330.
    253. Anthony Garratt & James Mitchell & Shaun P. Vahey & Elizabeth C. Wakerly, 2009. "Real-time Inflation Forecast Densities from Ensemble Phillips Curves," Birkbeck Working Papers in Economics and Finance 0910, Birkbeck, Department of Economics, Mathematics & Statistics.
    254. León, Ángel & Ñíguez, Trino-Manuel, 2021. "The transformed Gram Charlier distribution: Parametric properties and financial risk applications," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 323-349.
    255. McAdam, Peter & Warne, Anders, 2019. "Euro area real-time density forecasting with financial or labor market frictions," International Journal of Forecasting, Elsevier, vol. 35(2), pages 580-600.
    256. Onorante, Luca & Koop, Gary, 2012. "Estimating Phillips curves in turbulent times using the ECB's survey of professional forecasters," Working Paper Series 1422, European Central Bank.
    257. McAlinn, Kenichiro & West, Mike, 2019. "Dynamic Bayesian predictive synthesis in time series forecasting," Journal of Econometrics, Elsevier, vol. 210(1), pages 155-169.
    258. Laura Liu & Yulong Wang, 2025. "Binary Outcome Models with Extreme Covariates: Estimation and Prediction," Papers 2502.16041, arXiv.org.
    259. Domit, Sílvia & Monti, Francesca & Sokol, Andrej, 2019. "Forecasting the UK economy with a medium-scale Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1669-1678.
    260. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    261. Sebastiano Manzan & Dawit Zerom, 2015. "Asymmetric Quantile Persistence and Predictability: the Case of US Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(2), pages 297-318, April.
    262. Tingting Cheng & Jiti Gao & Xibin Zhang, 2014. "Semiparametric Localized Bandwidth Selection in Kernel Density Estimation," Monash Econometrics and Business Statistics Working Papers 14/14, Monash University, Department of Econometrics and Business Statistics.
    263. Fabian Krüger & Sebastian Lerch & Thordis Thorarinsdottir & Tilmann Gneiting, 2021. "Predictive Inference Based on Markov Chain Monte Carlo Output," International Statistical Review, International Statistical Institute, vol. 89(2), pages 274-301, August.
    264. Rui Fan & Stephen J. Taylor & Matteo Sandri, 2018. "Density forecast comparisons for stock prices, obtained from high‐frequency returns and daily option prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(1), pages 83-103, January.
    265. Jonas E. Arias & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models," Working Papers 21-21, Federal Reserve Bank of Philadelphia.
    266. Liu, Xiaochun, 2011. "Modeling the time-varying skewness via decomposition for out-of-sample forecast," MPRA Paper 41248, University Library of Munich, Germany.
    267. Łukasz Lenart, 2017. "Examination of Seasonal Volatility in HICP for Baltic Region Countries: Non-Parametric Test versus Forecasting Experiment," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 9(1), pages 29-67, March.
    268. Gianni Amisano & Roberta Colavecchio, 2013. "Money Growth and Inflation: evidence from a Markov Switching Bayesian VAR," Macroeconomics and Finance Series 201304, University of Hamburg, Department of Socioeconomics.
    269. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
    270. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2011. "Scoring rules and survey density forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 379-393, April.
    271. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2022. "Common factors and the dynamics of cereal prices. A forecasting perspective," Journal of Commodity Markets, Elsevier, vol. 28(C).
    272. Francesco Ravazzolo & Philip Rothman, 2010. "Oil and US GDP: A real-time out-of-sample examination," Working Paper 2010/18, Norges Bank.
    273. Szabolcs Blazsek & Han-Chiang Ho, 2017. "Markov regime-switching Beta--EGARCH," Applied Economics, Taylor & Francis Journals, vol. 49(47), pages 4793-4805, October.
    274. Francesco Ravazzolo & Shaun P Vahey, 2010. "Measuring Core Inflation in Australia with Disaggregate Ensembles," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    275. Kyungchul Song, 2009. "Testing Predictive Ability and Power Robustification," PIER Working Paper Archive 09-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    276. Ida Wolden Bache & James Mitchell & Francesco Ravazzolo & Shaun P. Vahey, 2009. "Macro modelling with many models," Working Paper 2009/15, Norges Bank.
    277. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    278. Andre Lucas & Anne Opschoor & Luca Rossini, 2021. "Tail Heterogeneity for Dynamic Covariance Matrices: the F-Riesz Distribution," Tinbergen Institute Discussion Papers 21-010/III, Tinbergen Institute, revised 11 Jul 2023.
    279. Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
    280. Mathieu Gatumel & Florian Ielpo, 2011. "The Number of Regimes Across Asset Returns: Identification and Economic Value," Post-Print halshs-00658540, HAL.
    281. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
    282. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    283. Tingting Cheng & Jiti Gao & Xibin Zhang, 2014. "Semiparametric Localized Bandwidth Selection for Kernel Density Estimation," Monash Econometrics and Business Statistics Working Papers 27/14, Monash University, Department of Econometrics and Business Statistics.
    284. Ñíguez, Trino-Manuel & Perote, Javier, 2016. "Multivariate moments expansion density: Application of the dynamic equicorrelation model," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 216-232.
    285. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Papers No 3/2014, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    286. Matteo Malavasi & Gareth W. Peters & Stefan Treuck & Pavel V. Shevchenko & Jiwook Jang & Georgy Sofronov, 2024. "Cyber Risk Taxonomies: Statistical Analysis of Cybersecurity Risk Classifications," Papers 2410.05297, arXiv.org.
    287. Malavasi, Matteo & Peters, Gareth W. & Trück, Stefan & Shevchenko, Pavel V. & Jang, Jiwook & Sofronov, Georgy, 2026. "Cyber risk taxonomies: statistical analysis of cybersecurity risk classifications," Insurance: Mathematics and Economics, Elsevier, vol. 126(C).
    288. Alexandridis, Antonios K. & Apergis, Iraklis & Panopoulou, Ekaterini & Voukelatos, Nikolaos, 2023. "Equity premium prediction: The role of information from the options market," Journal of Financial Markets, Elsevier, vol. 64(C).
    289. Clements, Michael P., 2018. "Are macroeconomic density forecasts informative?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 181-198.
    290. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010. "Likelihood-Related Estimation Methods and Non-Gaussian GARCH Processes," Post-Print halshs-00523371, HAL.
    291. Sílvia Domit & Francesca Monti & Andrej Sokol, 2016. "A Bayesian VAR benchmark for COMPASS," Bank of England Staff Working Paper series 583, Bank of England.
    292. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial indicators and density forecasts for US output and inflation," Temi di discussione (Economic working papers) 977, Bank of Italy, Economic Research and International Relations Area.
    293. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    294. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    295. Damian Stelmasiak & Grzegorz Szafrański, 2016. "Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 21-42, March.
    296. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    297. Aielli, Gian Piero & Caporin, Massimiliano, 2014. "Variance clustering improved dynamic conditional correlation MGARCH estimators," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 556-576.
    298. Marco Realdon, 2025. "Affine term structure models with Garch volatility," Risk Management, Palgrave Macmillan, vol. 27(4), pages 1-33, December.
    299. Tae-Hwy Lee & He Wang & Zhou Xi & Ru Zhang, 2021. "Density Forecast of Financial Returns Using Decomposition and Maximum Entropy," Working Papers 202115, University of California at Riverside, Department of Economics.
    300. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    301. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.
    302. Tingting Cheng & Jiti Gao & Peter CB Phillips, 2017. "Bayesian estimation based on summary statistics: Double asymptotics and practice," Monash Econometrics and Business Statistics Working Papers 4/17, Monash University, Department of Econometrics and Business Statistics.
    303. Michael W. McCracken, 2019. "Diverging Tests of Equal Predictive Ability," Working Papers 2019-018, Federal Reserve Bank of St. Louis, revised 09 Mar 2020.
    304. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2017. "Testing for Leverage Effects in the Returns of US Equities," Post-Print halshs-00973922, HAL.
    305. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014. "Testing for Leverage Effect in Financial Returns," Documents de travail du Centre d'Economie de la Sorbonne 14022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    306. K=osaku Takanashi & Kenichiro McAlinn, 2019. "Equivariant online predictions of non-stationary time series," Papers 1911.08662, arXiv.org, revised Jun 2023.
    307. Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.
    308. Mathieu Gatumel & Florian Ielpo, 2014. "Commodity Markets through the business cycle," Post-Print hal-01302479, HAL.
    309. Rubaszek, Michał, 2021. "Forecasting crude oil prices with DSGE models," International Journal of Forecasting, Elsevier, vol. 37(2), pages 531-546.
    310. Huurman, Christian & Ravazzolo, Francesco & Zhou, Chen, 2012. "The power of weather," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3793-3807.
    311. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2012. "Option Pricing for GARCH-type Models with Generalized Hyperbolic Innovations," Post-Print hal-00511965, HAL.
    312. Rompolis, Leonidas S., 2010. "Retrieving risk neutral densities from European option prices based on the principle of maximum entropy," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 918-937, December.
    313. Opschoor, Anne & Lucas, André, 2023. "Time-varying variance and skewness in realized volatility measures," International Journal of Forecasting, Elsevier, vol. 39(2), pages 827-840.
    314. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    315. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
    316. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

  25. Rossi, Barbara & Giacomini, Raffaella, 2005. "How Stable is the Forecasting Performance of the Yield Curve for Outpot Growth?," Working Papers 05-08, Duke University, Department of Economics.

    Cited by:

    1. Pesaran, M.H. & Pick, A. & Pranovich, M., 2011. "Optimal Forecasts in the Presence of Structural Breaks (Updated 14 November 2011)," Cambridge Working Papers in Economics 1163, Faculty of Economics, University of Cambridge.
    2. Gross, Marco, 2011. "Corporate bond spreads and real activity in the euro area - Least Angle Regression forecasting and the probability of the recession," Working Paper Series 1286, European Central Bank.
    3. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    4. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    5. Petri Kuosmanen & Juuso Vataja, 2026. "Global Insights Into Term Spreads: Unveiling Their Predictive Power During Unconventional Monetary Policy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(5), pages 2145-2159, August.
    6. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    7. Hännikäinen, Jari, 2017. "When does the yield curve contain predictive power? Evidence from a data-rich environment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1044-1064.
    8. Pierre Perron & Yohei Yamamoto, 2008. "On the Usefulness or Lack Thereof of Optimality Criteria for Structural Change Tests," Boston University - Department of Economics - Working Papers Series wp2008-006, Boston University - Department of Economics.
    9. Leo Krippner & Leif Anders Thorsrud, 2009. "Forecasting New Zealand's economic growth using yield curve information," Reserve Bank of New Zealand Discussion Paper Series DP2009/18, Reserve Bank of New Zealand.
    10. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    11. Zolotoy, Leon & Frederickson, James R. & Lyon, John D., 2017. "Aggregate earnings and stock market returns: The good, the bad, and the state-dependent," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 157-175.
    12. Klinkowska, Olga & Zadorozhna, Olha, 2026. "The yield curve strikes back: New evidence of its predictive power for economic activity and inflation," International Review of Financial Analysis, Elsevier, vol. 113(C).
    13. Kajal Lahiri & Cheng Yang, 2022. "ROC approach to forecasting recessions using daily yield spreads," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 57(4), pages 191-203, October.
    14. Argyropoulos Efthymios & Tzavalis Elias, 2015. "Term spread regressions of the rational expectations hypothesis of the term structure allowing for risk premium effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 49-70, February.
    15. Zagaglia, Paolo, 2006. "Does the Yield Spread Predict the Output Gap in the U.S.?," Research Papers in Economics 2006:5, Stockholm University, Department of Economics.
    16. Knut Lehre Seip & Dan Zhang, 2021. "The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    17. Lorenzo Boldrini & Eric Hillebrand, 2015. "The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach," CREATES Research Papers 2015-39, Department of Economics and Business Economics, Aarhus University.
    18. Stan Hurn & Peter C B Phillips & Shuping Shi, 2015. "Change Detection and the Casual Impact of the Yield Curve," NCER Working Paper Series 107, National Centre for Econometric Research.
    19. Zongwu Cai & Gunawan, 2023. "A Combination Forecast for Nonparametric Models with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202310, University of Kansas, Department of Economics, revised Sep 2023.
    20. Marcelle Chauvet & Zeynep Senyuz, 2012. "A Dynamic Factor Model of the Yield Curve as a Predictor of the Economy," Finance and Economics Discussion Series 2012-32, Board of Governors of the Federal Reserve System (U.S.).
    21. Argyropoulos, Efthymios & Tzavalis, Elias, 2015. "Real term structure forecasts of consumption growth," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 208-222.
    22. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2012. "The yield curve and the macro-economy across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1950-1970.
    23. Yasmeen Idilbi-Bayaa & Mahmoud Qadan, 2021. "Forecasting Commodity Prices Using the Term Structure," JRFM, MDPI, vol. 14(12), pages 1-39, December.
    24. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    25. Roberto Santis, 2015. "Quantity theory is alive: the role of international portfolio shifts," Empirical Economics, Springer, vol. 49(4), pages 1401-1430, December.
    26. Dong Jin Lee, 2021. "Bootstrap tests for structural breaks when the regressors and the serially correlated error term are unstable," Bulletin of Economic Research, Wiley Blackwell, vol. 73(2), pages 212-229, April.
    27. Morell, Joseph, 2018. "The decline in the predictive power of the US term spread: A structural interpretation," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 314-331.
    28. Cendejas Bueno, José Luis, 2023. "Recessions and flattening of the yield curve (1960–2021): A two-way road under a regime switching approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 8-20.
    29. Sokhanvar, Amin & Hammoudeh, Shawkat, 2024. "Comparative analysis of responses of risky and safe haven assets to stock market risk before and after the yield curve inversions in the U.S," International Review of Economics & Finance, Elsevier, vol. 94(C).
    30. Panopoulou, Ekaterini, 2009. "Financial variables and euro area growth: A non-parametric causality analysis," Economic Modelling, Elsevier, vol. 26(6), pages 1414-1419, November.
    31. Schrimpf, Andreas & Wang, Qingwei, 2010. "A reappraisal of the leading indicator properties of the yield curve under structural instability," International Journal of Forecasting, Elsevier, vol. 26(4), pages 836-857, October.
    32. Kuosmanen, Petri & Nabulsi, Nasib & Vataja, Juuso, 2015. "Financial variables and economic activity in the Nordic countries," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 368-379.
    33. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    34. Bordo, Michael D. & Haubrich, Joseph G., 2024. "Low interest rates and the predictive content of the yield curve," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    35. Anastasios Megaritis & Dimitrios Bakas & Theodora Bermpei & Athanasios Triantafyllou, 2026. "Term Spread Volatility as a Leading Indicator of Economic Activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(5), pages 2332-2354, August.
    36. Pantelis Promponas & David Alan Peel, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    37. Bell go, C. & Laurent Ferrara, 2009. "Forecasting Euro-area recessions using time-varying binary response models for financial," Working papers 259, Banque de France.
    38. Hännikäinen, Jari, 2014. "Zero lower bound, unconventional monetary policy and indicator properties of interest rate spreads," MPRA Paper 56737, University Library of Munich, Germany.
    39. Herman O. Stekler & Tianyu Ye, 2017. "Evaluating a leading indicator: an application—the term spread," Empirical Economics, Springer, vol. 53(1), pages 183-194, August.
    40. Paolo Zagaglia, 2024. "A Note on the Predictive Power of the Term Spread for the Output Gap," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(6), pages 1-7.
    41. Dovern, Jonas & Ziegler, Christina, 2008. "Predicting growth rates and recessions: assessing US leading indicators under real-time conditions," Kiel Working Papers 1397, Kiel Institute for the World Economy.
    42. Thomas B. King & Andrew T. Levin & Roberto Perli, 2007. "Financial market perceptions of recession risk," Finance and Economics Discussion Series 2007-57, Board of Governors of the Federal Reserve System (U.S.).
    43. Junttila, Juha & Vataja, Juuso, 2018. "Economic policy uncertainty effects for forecasting future real economic activity," Economic Systems, Elsevier, vol. 42(4), pages 569-583.
    44. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    45. Markku Lanne & Henri Nyberg, 2016. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 595-603, August.
    46. Joseph G. Haubrich, 2020. "Does the Yield Curve Predict Output?," Working Papers 20-34, Federal Reserve Bank of Cleveland.
    47. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
    48. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    49. Gebka, Bartosz & Wohar, Mark E., 2018. "The predictive power of the yield spread for future economic expansions: Evidence from a new approach," Economic Modelling, Elsevier, vol. 75(C), pages 181-195.
    50. David C. Wheelock & Mark E. Wohar, 2009. "Can the term spread predict output growth and recessions? a survey of the literature," Review, Federal Reserve Bank of St. Louis, vol. 91(Sep), pages 419-440.
    51. Kuosmanen, Petri & Vataja, Juuso, 2019. "Time-varying predictive content of financial variables in forecasting GDP growth in the G-7 countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 211-222.
    52. Paolo Zagaglia, 2024. "The Predictive Power of the Yield Spread Under the Veil of Time," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 13(3), pages 1-1.
    53. Smith Aaron, 2012. "Markov Breaks in Regression Models," Journal of Time Series Econometrics, De Gruyter, vol. 4(1), pages 1-35, May.
    54. Ronald Ravinesh Kumar & Peter Josef Stauvermann & Hang Thi Thu Vu, 2021. "The Relationship between Yield Curve and Economic Activity: An Analysis of G7 Countries," JRFM, MDPI, vol. 14(2), pages 1-23, February.
    55. Chauvet, Marcelle & Senyuz, Zeynep, 2008. "A Joint Dynamic Bi-Factor Model of the Yield Curve and the Economy as a Predictor of Business Cycles," MPRA Paper 15076, University Library of Munich, Germany, revised Apr 2009.
    56. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    57. Dalu Zhang & Peter Moffatt, 2013. "Time series non-linearity in the real growth / recession-term spread relationship," University of East Anglia Applied and Financial Economics Working Paper Series 047, School of Economics, University of East Anglia, Norwich, UK..
    58. Zagaglia, Paolo, 2006. "The Predictive Power of the Yield Spread under the Veil of Time," Research Papers in Economics 2006:4, Stockholm University, Department of Economics.
    59. De Santis, Roberto A., 2012. "Quantity theory is alive: the role of international portfolio shifts," Working Paper Series 1435, European Central Bank.
    60. Rashad Ahmed & Menzie D. Chinn, 2025. "Do Foreign Yield Curves Predict U.S. Recessions and GDP Growth?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 57(8), pages 2075-2098, December.
    61. Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2016. "Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data," Working Papers 201685, University of Pretoria, Department of Economics.
    62. He, Zhongfang, 2009. "Forecasting output growth by the yield curve: the role of structural breaks," MPRA Paper 28208, University Library of Munich, Germany.
    63. Petri Kuosmanen & Juuso Vataja, 2017. "The return of financial variables in forecasting GDP growth in the G-7," Economic Change and Restructuring, Springer, vol. 50(3), pages 259-277, August.
    64. Pesaran, M. Hashem & Pick, Andreas & Pranovich, Mikhail, 2013. "Optimal forecasts in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 134-152.
    65. Chauvet, Marcelle & Senyuz, Zeynep, 2016. "A dynamic factor model of the yield curve components as a predictor of the economy," International Journal of Forecasting, Elsevier, vol. 32(2), pages 324-343.
    66. Kuosmanen, Petri & Rahko, Jaana & Vataja, Juuso, 2019. "Predictive ability of financial variables in changing economic circumstances," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 37-47.
    67. Siliverstovs, Boriss, 2017. "Dissecting models' forecasting performance," Economic Modelling, Elsevier, vol. 67(C), pages 294-299.
    68. Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.
    69. Ciner, Cetin, 2020. "Causality dynamics from equities to economic growth," Finance Research Letters, Elsevier, vol. 34(C).
    70. De Pace, Pierangelo & Weber, Kyle D., 2016. "The time-varying leading properties of the high yield spread in the United States," International Journal of Forecasting, Elsevier, vol. 32(1), pages 203-230.
    71. Jonathan H. Wright, 2006. "The yield curve and predicting recessions," Finance and Economics Discussion Series 2006-07, Board of Governors of the Federal Reserve System (U.S.).

  26. Raffaella Giacomini & Ivana Komunjer, 2003. "Evaluation and Combination of Conditional Quantile Forecasts," Boston College Working Papers in Economics 571, Boston College Department of Economics.

    Cited by:

    1. Nieto, María Rosa & Ruiz Ortega, Esther, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Nowcasting tail risk to economic activity at a weekly frequency," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 843-866, August.
    4. Sessi Tokpavi, 2013. "Testing for the Systemically Important Financial Institutions: a Conditional Approach," Working Papers hal-04141194, HAL.
    5. McAleer, M.J. & Jiménez-Martín, J.A. & Pérez-Amaral, T., 2010. "GFC-Robust Risk Management Strategies under the Basel Accord," Econometric Institute Research Papers EI 2010-59, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Lei, Heng & Xue, Minggao & Liu, Huiling & Ye, Jing, 2023. "Precious metal as a safe haven for global ESG stocks: Portfolio implications for socially responsible investing," Resources Policy, Elsevier, vol. 80(C).
    7. Cathy W. S. Chen & Takaaki Koike & Wei‐Hsuan Shau, 2024. "Tail risk forecasting with semiparametric regression models by incorporating overnight information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1492-1512, August.
    8. Prodosh Simlai, 2021. "Accrual mispricing, value-at-risk, and expected stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1487-1517, November.
    9. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    10. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    11. Simon Lloyd & Ed Manuel & Konstantin Panchev, 2024. "Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(1), pages 335-392, March.
    12. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2014. "Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities," Tinbergen Institute Discussion Papers 14-090/III, Tinbergen Institute.
    13. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    14. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    15. Carlos Trucíos & James W. Taylor, 2023. "A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 989-1007, July.
    16. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    17. Anthoulla Phella, 2020. "Forecasting With Factor-Augmented Quantile Autoregressions: A Model Averaging Approach," Papers 2010.12263, arXiv.org.
    18. Francq, Christian & Zakoian, Jean-Michel, 2019. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," MPRA Paper 95965, University Library of Munich, Germany.
    19. Faria, Adriano & Almeida, Caio, 2018. "A hybrid spline-based parametric model for the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 86(C), pages 72-94.
    20. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    21. Kleppe, Tore Selland & Liesenfeld, Roman & Moura, Guilherme Valle & Oglend, Atle, 2022. "Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility," Econometrics and Statistics, Elsevier, vol. 23(C), pages 105-127.
    22. Ross Askanazi & Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2018. "On the Comparison of Interval Forecasts," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 953-965, November.
    23. Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Papers 1708.08622, arXiv.org.
    24. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    25. Gaglianone, Wagner Piazza & Linton, Oliver & Lima, Luiz Renato Regis de Oliveira, 2008. "Evaluating Value-at-Risk models via Quantile regressions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 679, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    26. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    27. Niels S. Hansen & Asger Lunde, 2013. "Analyzing Oil Futures with a Dynamic Nelson-Siegel Model," CREATES Research Papers 2013-36, Department of Economics and Business Economics, Aarhus University.
    28. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    29. Xu, Ke-Li, 2021. "On the serial correlation in multi-horizon predictive quantile regression," Economics Letters, Elsevier, vol. 200(C).
    30. Michael P. Clements & David I. Harvey, 2010. "Forecast encompassing tests and probability forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 1028-1062.
    31. Laporta, Alessandro G. & Merlo, Luca & Petrella, Lea, 2018. "Selection of Value at Risk Models for Energy Commodities," Energy Economics, Elsevier, vol. 74(C), pages 628-643.
    32. Gourieroux, C. & Jasiak, J., 2008. "Dynamic quantile models," Journal of Econometrics, Elsevier, vol. 147(1), pages 198-205, November.
    33. Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
    34. Wei Kuang, 2024. "High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-35, May.
    35. Edward W. Sun & Yu-Jen Wang & Min-Teh Yu, 2018. "Integrated Portfolio Risk Measure: Estimation and Asymptotics of Multivariate Geometric Quantiles," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 627-652, August.
    36. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    37. Victor Richmond R. Jose & Robert L. Winkler, 2009. "Evaluating Quantile Assessments," Operations Research, INFORMS, vol. 57(5), pages 1287-1297, October.
    38. Laura Garcia‐Jorcano & Alfonso Novales, 2021. "Volatility specifications versus probability distributions in VaR forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 189-212, March.
    39. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2015. "Exploiting Spillovers to forecast Crashes," Tinbergen Institute Discussion Papers 15-118/III, Tinbergen Institute.
    40. Taylor, James W. & Jeon, Jooyoung, 2015. "Forecasting wind power quantiles using conditional kernel estimation," Renewable Energy, Elsevier, vol. 80(C), pages 370-379.
    41. Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," NBER Working Papers 10428, National Bureau of Economic Research, Inc.
    42. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    43. Guanghui Cai & Zhimin Wu & Lei Peng, 2021. "Forecasting volatility with outliers in Realized GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 667-685, July.
    44. Jie Cheng, 2024. "Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3617-3643, December.
    45. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.
    46. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    47. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
    48. Buse, Rebekka & Görgen, Konstantin & Schienle, Melanie, 2025. "Predicting value at risk for cryptocurrencies with generalized random forests," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1199-1222.
    49. Chia-Lin Chang & Lydia González-Serrano & Juan-Ángel Jiménez-Martín, 2012. "Currency Hedging Strategies Using Dynamic Multivariate GARCH," Documentos de Trabajo del ICAE 2012-07, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Feb 2012.
    50. Fei, Fei & Fuertes, Ana-Maria & Kalotychou, Elena, 2017. "Dependence in credit default swap and equity markets: Dynamic copula with Markov-switching," International Journal of Forecasting, Elsevier, vol. 33(3), pages 662-678.
    51. Santos, P.A. & Jiménez-Martín, J.A. & McAleer, M.J. & Pérez-Amaral, T., 2011. "GFC-Robust Risk Management Under the Basel Accord Using Extreme Value Methodologies," Econometric Institute Research Papers EI2011-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    52. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    53. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
    54. Figueres, Juan Manuel & Jarociński, Marek, 2020. "Vulnerable growth in the Euro Area: Measuring the financial conditions," Working Paper Series 2458, European Central Bank.
    55. Martin Gachter & Elias Hasler & Florian Huber, 2023. "A tale of two tails: 130 years of growth-at-risk," Papers 2302.08920, arXiv.org.
    56. Giuseppe Storti & Chao Wang, 2020. "Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles," Papers 2005.04868, arXiv.org, revised Mar 2021.
    57. Trung H. Le, 2024. "Forecasting VaR and ES in emerging markets: The role of time‐varying higher moments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 402-414, March.
    58. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org, revised Jul 2025.
    59. Maximilian Boeck & Massimiliano Marcellino & Michael Pfarrhofer & Tommaso Tornese, 2024. "Predicting Tail-Risks for the Italian Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(3), pages 339-366, November.
    60. Xiaochun Liu, 2017. "An integrated macro‐financial risk‐based approach to the stressed capital requirement," Review of Financial Economics, John Wiley & Sons, vol. 34(1), pages 86-98, September.
    61. Ignacio Garr'on & Andrey Ramos, 2025. "High-frequency Density Nowcasts of U.S. State-Level Carbon Dioxide Emissions," Papers 2501.03380, arXiv.org.
    62. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    63. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    64. Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    65. Krenar Avdulaj & Jozef Barunik, 2013. "Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data," Papers 1307.5981, arXiv.org, revised Feb 2015.
    66. Keijsers, Bart & van Dijk, Dick, 2025. "Does economic uncertainty predict real activity in real time?," International Journal of Forecasting, Elsevier, vol. 41(2), pages 748-762.
    67. Rebekka Buse & Konstantin Gorgen & Melanie Schienle, 2022. "Predicting Value at Risk for Cryptocurrencies With Generalized Random Forests," Papers 2203.08224, arXiv.org, revised Dec 2024.
    68. Tae-Hwy Lee & Dingli Wang, 2026. "Median-Anchored Adjustment of Joint VaR--ES Forecasts," Working Papers 202604, University of California at Riverside, Department of Economics.
    69. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.
    70. Matteo Mogliani & Florens Odendahl, 2025. "Density forecast transformations," Working Papers 2511, Banco de España.
    71. Escanciano, J. C. & Olmo, J., 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," Working Papers 07/11, Department of Economics, City St George's, University of London.
    72. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    73. Yuru Sun & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Gael M. Martin, 2023. "Optimal probabilistic forecasts for risk management," Papers 2303.01651, arXiv.org.
    74. Sainan Jin & Valentina Corradi & Norman Swanson, 2015. "Robust Forecast Comparison," Departmental Working Papers 201502, Rutgers University, Department of Economics.
    75. Joanna Górka, 2010. "The Sign RCA Models: Comparing Predictive Accuracy of VaR Measures," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 61-80.
    76. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2026. "From Reactive to Proactive Volatility Modeling With Hemisphere Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(3), pages 265-279, April.
    77. Santos, André A. P. & Nogales, Francisco J. & Ruiz Ortega, Esther, 2009. "Comparing univariate and multivariate models to forecast portfolio value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws097222, Universidad Carlos III de Madrid. Departamento de Estadística.
    78. Xu, Qifa & Niu, Xufeng & Jiang, Cuixia & Huang, Xue, 2015. "The Phillips curve in the US: A nonlinear quantile regression approach," Economic Modelling, Elsevier, vol. 49(C), pages 186-197.
    79. Filip Zikes & Jozef Barunik, 2013. "Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility," Papers 1308.4276, arXiv.org.
    80. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2022. "Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications," Papers 2211.16121, arXiv.org, revised Aug 2024.
    81. Chiu, Yen-Chen & Chuang, I-Yuan, 2016. "The performance of the switching forecast model of value-at-risk in the Asian stock markets," Finance Research Letters, Elsevier, vol. 18(C), pages 43-51.
    82. Timo Dimitriadis & Tobias Fissler & Johanna Ziegel, 2020. "The Efficiency Gap," Papers 2010.14146, arXiv.org, revised Sep 2022.
    83. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    84. Opschoor, Anne & van Dijk, Dick & van der Wel, Michel, 2014. "Predicting volatility and correlations with Financial Conditions Indexes," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 435-447.
    85. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    86. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
    87. Christian Brownlees & Giuseppe Cavaliere & Alice Monti, 2018. "Evaluating The Accuracy Of Tail Risk Forecasts For Systemic Risk Measurement," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-25, June.
    88. Niko Hauzenberger & Massimiliano Marcellino & Michael Pfarrhofer & Anna Stelzer, 2024. "Nowcasting with Mixed Frequency Data Using Gaussian Processes," Papers 2402.10574, arXiv.org, revised Sep 2024.
    89. Alain-Philippe Fortin & Jean-Guy Simonato & Georges Dionne, 2018. "Forecasting Expected Shortfall: Should we use a Multivariate Model for Stock Market Factors?," Working Papers 18-4, HEC Montreal, Canada Research Chair in Risk Management.
    90. Michael W. McCracken, 2020. "Diverging Tests of Equal Predictive Ability," Econometrica, Econometric Society, vol. 88(4), pages 1753-1754, July.
    91. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2015. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Tinbergen Institute Discussion Papers 15-140/III, Tinbergen Institute, revised 19 Apr 2017.
    92. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.
    93. Tae-Hwan Kim & Dong Jin Lee & Paul Mizen, 2020. "Impulse Response Analysis in Conditional Quantile Models and an Application to Monetary Policy," Working papers 2020rwp-164, Yonsei University, Yonsei Economics Research Institute.
    94. Lang, Jan Hannes & Rusnák, Marek & Greiwe, Moritz, 2023. "Medium-term growth-at-risk in the euro area," Working Paper Series 2808, European Central Bank.
    95. Lis Szymon & Chlebus Marcin, 2023. "Combining forecasts? Keep it simple," Central European Economic Journal, Paradigm, vol. 10(57), pages 343-370, January.
    96. Giuseppe Storti & Chao Wang, 2021. "Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach," Papers 2104.04918, arXiv.org, revised Jul 2021.
    97. Polbin, Andrey & Shumilov, Andrei, 2025. "Наукастинг И Прогнозирование Ввп России И Его Компонентов С Помощью Квантильных Моделей [Nowcasting and forecasting Russian GDP and its components using quantile models]," MPRA Paper 125440, University Library of Munich, Germany.
    98. Tafakori, Laleh & Pourkhanali, Armin & Fard, Farzad Alavi, 2018. "Forecasting spikes in electricity return innovations," Energy, Elsevier, vol. 150(C), pages 508-526.
    99. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
    100. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    101. Chen, Cathy W.S. & Gerlach, Richard & Hwang, Bruce B.K. & McAleer, Michael, 2012. "Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range," International Journal of Forecasting, Elsevier, vol. 28(3), pages 557-574.
    102. Stavroula P. Fameliti & Vasiliki D. Skintzi, 2020. "Predictive ability and economic gains from volatility forecast combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 200-219, March.
    103. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    104. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    105. Xiao, Zhijie, 2009. "Quantile cointegrating regression," Journal of Econometrics, Elsevier, vol. 150(2), pages 248-260, June.
    106. Bams, Dennis & Blanchard, Gildas & Lehnert, Thorsten, 2017. "Volatility measures and Value-at-Risk," International Journal of Forecasting, Elsevier, vol. 33(4), pages 848-863.
    107. Laura Garcia-Jorcano & Alfonso Novales, 2019. "A dominance approach for comparing the performance of VaR forecasting models," Documentos de Trabajo del ICAE 2019-23, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    108. Diks, Cees & Fang, Hao, 2020. "Comparing density forecasts in a risk management context," International Journal of Forecasting, Elsevier, vol. 36(2), pages 531-551.
    109. Ilias Filippou & James Mitchell & My T. Nguyen, 2023. "The FOMC versus the Staff: Do Policymakers Add Value in Their Tales?," Working Papers 23-20, Federal Reserve Bank of Cleveland.
    110. Clements, Michael P. & Galvao, Ana Beatriz & Kim, Jae H., "undated". "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," Economic Research Papers 269747, University of Warwick - Department of Economics.
    111. Lhuissier, Stéphane, 2022. "Financial conditions and macroeconomic downside risks in the euro area," European Economic Review, Elsevier, vol. 143(C).
    112. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    113. Wang, Shuai & Wang, Qian & Lu, Helen & Zhang, Dongxue & Xing, Qianyi & Wang, Jianzhou, 2025. "Learning about tail risk: Machine learning and combination with regularization in market risk management," Omega, Elsevier, vol. 133(C).
    114. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    115. Brownlees, Christian & Souza, André B.M., 2021. "Backtesting global Growth-at-Risk," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 312-330.
    116. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Quantile forecast optimal combination to enhance safety stock estimation," International Journal of Forecasting, Elsevier, vol. 35(1), pages 239-250.
    117. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    118. Halbleib, Roxana & Pohlmeier, Winfried, 2012. "Improving the value at risk forecasts: Theory and evidence from the financial crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1212-1228.
    119. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    120. Zijian Zeng & Meng Li, 2020. "Bayesian Median Autoregression for Robust Time Series Forecasting," Papers 2001.01116, arXiv.org, revised Dec 2020.
    121. Kellner, Domenic & Lang, Jan Hannes & Rusnák, Marek & Nagy, Lukas Joseph, 2026. "A SPOT in the dark: using AI to assess financial stability risks," Working Paper Series 3262, European Central Bank.
    122. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    123. Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2014. "Are news important to predict large losses?," Papers 1410.6898, arXiv.org, revised Oct 2014.
    124. Andrey Polbin & Andrei Shumilov, 2025. "Nowcasting and forecasting Russian GDP and its components using quantile models," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 79, pages 5-26.
    125. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    126. Vica Tendenan & Richard Gerlach & Chao Wang, 2020. "Tail risk forecasting using Bayesian realized EGARCH models," Papers 2008.05147, arXiv.org, revised Aug 2020.
    127. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    128. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    129. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    130. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    131. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    132. Chaker Aloui & Hela BEN HAMIDA, 2015. "Estimation and Performance Assessment of Value-at-Risk and Expected Shortfall Based on Long-Memory GARCH-Class Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(1), pages 30-54, January.
    133. Mai Dao & Lam Nguyen, 2025. "Variable selection in macroeconomic stress test: a Bayesian quantile regression approach," Empirical Economics, Springer, vol. 68(3), pages 1113-1169, March.
    134. Sander Barendse, 2017. "Interquantile Expectation Regression," Tinbergen Institute Discussion Papers 17-034/III, Tinbergen Institute.
    135. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2013. "Predicting Covariance Matrices with Financial Conditions Indexes," Tinbergen Institute Discussion Papers 13-113/III, Tinbergen Institute.
    136. Timmermann, Allan, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, Centre for Economic Policy Research.
    137. Wei Kuang, 2022. "Oil tail-risk forecasts: from financial crisis to COVID-19," Risk Management, Palgrave Macmillan, vol. 24(4), pages 420-460, December.
    138. Baruník, Jozef & Čech, František, 2021. "Measurement of common risks in tails: A panel quantile regression model for financial returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    139. Timo Dimitriadis & Julie Schnaitmann, 2019. "Forecast Encompassing Tests for the Expected Shortfall," Papers 1908.04569, arXiv.org, revised Aug 2020.
    140. Sokol, Andrej, 2025. "Fan charts 2.0: Flexible forecast distributions with expert judgement," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1148-1164.
    141. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    142. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    143. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2025. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(1), pages 57-73, January.
    144. Fantazzini, Dean, 2009. "The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo study," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2168-2188, April.
    145. Huiyu Huang & Tae-Hwy Lee, 2006. "To Combine Forecasts or to Combine Information?," Working Papers 200806, University of California at Riverside, Department of Economics, revised Feb 2009.
    146. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    147. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    148. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    149. František Čech & Jozef Baruník, 2019. "Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1167-1189, September.
    150. Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Yoldas, Emre, 2007. "Optimality of the RiskMetrics VaR model," Finance Research Letters, Elsevier, vol. 4(3), pages 137-145, September.
    151. Zeng, Zijian & Li, Meng, 2021. "Bayesian median autoregression for robust time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 1000-1010.
    152. Jan G. De Gooijer, 2023. "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 407-424, June.
    153. Juan Carlos Escanciano & Jose Olmo, 2007. "Backtesting Parametric Value-at-Risk with Estimation Risk," CAEPR Working Papers 2007-005, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, revised Sep 2008.
    154. De Gooijer Jan G. & Zerom Dawit, 2020. "Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-15, January.
    155. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.

  27. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, University Library of Munich, Germany.

    Cited by:

    1. Juan Carlos Pérez-Velasco Pavón, 2009. "Determinantes de la demanda por la denominación promedio de billete: el caso de México," Monetaria, CEMLA, vol. 0(4), pages 523-548, octubre-d.
    2. Gomez, William & Wang, Fu-Kwun & Sheu, Shey-Huei, 2025. "Short-term smart grid energy forecasting using a hybrid deep learning method on univariate and multivariate data sets," Energy, Elsevier, vol. 335(C).
    3. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    4. Benchimol, Jonathan & El-Shagi, Makram, 2020. "Forecast performance in times of terrorism," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 91, pages 386-402.
    5. Roxana Halbleib & Valeri Voev, 2012. "Forecasting Covariance Matrices: A Mixed Frequency Approach," Working Paper Series of the Department of Economics, University of Konstanz 2012-30, Department of Economics, University of Konstanz.
    6. Ñíguez, Trino-Manuel & Perote, Javier, 2017. "Moments expansion densities for quantifying financial risk," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 53-69.
    7. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    8. Norman R. Swanson & Nii Ayi Armah, 2011. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 201103, Rutgers University, Department of Economics.
    9. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    10. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
    11. Andrea Panozzo, Lorenzo Spadavecchia, Adam Mugume, Elizabeth Kasekende, Samuel Namwanja Musoke, Mariss Nakayaga, Deo Sande, Anita Mpagi, Nzima Ghislain, 2026. "Nowcasting GDP with Digital Payments: Evidence from Uganda," BAFFI CAREFIN Working Papers 26282, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    12. Lee, Tae-Hwy & Long, Xiangdong, 2009. "Copula-based multivariate GARCH model with uncorrelated dependent errors," Journal of Econometrics, Elsevier, vol. 150(2), pages 207-218, June.
    13. Liu, Xiaochun, 2015. "Modeling time-varying skewness via decomposition for out-of-sample forecast," International Journal of Forecasting, Elsevier, vol. 31(2), pages 296-311.
    14. Bec, Frédérique & Gollier, Christian, 2014. "Cyclicality and term structure of Value-at-Risk within a threshold autoregression setup," IDEI Working Papers 835, Institut d'Économie Industrielle (IDEI), Toulouse.
    15. Chevillon, Guillaume, 2009. "Multi-step forecasting in emerging economies: An investigation of the South African GDP," International Journal of Forecasting, Elsevier, vol. 25(3), pages 602-628, July.
    16. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    17. Isao Ishida, 2005. "Scanning Multivariate Conditional Densities with Probability Integral Transforms," CARF F-Series CARF-F-045, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    18. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    19. Juan Manuel Julio & Javier Guillermo Gómez & Manuel Dario Hernández, 2017. "La Inflación de los Precios Rígidos en Colombia," Borradores de Economia 1007, Banco de la Republica de Colombia.
    20. Andrea Monticini & Francesco Ravazzolo, 2014. "Forecasting the intraday market price of money," DISCE - Working Papers del Dipartimento di Economia e Finanza def010, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    21. Guilherme Schultz Lindenmeyer & Hudson Silva Torrent, 2024. "Boosting and Predictability of Macroeconomic Variables: Evidence from Brazil," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 377-409, July.
    22. Alexander M. Chinco & Adam D. Clark-Joseph & Mao Ye, 2017. "Sparse Signals in the Cross-Section of Returns," NBER Working Papers 23933, National Bureau of Economic Research, Inc.
    23. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    24. Verena Monschang & Mark Trede & Bernd Wilfling, 2023. "Multi-horizon uniform superior predictive ability revisited: A size-exploiting and consistent test," CQE Working Papers 10623, Center for Quantitative Economics (CQE), University of Muenster.
    25. David I. Harvey & Stephen J. Leybourne & Yang Zu, 2024. "Tests for equal forecast accuracy under heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 850-869, August.
    26. Filip Staněk, 2023. "Optimal out‐of‐sample forecast evaluation under stationarity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2249-2279, December.
    27. El-Shagi, Makram & Giesen, Sebastian & Jung, Alexander, 2016. "Revisiting the relative forecast performances of Fed staff and private forecasters: A dynamic approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 313-323.
    28. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    29. Jack Fosten, 2016. "Model selection with factors and variables," University of East Anglia School of Economics Working Paper Series 2016-07, School of Economics, University of East Anglia, Norwich, UK..
    30. Ghent, Andra C., 2009. "Comparing DSGE-VAR forecasting models: How big are the differences?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 864-882, April.
    31. Medel, Carlos A., 2012. "¿Akaike o Schwarz? ¿Cuál elegir para predecir el PIB chileno? [Akaike or Schwarz? Which One is a Better Predictor of Chilean GDP?]," MPRA Paper 35950, University Library of Munich, Germany.
    32. James W. Taylor & Xiaochun Meng, 2026. "Angular Combining of Forecasts of Probability Distributions," Management Science, INFORMS, vol. 72(3), pages 2111-2133, March.
    33. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
    34. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    35. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    36. Hännikäinen, Jari, 2017. "When does the yield curve contain predictive power? Evidence from a data-rich environment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1044-1064.
    37. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    38. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    39. Pierdzioch, Christian & Döpke, Jörg & Hartmann, Daniel, 2008. "Forecasting stock market volatility with macroeconomic variables in real time," Journal of Economics and Business, Elsevier, vol. 60(3), pages 256-276.
    40. Massacci, Daniele & Kapetanios, George, 2024. "Forecasting in factor augmented regressions under structural change," International Journal of Forecasting, Elsevier, vol. 40(1), pages 62-76.
    41. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    42. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    43. Yvonne Adema & Kees Folmer & Gerrit Hugo Heuvelen & Sonny Kuijpers & Rob Luginbuhl & Bas Scheer, 2020. "Unemployment Forecasts: Room for Improvement?," De Economist, Springer, vol. 168(3), pages 403-417, September.
    44. John Y. Campbell & Samuel B. Thompson, 2005. "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?," NBER Working Papers 11468, National Bureau of Economic Research, Inc.
    45. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2014. "Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities," Tinbergen Institute Discussion Papers 14-090/III, Tinbergen Institute.
    46. Juan Tenorio & Heidi Alpiste & Jakelin Rem'on & Arian Segil, 2025. "An Artificial Trend Index for Private Consumption Using Google Trends," Papers 2503.21981, arXiv.org.
    47. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    48. Guillen, Osmani Teixeira Carvalho & Hecq, Alain & Issler, João Victor & Saraiva, Diogo Vinícius Menezes, 2013. "Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 742, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    49. Buccheri, Giuseppe & Renò, Roberto & Vocalelli, Giorgio, 2025. "Taking advantage of biased proxies for forecast evaluation," Journal of Econometrics, Elsevier, vol. 251(C).
    50. D'Agostino, Antonello & Domenico, Giannone & Surico, Paolo, 2006. "(Un)Predictability and Macroeconomic Stability," Research Technical Papers 5/RT/06, Central Bank of Ireland.
    51. O-Chia Chuang & Chenxu Yang, 2022. "Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model," Energies, MDPI, vol. 15(8), pages 1-14, April.
    52. El-Shagi, Makram, 2019. "Rationality tests in the presence of instabilities in finite samples," Economic Modelling, Elsevier, vol. 79(C), pages 242-246.
    53. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    54. Guarin, Alexander & Lozano, Ignacio, 2017. "Credit funding and banking fragility: A forecasting model for emerging economies," Emerging Markets Review, Elsevier, vol. 32(C), pages 168-189.
    55. Elizondo Rocío, 2013. "Forecasting the Term Structure of Interest Rates in Mexico Using an Affine Model," Working Papers 2013-03, Banco de México.
    56. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 1-33.
    57. Geert Dhaene & Piet Sercu & Jianbin Wu, 2022. "Volatility spillovers: A sparse multivariate GARCH approach with an application to commodity markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(5), pages 868-887, May.
    58. Tobias Fissler & Yannick Hoga, 2021. "Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability," Papers 2104.10673, arXiv.org, revised Feb 2022.
    59. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    60. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    61. Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2020. "Comparing Forecast Performance with State Dependence," CEPR Discussion Papers 15217, Centre for Economic Policy Research.
    62. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    63. María Alejandra Hernández-Montes & Ramón Hernández-Ortega & Jonathan Alexander Muñoz-Martínez, 2022. "Aporte de las expectativas de empresarios al pronóstico de las variables macroeconómicas," Borradores de Economia 1202, Banco de la Republica de Colombia.
    64. Diks, Cees & Panchenko, Valentyn & Sokolinskiy, Oleg & van Dijk, Dick, 2014. "Comparing the accuracy of multivariate density forecasts in selected regions of the copula support," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 79-94.
    65. Carriero, A. & Kapetanios, G. & Marcellino, M., 2009. "Forecasting exchange rates with a large Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 25(2), pages 400-417.
    66. Pirschel, Inske & Wolters, Maik, 2014. "Forecasting German key macroeconomic variables using large dataset methods," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100587, Verein für Socialpolitik / German Economic Association.
    67. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    68. Grzegorz Marcjasz & Bartosz Uniejewski & Rafał Weron, 2020. "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts," Energies, MDPI, vol. 13(7), pages 1-16, April.
    69. Koo, Bonsoo & Seo, Myung Hwan, 2015. "Structural-break models under mis-specification: Implications for forecasting," Journal of Econometrics, Elsevier, vol. 188(1), pages 166-181.
    70. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    71. Jiang, He & Dong, Yawei & Dong, Yao & Wang, Jianzhou, 2025. "Probabilistic electricity price forecasting by integrating interpretable model," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    72. Luisa Bisaglia & Matteo Grigoletto, 2018. "A new time-varying model for forecasting long-memory series," Papers 1812.07295, arXiv.org.
    73. Kees E. Bouwman & Elvira Sojli & Wing Wah Tham, 2012. "Aggregate Stock Market Illiquidity and Bond Risk Premia," Tinbergen Institute Discussion Papers 12-140/IV/DSF46, Tinbergen Institute.
    74. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting," Papers 2404.02671, arXiv.org, revised Nov 2024.
    75. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    76. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    77. Ataman Ozyildirim & Brian Schaitkin & Victor Zarnowitz, 2010. "Business cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 6-28.
    78. Chen, Yong & Eaton, Gregory W. & Paye, Bradley S., 2018. "Micro(structure) before macro? The predictive power of aggregate illiquidity for stock returns and economic activity," Journal of Financial Economics, Elsevier, vol. 130(1), pages 48-73.
    79. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    80. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
    81. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    82. Ziegler, Christina & Eickmeier, Sandra, 2006. "How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Discussion Paper Series 1: Economic Studies 2006,42, Deutsche Bundesbank.
    83. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639.
    84. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    85. Kirstin Hubrich & Kenneth D. West, 2008. "Forecast Evaluation of Small Nested Model Sets," NBER Working Papers 14601, National Bureau of Economic Research, Inc.
    86. Parigi, Giuseppe & Golinelli, Roberto, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, Centre for Economic Policy Research.
    87. Òscar Jordà & Moritz Schularick & Alan M. Taylor, 2010. "Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons," NBER Working Papers 16567, National Bureau of Economic Research, Inc.
    88. Faria, Adriano & Almeida, Caio, 2018. "A hybrid spline-based parametric model for the yield curve," Journal of Economic Dynamics and Control, Elsevier, vol. 86(C), pages 72-94.
    89. George Tzagkarakis & Frantz Maurer, 2020. "An energy-based measure for long-run horizon risk quantification," Annals of Operations Research, Springer, vol. 289(2), pages 363-390, June.
    90. Sepideh Dolatabadi & Ke Xu & Morten Ø. Nielsen & Paresh Kumar Narayan, 2017. "Economic Significance Of Commodity Return Forecasts From The Fractionally Cointegrated Var Model," Working Paper 1337, Economics Department, Queen's University.
    91. Michael Dotsey & Shigeru Fujita & Tom Stark, 2017. "Do Phillips Curves Conditionally Help to Forecast Inflation?," Working Papers 17-26, Federal Reserve Bank of Philadelphia.
    92. Medel, Carlos A., 2017. "Forecasting Chilean Inflation with the Hybrid New Keynesian Phillips Curve: Globalisation, Combination, and Accuracy," MPRA Paper 78439, University Library of Munich, Germany.
    93. Chen, Yi-Ting & Liu, Chu-An & Su, Jiun-Hua, 2025. "Bregman model averaging for forecast combination," Journal of Econometrics, Elsevier, vol. 251(C).
    94. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    95. Gourieroux, C. & Monfort, A., 2021. "Model risk management: Valuation and governance of pseudo-models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 1-22.
    96. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," Economics Papers 2009-W12, Economics Group, Nuffield College, University of Oxford.
    97. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    98. João Caldeira & Guilherme Moura & André Santos, 2015. "Measuring Risk in Fixed Income Portfolios using Yield Curve Models," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 65-82, June.
    99. Sihong Chen & Qi Li & Qiaoyu Wang & Yu Yvette Zhang, 2023. "Multivariate models of commodity futures markets: a dynamic copula approach," Empirical Economics, Springer, vol. 64(6), pages 3037-3057, June.
    100. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.
    101. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    102. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    103. Carlos Medel, 2015. "Inflation Dynamics and the Hybrid Neo Keynesian Phillips Curve: The Case of Chile," Working Papers Central Bank of Chile 769, Central Bank of Chile.
    104. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2016. "What derives the bond portfolio value-at-risk: Information roles of macroeconomic and financial stress factors," SFB 649 Discussion Papers 2016-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    105. Nikoleta Anesti & Ana Beatriz Galvão & Silvia Miranda‐Agrippino, 2022. "Uncertain Kingdom: Nowcasting Gross Domestic Product and its revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 42-62, January.
    106. Marcin Dec, 2026. "When 3% means nothing: Calibrating escalation limits to a bank’s own forecasting error distribution," GRAPE Working Papers 114, GRAPE Group for Research in Applied Economics.
    107. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
    108. Yu‐Sheng Lai, 2019. "Flexible covariance dynamics, high‐frequency data, and optimal futures hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1529-1548, December.
    109. Neil Shephard & Kevin Sheppard & Robert F. Engle, 2008. "Fitting vast dimensional time-varying covariance models," Economics Series Working Papers 403, University of Oxford, Department of Economics.
    110. Coroneo, Laura & Iacone, Fabrizio, 2025. "Testing for equal predictive accuracy with strong dependence," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1073-1092.
    111. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
    112. Eran Raviv, 2013. "Prediction Bias Correction for Dynamic Term Structure Models," Tinbergen Institute Discussion Papers 13-041/III, Tinbergen Institute.
    113. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
    114. James Mitchell & Aubrey Poon & Dan Zhu, 2024. "Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 790-812, August.
    115. Ellington, Michael & Kalli, Maria, 2025. "Predictive distributions and the market return: The role of market illiquidity," European Journal of Operational Research, Elsevier, vol. 323(1), pages 309-322.
    116. Luis Filipe Martins & Pierre Perron, 2016. "Improved Tests for Forecast Comparisons in the Presence of Instabilities," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 650-659, September.
    117. Yuta Kurose, 2021. "Stochastic volatility model with range-based correction and leverage," Papers 2110.00039, arXiv.org, revised Oct 2021.
    118. Oh, Dong Hwan & Patton, Andrew J., 2023. "Dynamic factor copula models with estimated cluster assignments," Journal of Econometrics, Elsevier, vol. 237(2).
    119. Klinkowska, Olga & Zadorozhna, Olha, 2026. "The yield curve strikes back: New evidence of its predictive power for economic activity and inflation," International Review of Financial Analysis, Elsevier, vol. 113(C).
    120. Tobias Fissler & Yannick Hoga, 2024. "How to Compare Copula Forecasts?," Papers 2410.04165, arXiv.org.
    121. Akram Shavkatovich Hasanov & Robert Brooks & Sirojiddin Abrorov & Aktam Usmanovich Burkhanov, 2024. "Structural breaks and GARCH models of exchange rate volatility: Re‐examination and extension," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1403-1407, November.
    122. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    123. Pablo Pincheira Brown & Nicolás Hardy, 2024. "Correlation‐based tests of predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1835-1858, September.
    124. Reichlin, Lucrezia & Giannone, Domenico & Bańbura, Marta, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, Centre for Economic Policy Research.
    125. Khalaf, Lynda & Saunders, Charles J., 2017. "Monte Carlo forecast evaluation with persistent data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 1-10.
    126. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
    127. Ahmed, Shamim & Tsvetanov, Daniel, 2016. "The predictive performance of commodity futures risk factors," Journal of Banking & Finance, Elsevier, vol. 71(C), pages 20-36.
    128. Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
    129. Yafeng Shi & Tingting Ying & Yanlong Shi & Chunrong Ai, 2020. "A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1025-1034, November.
    130. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    131. Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018. "A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile," International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
    132. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    133. Chan Kim & Peter Reinhard Hansen, 2026. "The Quarter-Hour Effect: Periodic Algorithmic Trading and Return Predictability in Cryptocurrency Futures," Papers 2607.09426, arXiv.org, revised Jul 2026.
    134. Medel, Carlos A., 2015. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," MPRA Paper 67081, University Library of Munich, Germany.
    135. Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Paper series 42_09, Rimini Centre for Economic Analysis.
    136. Filip Stanek, 2021. "Optimal Out-of-Sample Forecast Evaluation under Stationarity," CERGE-EI Working Papers wp712, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    137. Georgios Tsiotas, 2009. "On the use of non-linear transformations in Stochastic Volatility models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(4), pages 555-583, November.
    138. Liyu Dou, 2024. "Optimal HAR inference," Quantitative Economics, Econometric Society, vol. 15(4), pages 1107-1149, November.
    139. Mao, Xiuping & Ruiz Ortega, Esther & Veiga, Helena, 2013. "One for all : nesting asymmetric stochastic volatility models," DES - Working Papers. Statistics and Econometrics. WS ws131110, Universidad Carlos III de Madrid. Departamento de Estadística.
    140. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.
    141. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    142. Hambuckers, J. & Ulm, M., 2023. "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, vol. 129(C).
    143. Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers Archive 32462, Iowa State University, Department of Economics.
    144. Ard Reijer, 2013. "Forecasting Dutch GDP and inflation using alternative factor model specifications based on large and small datasets," Empirical Economics, Springer, vol. 44(2), pages 435-453, April.
    145. Nicholas Taylor, 2014. "The Economic Value of Volatility Forecasts: A Conditional Approach," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 433-478.
    146. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    147. LAURENT, Sébastien & VIOLANTE, Francesco, 2012. "Volatility forecasts evaluation and comparison," LIDAM Reprints CORE 2414, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    148. Arkadiusz Lipiecki & Bartosz Uniejewski & Rafa{l} Weron, 2024. "Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression," Papers 2404.02270, arXiv.org, revised Oct 2024.
    149. Fiszeder, Piotr & Małecka, Marta & Molnár, Peter, 2024. "Robust estimation of the range-based GARCH model: Forecasting volatility, value at risk and expected shortfall of cryptocurrencies," Economic Modelling, Elsevier, vol. 141(C).
    150. Niels S. Hansen & Asger Lunde, 2013. "Analyzing Oil Futures with a Dynamic Nelson-Siegel Model," CREATES Research Papers 2013-36, Department of Economics and Business Economics, Aarhus University.
    151. Guo, Hongfei & Marín, Juan Miguel & Veiga, Helena, 2026. "Diagnosing and Stabilizing Dynamic Correlations in Multivariate Stochastic Volatility Models," DES - Working Papers. Statistics and Econometrics. WS 50561, Universidad Carlos III de Madrid. Departamento de Estadística.
    152. Malte Knüppel & Guido Schultefrankenfeld, 2017. "Interest rate assumptions and predictive accuracy of central bank forecasts," Empirical Economics, Springer, vol. 53(1), pages 195-215, August.
    153. Milas, Costas & Rothman, Philip, 2008. "Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 101-121.
    154. Michele Berardi & Jaqueson K. Galimberti, 2015. "Empirical Calibration of Adaptive Learning," KOF Working papers 15-392, KOF Swiss Economic Institute, ETH Zurich.
    155. Florens Odendahl & Barbara Rossi & Tatevik Sekhposyan, 2021. "Evaluating Forecast Performance with State Dependence," Working Papers 1295, Barcelona School of Economics.
    156. Kruse, Robinson & Leschinski, Christian & Will, Michael, 2016. "Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting," Hannover Economic Papers (HEP) dp-571, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    157. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    158. Roccazzella, Francesco & Candelon, Bertrand, 2022. "Should we care about ECB inflation expectations?," LIDAM Discussion Papers LFIN 2022004, Université catholique de Louvain, Louvain Finance (LFIN).
    159. Yu‐Sheng Lai, 2022. "Use of high‐frequency data to evaluate the performance of dynamic hedging strategies," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 104-124, January.
    160. Christophe BOUCHER & Wassim LE LANN & Stéphane MATTON & Sessi TOKPAVI, 2021. "Backtesting ESG Ratings," LEO Working Papers / DR LEO 2883, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    161. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2015. "A real-time quantile-regression approach to forecasting gold returns under asymmetric loss," Resources Policy, Elsevier, vol. 45(C), pages 299-306.
    162. Fawad, Muhammad & Yan, Ting & Chen, Lu & Huang, Kangdi & Singh, Vijay P., 2019. "Multiparameter probability distributions for at-site frequency analysis of annual maximum wind speed with L-Moments for parameter estimation," Energy, Elsevier, vol. 181(C), pages 724-737.
    163. Loaiza-Maya, Rubén & Nibbering, Didier & Zhu, Dan, 2024. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Journal of Econometrics, Elsevier, vol. 241(2).
    164. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2020. "The information content of funds from operations and net income in real estate investment trusts," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    165. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.
    166. Mekelburg, Erik & Strauss, Jack, 2024. "Pooling and winsorizing machine learning forecasts to predict stock returns with high-dimensional data," Journal of Empirical Finance, Elsevier, vol. 79(C).
    167. Andrew Meldrum & Marek Raczko & Peter Spencer, 2016. "Overseas unspanned factors and domestic bond returns," Bank of England Staff Working Paper series 618, Bank of England.
    168. Sung Je Byun, 2016. "Speculation in Commodity Futures Markets, Inventories and the Price of Crude Oil," Occasional Papers 16-3, Federal Reserve Bank of Dallas.
    169. Giacomini, Raffaella & Ragusa, Giuseppe, 2011. "Incorporating theoretical restrictions into forecasting by projection methods," CEPR Discussion Papers 8604, Centre for Economic Policy Research.
    170. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
    171. Laporta, Alessandro G. & Merlo, Luca & Petrella, Lea, 2018. "Selection of Value at Risk Models for Energy Commodities," Energy Economics, Elsevier, vol. 74(C), pages 628-643.
    172. Gourieroux, C. & Jasiak, J., 2008. "Dynamic quantile models," Journal of Econometrics, Elsevier, vol. 147(1), pages 198-205, November.
    173. Soylu, Pınar Kaya & Güloğlu, Bülent, 2019. "Financial contagion and flight to quality between emerging markets and U.S. bond market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    174. Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021. "Forecasting the production side of GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
    175. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    176. Gupta, Rangan & Steinbach, Rudi, 2013. "A DSGE-VAR model for forecasting key South African macroeconomic variables," Economic Modelling, Elsevier, vol. 33(C), pages 19-33.
    177. El-Shagi, Makram, 2011. "Inflation expectations: Does the market beat econometric forecasts?," The North American Journal of Economics and Finance, Elsevier, vol. 22(3), pages 298-319.
    178. Cerasa, Andrea & Zani, Alessandro, 2025. "Enhancing electricity price forecasting accuracy: A novel filtering strategy for improved out-of-sample predictions," Applied Energy, Elsevier, vol. 383(C).
    179. Onno Kleen, 2024. "Scaling and measurement error sensitivity of scoring rules for distribution forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 833-849, August.
    180. Alexander Vosseler & Enzo Weber, 2018. "Forecasting seasonal time series data: a Bayesian model averaging approach," Computational Statistics, Springer, vol. 33(4), pages 1733-1765, December.
    181. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2021. "Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility," Discussion paper series HIAS-E-104, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    182. David E. Rapach & Jack K. Strauss, 2008. "Structural breaks and GARCH models of exchange rate volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 65-90.
    183. Arkadiusz Lipiecki & Rafa{l} Weron, 2026. "Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?," Papers 2609.00089, arXiv.org.
    184. Zhanyi Jiao & Qiuqi Wang & Yimiao Zhao, 2025. "Comparative e-backtests for general risk measures," Papers 2511.05840, arXiv.org, revised Mar 2026.
    185. Eric Ghysels & Andros Kourtellos & Elena Andreou, 2012. "Should macroeconomic forecasters use daily financial data and how?," 2012 Meeting Papers 1196, Society for Economic Dynamics.
    186. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    187. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    188. Sidaoui José Julián & Capistrán Carlos & Chiquiar Daniel & Ramos Francia Manuel, 2009. "A Note on the Predictive Content of PPI over CPI Inflation: The Case of Mexico," Working Papers 2009-14, Banco de México.
    189. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    190. Jorge V Pérez-Rodríguez & Juan M Hernández & Julián Andrada-Félix, 2024. "Modelling prices and volatilities in the sharing economy," Tourism Economics, , vol. 30(5), pages 1189-1215, August.
    191. Ibarra-Ramírez Raúl, 2010. "Forecasting Inflation in Mexico Using Factor Models: Do Disaggregated CPI Data Improve Forecast Accuracy?," Working Papers 2010-01, Banco de México.
    192. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    193. Kratz, Marie & Lok, Y-H & McNeil, Alexander J., 2016. "Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall," ESSEC Working Papers WP1617, ESSEC Research Center, ESSEC Business School.
    194. Hofmann, Boris, 2009. "Do monetary indicators lead euro area inflation?," Journal of International Money and Finance, Elsevier, vol. 28(7), pages 1165-1181, November.
    195. Andre Lucas & Anne Opschoor, 2016. "Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns," Tinbergen Institute Discussion Papers 16-069/IV, Tinbergen Institute, revised 07 Jul 2017.
    196. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
    197. Arkadiusz Jedrzejewski & Grzegorz Marcjasz & Rafal Weron, 2021. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO," WORking papers in Management Science (WORMS) WORMS/21/04, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    198. Dong Hwan Oh & Andrew J. Patton, 2015. "High-Dimensional Copula-Based Distributions with Mixed Frequency Data," Finance and Economics Discussion Series 2015-50, Board of Governors of the Federal Reserve System (U.S.).
    199. Lorenzo Boldrini & Eric Hillebrand, 2015. "The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach," CREATES Research Papers 2015-39, Department of Economics and Business Economics, Aarhus University.
    200. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    201. Le, Van & Zurbruegg, Ralf, 2014. "Forecasting option smile dynamics," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 32-45.
    202. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2012. "Properties of foreign exchange risk premiums," Journal of Financial Economics, Elsevier, vol. 105(2), pages 279-310.
    203. Byun, Sung Je, 2016. "The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 162-180.
    204. Kevin J. Lansing & Adam Hale Shapiro, 2026. "Measuring Inflation Shock Momentum," Working Paper Series 2026-10, Federal Reserve Bank of San Francisco.
    205. Edward W. Sun & Yu-Jen Wang & Min-Teh Yu, 2018. "Integrated Portfolio Risk Measure: Estimation and Asymptotics of Multivariate Geometric Quantiles," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 627-652, August.
    206. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    207. Rossi, Barbara & Sekhposyan, Tatevik, 2019. "Alternative tests for correct specification of conditional predictive densities," Journal of Econometrics, Elsevier, vol. 208(2), pages 638-657.
    208. Francesco Ravazzolo & Philip Rothman, 2011. "Oil and US GDP: A Real-Time out-of Sample Examination," Working Papers No 2/2011, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    209. Altug, Sumru & Çakmaklı, Cem, 2015. "Forecasting Inflation using Survey Expectations and Target Inflation: Evidence for Brazil and Turkey," CEPR Discussion Papers 10419, Centre for Economic Policy Research.
    210. Hasanov, Akram Shavkatovich & Shaiban, Mohammed Sharaf & Al-Freedi, Ajab, 2020. "Forecasting volatility in the petroleum futures markets: A re-examination and extension," Energy Economics, Elsevier, vol. 86(C).
    211. Kruse-Becher, Robinson & Letixerant, Philip, 2025. "Oil price expectations in explosive phases," Energy Economics, Elsevier, vol. 152(C).
    212. Laura Garcia‐Jorcano & Alfonso Novales, 2021. "Volatility specifications versus probability distributions in VaR forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 189-212, March.
    213. Jonathan Benchimol & André Fourçans, 2017. "Money and monetary policy in the Eurozone: an empirical analysis during crises," Post-Print hal-01525492, HAL.
    214. Manuel Landajo & Javier De Andrés & Pedro Lorca, 2008. "Measuring firm performance by using linear and non‐parametric quantile regressions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 227-250, April.
    215. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.
    216. Chudik, Alexander & Pesaran, Hashem, 2009. "Infinite-dimensional VARs and factor models," Working Paper Series 998, European Central Bank.
    217. Boneva, Lena & Cloyne, James & Weale, Martin & Wieladek, Tomasz, 2018. "Firms' Expectations of New Orders, Employment, Costs and Prices: Evidence from Micro Data," CEPR Discussion Papers 12722, Centre for Economic Policy Research.
    218. Gao, Yuyang & Wang, Jianzhou & Yang, Hufang, 2022. "A multi-component hybrid system based on predictability recognition and modified multi-objective optimization for ultra-short-term onshore wind speed forecasting," Renewable Energy, Elsevier, vol. 188(C), pages 384-401.
    219. Teodosio Perez-Amaral & Giampiero M. Gallo & Halbert White, 2003. "Flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)," Documentos de Trabajo del ICAE 0309, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    220. Li, Mengheng & Koopman, Siem Jan & Lit, Rutger & Petrova, Desislava, 2020. "Long-term forecasting of El Niño events via dynamic factor simulations," Journal of Econometrics, Elsevier, vol. 214(1), pages 46-66.
    221. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-172/4, Tinbergen Institute.
    222. Angela Capolongo & Claudia Pacella, 2019. "Forecasting inflation in the euro area: countries matter!," Temi di discussione (Economic working papers) 1224, Bank of Italy, Economic Research and International Relations Area.
    223. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    224. Avdis, Efstathios & Wachter, Jessica A., 2017. "Maximum likelihood estimation of the equity premium," Journal of Financial Economics, Elsevier, vol. 125(3), pages 589-609.
    225. Firmin Doko Tchatoka & Qazi Haque, 2023. "On bootstrapping tests of equal forecast accuracy for nested models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1844-1864, November.
    226. Leonardo N. Ferreira & Caio Garzeri & Diogo Guillen & Antônio Lima & Victor Monteiro, 2025. "The Not So Quiet Revolution: signal and noise in central bank communication," Working Papers Series 635, Central Bank of Brazil, Research Department.
    227. Pyun, Sungjune, 2019. "Variance risk in aggregate stock returns and time-varying return predictability," Journal of Financial Economics, Elsevier, vol. 132(1), pages 150-174.
    228. Chiang, I-Hsuan Ethan & Liao, Yin & Zhou, Qing, 2021. "Modeling the cross-section of stock returns using sensible models in a model pool," Journal of Empirical Finance, Elsevier, vol. 60(C), pages 56-73.
    229. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    230. Vasnev, Andrey & Liu, Chu-An, 2026. "Corrected Forecast Combinations," Working Papers BAWP-2026-01, University of Sydney Business School, Discipline of Business Analytics.
    231. González-Pla, Francisco & Lovreta, Lidija, 2022. "Modeling and forecasting firm-specific volatility: The role of asymmetry and long-memory," Finance Research Letters, Elsevier, vol. 48(C).
    232. Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2022. "Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    233. Garratt, Anthony & Lee, Kevin & Shields, Kalvinder, 2016. "Forecasting global recessions in a GVAR model of actual and expected output," International Journal of Forecasting, Elsevier, vol. 32(2), pages 374-390.
    234. Cheng, Mingmian, 2026. "Sparse heterogeneous auto-regressive model for volatility forecasting," Journal of Empirical Finance, Elsevier, vol. 87(C).
    235. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2024. "Comparing forecasting performance with panel data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 918-941.
    236. Ferraro, Domenico & Rogoff, Kenneth & Rossi, Barbara, 2015. "Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates," Journal of International Money and Finance, Elsevier, vol. 54(C), pages 116-141.
    237. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    238. Sung Hoon Choi, 2021. "Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia," Papers 2108.10250, arXiv.org, revised May 2022.
    239. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    240. Òscar Jordà & Alan M. Taylor, 2009. "The Carry Trade and Fundamentals: Nothing to Fear But FEER Itself," NBER Working Papers 15518, National Bureau of Economic Research, Inc.
    241. Garrón, Ignacio & Rodríguez-Caballero, Vladimir & Ruiz, Esther, 2026. "International factors and inflation risks," International Journal of Forecasting, Elsevier, vol. 42(3), pages 889-908.
    242. Luisa Bisaglia & Massimiliano Caporin & Matteo Grigoletto, 2025. "Forecasting time series by long-memory models for count data with an application to price jumps," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(3), pages 417-441, September.
    243. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    244. Rombouts, Jeroen & Stentoft, Lars & Violante, Franceso, 2014. "The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options," International Journal of Forecasting, Elsevier, vol. 30(1), pages 78-98.
    245. Baur, Dirk G. & Dichtl, Hubert & Drobetz, Wolfgang & Wendt, Viktoria-Sophie, 2020. "Investing in gold – Market timing or buy-and-hold?," International Review of Financial Analysis, Elsevier, vol. 71(C).
    246. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers (2017-2023 papers hosted by The National Institute of Economic and Social Research) ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    247. Alexander Chudik & M. Hashem Pesaran, 2014. "Theory and practice of GVAR modeling," Globalization Institute Working Papers 180, Federal Reserve Bank of Dallas.
    248. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    249. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    250. Mihaylov, George & Cheong, Chee Seng & Zurbruegg, Ralf, 2015. "Can security analyst forecasts predict gold returns?," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 237-246.
    251. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    252. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    253. Hajo Holzmann & Matthias Eulert, 2014. "The role of the information set for forecasting - with applications to risk management," Papers 1404.7653, arXiv.org.
    254. Zhang, Feipeng & Gao, Hongfu & Yuan, Di, 2024. "The asymmetric effect of G7 stock market volatility on predicting oil price volatility: Evidence from quantile autoregression model," Journal of Commodity Markets, Elsevier, vol. 35(C).
    255. Christophe Boucher & A. Jasinski & S. Tokpavi, 2023. "Conditional mean reversion of financial ratios and the predictability of returns," Post-Print hal-04352991, HAL.
    256. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2020. "Economic indicators and stock market volatility in an emerging economy," Economic Systems, Elsevier, vol. 44(2).
    257. Nikolsko-Rzhevskyy, Alex & Prodan, Ruxandra, 2012. "Markov switching and exchange rate predictability," International Journal of Forecasting, Elsevier, vol. 28(2), pages 353-365.
    258. Michael S. O’Doherty & N. E. Savin & Ashish Tiwari, 2016. "Evaluating Hedge Funds with Pooled Benchmarks," Management Science, INFORMS, vol. 62(1), pages 69-89, January.
    259. Pesaran, M.H. & Pick, A. & Timmermann, A., 2009. "Variable Selection and Inference for Multi-period Forecasting Problems," Cambridge Working Papers in Economics 0901, Faculty of Economics, University of Cambridge.
    260. Roque Montero & Javier García-Cicco, 2012. "Modelo y Pronóstico del Precio del Cobre: Un Enfoque de Cambio de Regímenes," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(2), pages 099-116, August.
    261. Siem Jan Koopman & Rutger Lit & André Lucas & Anne Opschoor, 2018. "Dynamic discrete copula models for high‐frequency stock price changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 966-985, November.
    262. Yuta Yamauchi & Yasuhiro Omori, 2018. "Multivariate Stochastic Volatility Model with Realized Volatilities and Pairwise Realized Correlations," Papers 1809.09928, arXiv.org, revised Mar 2019.
    263. Dimos S. Kambouroudis & David G. McMillan & Katerina Tsakou, 2021. "Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(10), pages 1618-1639, October.
    264. Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.
    265. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    266. Sandra Hanslin Grossmann & Rolf Scheufele, 2016. "Foreign PMIs: A reliable indicator for exports?," Working Papers 2016-01, Swiss National Bank.
    267. Zhou, Xinmiao & Qian, Huanhuan & Pérez-Rodríguez, Jorge. V. & González López-Valcárcel, Beatriz, 2020. "Risk dependence and cointegration between pharmaceutical stock markets: The case of China and the USA," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    268. Nima Nonejad, 2021. "Crude oil price point forecasts of the Norwegian GDP growth rate," Empirical Economics, Springer, vol. 61(5), pages 2913-2930, November.
    269. Jie Cheng, 2024. "Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3617-3643, December.
    270. Valentina Corradi & Norman Swanson, 2004. "Bootstrap Procedures for Recursive Estimation Schemes With Applications to Forecast Model Selection," Departmental Working Papers 200418, Rutgers University, Department of Economics.
    271. Emilio Zanetti Chini, 2017. "Generalizing Smooth Transition Autoregressions," DEM Working Papers Series 138, University of Pavia, Department of Economics and Management.
    272. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.
    273. Mihaela Bratu, 2011. "The Assessement Of Uncertainty In Predictions Determined By The Variables Aggregation," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(13), pages 1-31.
    274. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    275. Marcelo Fernandes & Marcelo Cunha Medeiros & MArcelo Scharth, 2007. "Modeling and predicting the CBOE market volatility index," Textos para discussão 548, Department of Economics PUC-Rio (Brazil).
    276. Todd E. Clark & Michael W. McCracken, 2004. "Improving forecast accuracy by combining recursive and rolling forecasts," Research Working Paper RWP 04-10, Federal Reserve Bank of Kansas City.
    277. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    278. Kin G. Olivares & Cristian Challu & Grzegorz Marcjasz & Rafal Weron & Artur Dubrawski, 2021. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," WORking papers in Management Science (WORMS) WORMS/21/07, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    279. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial conditions and density forecasts for US output and inflation," CReMFi Discussion Papers 1, CReMFi, School of Economics and Finance, QMUL.
    280. Buse, Rebekka & Görgen, Konstantin & Schienle, Melanie, 2025. "Predicting value at risk for cryptocurrencies with generalized random forests," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1199-1222.
    281. Heinisch, Katja & Lindner, Axel, 2018. "For how long do IMF forecasts of world economic growth stay up-to-date?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Latest ar, pages 1-6.
    282. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    283. Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2016. "Forecasting macroeconomic variables in data-rich environments," Economics Letters, Elsevier, vol. 138(C), pages 50-52.
    284. Massimo Guidolin & Giulia F. Panzeri, 2024. "Forecasting the CBOE VIX and SKEW Indices Using Heterogeneous Autoregressive Models," Forecasting, MDPI, vol. 6(3), pages 1-33, September.
    285. Fissler Tobias & Ziegel Johanna F., 2021. "On the elicitability of range value at risk," Statistics & Risk Modeling, De Gruyter, vol. 38(1-2), pages 25-46, January.
    286. Carlos A. Medel & Michael Pedersen & Pablo M. Pincheira, 2016. "The Elusive Predictive Ability of Global Inflation," International Finance, Wiley Blackwell, vol. 19(2), pages 120-146, June.
    287. Fei Su & Lei Wang, 2020. "Conditional Volatility Persistence and Realized Volatility Asymmetry: Evidence from the Chinese Stock Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(14), pages 3252-3269, November.
    288. Reichenbacher, Michael & Schuster, Philipp, 2022. "Size-adapted bond liquidity measures and their asset pricing implications," Journal of Financial Economics, Elsevier, vol. 146(2), pages 425-443.
    289. Massimo Guidolin & Francesco Chincoli, 2017. "Linear and Nonlinear Predictability in Investment Style Factors: Multivariate Evidence," BAFFI CAREFIN Working Papers 1754, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    290. Laurent Ferrara & Massimiliano Marcellino & Matteo Mogliani, 2012. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," Working papers 383, Banque de France.
    291. Timo Dimitriadis & Marius Puke, 2026. "Statistical Inference for Score Decompositions," Papers 2603.04275, arXiv.org, revised Sep 2026.
    292. Audrino, Francesco & Hu, Yujia, 2011. "Volatility Forecasting: Downside Risk, Jumps and Leverage Effect," Economics Working Paper Series 1138, University of St. Gallen, School of Economics and Political Science.
    293. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    294. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    295. Coroneo, Laura & Iacone, Fabrizio & Profumo, Fabio, 2024. "Survey density forecast comparison in small samples," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1486-1504.
    296. Claus, Edda & Lucey, Brian M., 2012. "Equity market integration in the Asia Pacific region: Evidence from discount factors," Research in International Business and Finance, Elsevier, vol. 26(2), pages 137-163.
    297. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    298. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    299. Robinson Durán & Evelyn Garrido & Carolina Godoy & Juan de Dios Tena, 2012. "Predicción de la inflación en México con modelos desagregados por componente," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 27(1), pages 133-167.
    300. Andrea Bucci & Giulio Palomba & Eduardo Rossi, 2019. "Does macroeconomics help in predicting stock markets volatility comovements? A nonlinear approach," Working Papers 440, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    301. Stamer, Vincent, 2024. "Thinking outside the container: A sparse partial least squares approach to forecasting trade flows," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1336-1358.
    302. Galbraith, John W. & KI[#x1e63]Inbay, Turgut, 2005. "Content horizons for conditional variance forecasts," International Journal of Forecasting, Elsevier, vol. 21(2), pages 249-260.
    303. Boriss Siliverstovs & Konstantin A. Kholodilin, 2006. "On Selection of Components for a Diffusion Index Model: It's not the Size, It's How You Use It," Discussion Papers of DIW Berlin 598, DIW Berlin, German Institute for Economic Research.
    304. Peter Reinhard Hansen & Allan Timmermann, 2012. "Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics," CREATES Research Papers 2012-45, Department of Economics and Business Economics, Aarhus University.
    305. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2023. "Comparing forecasting performance in cross-sections," Journal of Econometrics, Elsevier, vol. 237(2).
    306. Jörg Breitung & Malte Knüppel, 2021. "How far can we forecast? Statistical tests of the predictive content," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 369-392, June.
    307. Pincheira, Pablo & Hardy, Nicolas, 2020. "The Mean Squared Prediction Error Paradox: A summary," MPRA Paper 105020, University Library of Munich, Germany.
    308. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
    309. Michel Ferreira Cardia Haddad & Szabolcs Blazsek & Philip Arestis & Franz Fuerst & Hsia Hua Sheng, 2023. "The two-component Beta-t-QVAR-M-lev: a new forecasting model," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 379-401, December.
    310. Marc Joëts, 2015. "Heterogeneous beliefs, regret, and uncertainty: The role of speculation in energy price dynamics," Post-Print hal-01609889, HAL.
    311. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    312. Todd E. Clark & Kenneth D. West, 2005. "Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference," NBER Technical Working Papers 0305, National Bureau of Economic Research, Inc.
    313. Manzan, Sebastiano & Zerom, Dawit, 2009. "Are Macroeconomic Variables Useful for Forecasting the Distribution of U.S. Inflation?," MPRA Paper 14387, University Library of Munich, Germany.
    314. Jinbo Cai & Wenze Li & Wenjie Wang, 2025. "Electricity Market Predictability: Virtues of Machine Learning and Links to the Macroeconomy," Papers 2507.07477, arXiv.org.
    315. Daan Schoemaker & André Lucas & Anne Opschoor, 2025. "Conditional Fat Tails and Scale Dynamics for Intraday Discrete Price Changes," Tinbergen Institute Discussion Papers 25-039/III, Tinbergen Institute.
    316. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    317. Seiler, Christian & Heumann, Christian, 2013. "Microdata imputations and macrodata implications: Evidence from the Ifo Business Survey," Economic Modelling, Elsevier, vol. 35(C), pages 722-733.
    318. Pablo Pincheira B. & Nicolás Fernández, 2011. "Jaque Mate a las Proyecciones de Consenso," Working Papers Central Bank of Chile 630, Central Bank of Chile.
    319. Jari Hännikäinen, 2016. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Working Papers 1692, Tampere University, Faculty of Management and Business, Economics.
    320. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.
    321. Pincheira, Pablo, 2017. "A Power Booster Factor for Out-of-Sample Tests of Predictability," MPRA Paper 77027, University Library of Munich, Germany.
    322. Ahmed, Hanan, 2022. "Extreme value statistics using related variables," Other publications TiSEM 246f0f13-701c-4c0d-8e09-e, Tilburg University, School of Economics and Management.
    323. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2016. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," CESifo Working Paper Series 5759, CESifo.
    324. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    325. Fredy Pokou & Jules Sadefo Kamdem & Kpante Emmanuel Gnandi, 2026. "Predictive Accuracy versus Interpretability in Energy Markets: A Copula-Enhanced TVP-SVAR Analysis," Papers 2601.19321, arXiv.org.
    326. Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Papers 2009-W03, Economics Group, Nuffield College, University of Oxford.
    327. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.
    328. Hong, Yongmiao & Li, Haitao & Zhao, Feng, 2007. "Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates," Journal of Econometrics, Elsevier, vol. 141(2), pages 736-776, December.
    329. A. Amendola & V. Candila, 2016. "Evaluation of volatility predictions in a VaR framework," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 695-709, May.
    330. Nima Zarrabi & Stuart Snaith & Jerry Coakley, 2022. "Exchange rate forecasting using economic models and technical trading rules," The European Journal of Finance, Taylor & Francis Journals, vol. 28(10), pages 997-1018, July.
    331. Gary Koop & Simon M. Potter, 2003. "Forecasting in large macroeconomic panels using Bayesian Model Averaging," Staff Reports 163, Federal Reserve Bank of New York.
    332. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    333. Elena Andreou & Constantinos Kourouyiannis & Andros Kourtellos, 2012. "Volatility Forecast Combinations using Asymmetric Loss Functions," University of Cyprus Working Papers in Economics 07-2012, University of Cyprus Department of Economics.
    334. Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian & Wohar, Mark E., 2017. "Do terror attacks predict gold returns? Evidence from a quantile-predictive-regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 276-284.
    335. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    336. Sermpinis, Georgios & Stasinakis, Charalampos & Hassanniakalager, Arman, 2017. "Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds," European Journal of Operational Research, Elsevier, vol. 263(2), pages 540-558.
    337. Zografopoulos, Lazaros & Iannino, Maria Chiara & Psaradellis, Ioannis & Sermpinis, Georgios, 2025. "Industry return prediction via interpretable deep learning," European Journal of Operational Research, Elsevier, vol. 321(1), pages 257-268.
    338. Ellington, Michael & Fu, Xi & Zhu, Yunyi, 2023. "Real estate illiquidity and returns: A time-varying regional perspective," International Journal of Forecasting, Elsevier, vol. 39(1), pages 58-72.
    339. Yuhan Cheng & Heyang Zhou & Yanchu Liu, 2025. "Large Language Models and Futures Price Factors in China," Papers 2509.23609, arXiv.org.
    340. Philippe Goulet Coulombe, 2026. "Quantifying the Risk-Return Tradeoff in Forecasting," Papers 2605.09712, arXiv.org.
    341. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    342. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    343. Pesaran, M. Hashem & Pick, Andreas & Timmermann, Allan, 2011. "Variable selection, estimation and inference for multi-period forecasting problems," Journal of Econometrics, Elsevier, vol. 164(1), pages 173-187, September.
    344. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    345. Gourieroux, Christian & Jasiak, Joann, 2025. "Long-run risk in stationary vector autoregressive models," Journal of Econometrics, Elsevier, vol. 248(C).
    346. Mascarenhas, Maria Margarida & De Blauwe, Jilles & Amelin, Mikael & Kazmi, Hussain, 2026. "Leveraging asynchronous cross-border market data for improved day-ahead electricity price forecasting in European markets," Applied Energy, Elsevier, vol. 404(C).
    347. Pablo Pincheira & Carlos A. Medel, 2012. "Forecasting Inflation with a Simple and Accurate Benchmark: a Cross-Country Analysis," Working Papers Central Bank of Chile 677, Central Bank of Chile.
    348. Psaradellis, Ioannis & Sermpinis, Georgios, 2016. "Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1268-1283.
    349. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    350. Robert J. Bianchi & Michael E. Drew & Timothy Whittaker, 2016. "The Predictive Performance of Asset Pricing Models: Evidence from the Australian Securities Exchange," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-18, December.
    351. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.
    352. De Gooijer, Jan G. & Henter, Gustav Eje & Yuan, Ao, 2022. "Kernel-based hidden Markov conditional densities," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    353. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    354. Apergis, Nicholas & Mustafa, Ghulam & Malik, Shafaq, 2023. "The role of the COVID-19 pandemic in US market volatility: Evidence from the VIX index," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 27-35.
    355. Haroon Mumtaz & Nitin Kumar, 2012. "An application of data-rich environment for policy analysis of the Indian economy," Joint Research Papers 2, Centre for Central Banking Studies, Bank of England.
    356. Marco Lombardi & Mr. Raphael A Espinoza & Fabio Fornari, 2009. "The Role of Financial Variables in Predicting Economic Activity in the Euro Area," IMF Working Papers 2009/241, International Monetary Fund.
    357. Chernov, Mikhail & Mueller, Philippe, 2012. "The term structure of inflation expectations," Journal of Financial Economics, Elsevier, vol. 106(2), pages 367-394.
    358. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    359. Boriss Siliverstovs, 2015. "Dissecting Models' Forecasting Performance," KOF Working papers 15-397, KOF Swiss Economic Institute, ETH Zurich.
    360. Qiao, Fang & Xu, Lai & Zhang, Xiaoyan & Zhou, Hao, 2024. "Variance risk premiums in emerging markets," Journal of Banking & Finance, Elsevier, vol. 167(C).
    361. Boucher, C. & Jasinski, A. & Tokpavi, S., 2023. "Conditional mean reversion of financial ratios and the predictability of returns," Journal of International Money and Finance, Elsevier, vol. 137(C).
    362. Pagano Patrizio & Pisani Massimiliano, 2009. "Risk-Adjusted Forecasts of Oil Prices," The B.E. Journal of Macroeconomics, De Gruyter, vol. 9(1), pages 1-28, June.
    363. Pablo Pincheira Brown & Álvaro García Marín, 2009. "Forecasting Inflation in Chile With an Accurate Benchmark," Working Papers Central Bank of Chile 514, Central Bank of Chile.
    364. Hännikäinen, Jari, 2014. "The mortgage spread as a predictor of real-time economic activity," MPRA Paper 58360, University Library of Munich, Germany.
    365. Uniejewski, Bartosz, 2025. "Smoothing quantile regression averaging: A new approach to probabilistic forecasting of electricity prices," Journal of Commodity Markets, Elsevier, vol. 39(C).
    366. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    367. Ben Amor, Souhir & Ziel, Florian, 2026. "Recurrent neural networks with linear structures for electricity price forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 231(C).
    368. Nicholas Fawcett & George Kapetanios & James Mitchell & Simon Price, 2014. "Generalised Density Forecast Combinations," CAMA Working Papers 2014-24, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    369. Krenar Avdulaj & Jozef Barunik, 2013. "Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data," Papers 1307.5981, arXiv.org, revised Feb 2015.
    370. Tsionas, Mike & Ghalayini, Aya & Izzeldin, Marwan & Trapani, Lorenzo, 2026. "A dynamic state-space HAR model," Journal of Econometrics, Elsevier, vol. 256(PB).
    371. Siem Jan Koopman & Rutger Lit, 2012. "A Dynamic Bivariate Poisson Model for Analysing and Forecasting Match Results in the English Premier League," Tinbergen Institute Discussion Papers 12-099/III, Tinbergen Institute.
    372. Buncic, Daniel & Gisler, Katja I.M., 2017. "The role of jumps and leverage in forecasting volatility in international equity markets," Journal of International Money and Finance, Elsevier, vol. 79(C), pages 1-19.
    373. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    374. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
    375. Yuta Kurose, 2022. "Bayesian GARCH modeling for return and range," Economics Bulletin, AccessEcon, vol. 42(3), pages 1717-1727.
    376. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
    377. Lai, Yu-Sheng, 2023. "Economic evaluation of dynamic hedging strategies using high-frequency data," Finance Research Letters, Elsevier, vol. 57(C).
    378. Pablo Pincheira Brown & Nicolás Hardy, 2024. "The mean squared prediction error paradox," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2298-2321, September.
    379. Halil Ibrahim Gunduz & Furkan Emirmahmutoglu & M. Eray Yucel, 2025. "A New Look at Cross-Country Aggregation in the Global VAR Approach: Theory and Monte Carlo Simulation," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 21-67, January.
    380. Ghent, Andra, 2006. "Comparing Models of Macroeconomic Fluctuations: How Big Are the Differences?," MPRA Paper 180, University Library of Munich, Germany.
    381. Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
    382. Chacon, David Ugarte & Lee, Seohyun & Park, Jaehyuk, 2026. "An explainable machine learning model for consumer credit scoring in Mexico," Emerging Markets Review, Elsevier, vol. 71(C).
    383. Michael P. Clements & David F. Hendry, 2005. "Guest Editors’ Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.
    384. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2010. "Out-of-sample comparison of copula specifications in multivariate density forecasts," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1596-1609, September.
    385. Cordeiro, Werley & Caldeira, João F. & Moura, Guilherme V., 2025. "Forecasting the Brazilian yield curve using macroeconomics expectations and time-varying volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 104(C).
    386. Chao, Shih-Wei, 2016. "Do economic variables improve bond return volatility forecasts?," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 10-26.
    387. Christian Gourieroux & Wei Liu, 2009. "Control and Out‐of‐Sample Validation of Dependent Risks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(3), pages 683-707, September.
    388. Marie Bessec & Julien Fouquau & Sophie Méritet, 2014. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Post-Print hal-01502835, HAL.
    389. Raffaella Giacomini & Barbara Rossi, 2006. "How Stable is the Forecasting Performance of the Yield Curve for Output Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 783-795, December.
    390. Rebekka Buse & Konstantin Gorgen & Melanie Schienle, 2022. "Predicting Value at Risk for Cryptocurrencies With Generalized Random Forests," Papers 2203.08224, arXiv.org, revised Dec 2024.
    391. Katelijne A.E. Carbonez & Van Thi Tuong Nguyen & Piet Sercu, 2011. "Hedging with Two Futures Contracts: Simplicity Pays," European Financial Management, European Financial Management Association, vol. 17(5), pages 806-834, November.
    392. Yo Joong Choe & Aaditya Ramdas, 2024. "Comparing Sequential Forecasters," Operations Research, INFORMS, vol. 72(4), pages 1368-1387, July.
    393. Hofmann, Boris, 2006. "Do monetary indicators (still) predict euro area inflation?," Discussion Paper Series 1: Economic Studies 2006,18, Deutsche Bundesbank.
    394. Casey, Eddie, 2021. "Are professional forecasters overconfident?," International Journal of Forecasting, Elsevier, vol. 37(2), pages 716-732.
    395. Tae-Hwy Lee & Dingli Wang, 2026. "Median-Anchored Adjustment of Joint VaR--ES Forecasts," Working Papers 202604, University of California at Riverside, Department of Economics.
    396. Chevillon, Guillaume & Mavroeidis, Sophocles, 2011. "Learning generates Long Memory," ESSEC Working Papers WP1113, ESSEC Research Center, ESSEC Business School.
    397. Nonejad, Nima, 2021. "Predicting equity premium by conditioning on macroeconomic variables: A prediction selection strategy using the price of crude oil," Finance Research Letters, Elsevier, vol. 41(C).
    398. Timo Dimitriadis & Yannick Hoga, 2026. "Systemic Risk Surveillance," Papers 2601.08598, arXiv.org.
    399. Taylor, James W., 2021. "Evaluating quantile-bounded and expectile-bounded interval forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 800-811.
    400. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    401. Naraidoo, Ruthira & Paya, Ivan, 2012. "Forecasting monetary policy rules in South Africa," International Journal of Forecasting, Elsevier, vol. 28(2), pages 446-455.
    402. Semih Emre Cekin & Menelik S. Geremew & Hardik Marfatia, 2019. "Monetary policy co-movement and spillover of shocks among BRICS economies," Applied Economics Letters, Taylor & Francis Journals, vol. 26(15), pages 1253-1263, September.
    403. Matteo Luciani, 2011. "Forecasting with Approximate Dynamic Factor Models: the Role of Non-Pervasive Shocks," Working Papers ECARES ECARES 2011‐022, ULB -- Universite Libre de Bruxelles.
    404. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
    405. Ruthira Naraidoo & Kasai Ndahiriwe, 2010. "Financial asset prices, linear and nonlinear policy rules. An In-sample assessment of the reaction function of the South African Reserve Bank," Working Papers 201006, University of Pretoria, Department of Economics.
    406. Matteo Mogliani & Florens Odendahl, 2025. "Density forecast transformations," Working Papers 2511, Banco de España.
    407. Park, Timothy A. & Gubanova, Tatiana & Lohr, Luanne & Escalante, Cesar L., 2005. "Forecasting Organic Food Prices: Testing and Evaluating Conditional Predictive Ability," 2005 Annual meeting, July 24-27, Providence, RI 19412, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    408. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.
    409. Nader Trabelsi & Aviral Kumar Tiwari, 2023. "CO2 Emission Allowances Risk Prediction with GAS and GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 775-805, February.
    410. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    411. Kozhan, Roman & Salmon, Mark, 2012. "The information content of a limit order book: The case of an FX market," Journal of Financial Markets, Elsevier, vol. 15(1), pages 1-28.
    412. Pablo Pincheira, 2012. "A Joint Test of Superior Predictive Ability for Chilean Inflation Forecasts," Journal Econom a Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(3), pages 04-39, December.
    413. Jussi Klemelä, 2026. "Tests and graphical tools for the comparison of time series predictors," Computational Statistics, Springer, vol. 41(1), pages 1-28, January.
    414. Javier Pereda, 2011. "Estimación de la tasa natural de interés para Perú: un enfoque financiero," Monetaria, CEMLA, vol. 0(4), pages 429-459, octubre-d.
    415. Hännikäinen, Jari, 2014. "Multi-step forecasting in the presence of breaks," MPRA Paper 55816, University Library of Munich, Germany.
    416. Bae, Juhee, 2024. "Factor-augmented forecasting in big data," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1660-1688.
    417. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    418. Taylor, Nick, 2019. "Forecasting returns in the VIX futures market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1193-1210.
    419. Piersilvio De Bortoli & Davide Ferrari & Francesco Ravazzolo & Luca Rossini, 2026. "Model selection confidence sets for time series models with applications to electricity load data," Papers 2602.16527, arXiv.org.
    420. Todd E. Clark & Fabian Krueger & Francesco Ravazzolo, 2015. "Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts," Working Papers (Old Series) 1439, Federal Reserve Bank of Cleveland.
    421. Segnon, Mawuli & Gupta, Rangan & Wilfling, Bernd, 2024. "Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks," International Journal of Forecasting, Elsevier, vol. 40(1), pages 29-43.
    422. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    423. Aslanidis, Nektarios & Martinez, Oscar, 2021. "Correlation regimes in international equity and bond returns," Economic Modelling, Elsevier, vol. 97(C), pages 397-410.
    424. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2012. "Multivariate Rotated ARCH Models," Economics Papers 2012-W01, Economics Group, Nuffield College, University of Oxford.
    425. Yuru Sun & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Gael M. Martin, 2023. "Optimal probabilistic forecasts for risk management," Papers 2303.01651, arXiv.org.
    426. Jonathan Alexander Muñoz-Martínez & David Orozco & Mario A. Ramos-Veloza, 2023. "Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia," Borradores de Economia 1256, Banco de la Republica de Colombia.
    427. Michael B. Gordy & Alexander J. McNeil, 2017. "Spectral backtests of forecast distributions with application to risk management," Papers 1708.01489, arXiv.org, revised Jul 2019.
    428. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    429. Emilio Zanetti Chini, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," CREATES Research Papers 2018-13, Department of Economics and Business Economics, Aarhus University.
    430. Jung, Alexander & El-Shagi, Makram & Giesen, Sebastian, 2013. "Does Central Bank Staff Beat Private Forecasters?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79925, Verein für Socialpolitik / German Economic Association.
    431. Carlos A. Medel Vera, 2011. "¿Akaike o Schwarz? ¿Cuál utilizar para predecir el PIB chileno?," Monetaria, CEMLA, vol. 0(4), pages 591-615, octubre-d.
    432. Carlo A. Favero & Arie E. Gozluklu & Haoxi Yang, 2016. "Demographics and the Behavior of Interest Rates," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 64(4), pages 732-776, November.
    433. Casini, Alessandro & Perron, Pierre, 2024. "Prewhitened long-run variance estimation robust to nonstationarity," Journal of Econometrics, Elsevier, vol. 242(1).
    434. Michele Berardi & Jaqueson K. Galimberti, 2012. "On the plausibility of adaptive learning in macroeconomics: A puzzling conflict in the choice of the representative algorithm," Centre for Growth and Business Cycle Research Discussion Paper Series 177, Economics, The University of Manchester.
    435. Chris Kirby, 2025. "Using Daily Stock Returns to Estimate the Unconditional and Conditional Variances of Lower-Frequency Stock Returns," Risks, MDPI, vol. 13(10), pages 1-17, October.
    436. Fei Su, 2018. "Essays on Price Discovery and Volatility Dynamics in the Foreign Exchange Market," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2018, January-A.
    437. Qiuqi Wang & Ruodu Wang & Johanna Ziegel, 2026. "E-backtesting," Management Science, INFORMS, vol. 72(6), pages 4952-4973, June.
    438. Cecilia Frale, "undated". "Do Surveys Help in Macroeconomic Variables Disaggregation and Estimation?," Working Papers wp2008-2, Department of the Treasury, Ministry of the Economy and of Finance.
    439. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    440. Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    441. Oguzhan Akgun & Alain Pirotte & Giovanni Urga & Zhenlin Yang, 2020. "Equal Predictive Ability Tests Based on Panel Data with Applications to OECD and IMF Forecasts," Papers 2003.02803, arXiv.org, revised Feb 2023.
    442. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    443. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    444. Travis Berge & Òscar Jordà & Alan M. Taylor, 2010. "Currency Carry Trades," NBER Chapters, in: NBER International Seminar on Macroeconomics 2010, pages 357-387, National Bureau of Economic Research, Inc.
    445. Santos, André A. P. & Nogales, Francisco J. & Ruiz Ortega, Esther, 2009. "Comparing univariate and multivariate models to forecast portfolio value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws097222, Universidad Carlos III de Madrid. Departamento de Estadística.
    446. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    447. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, Center for Economic and Financial Research (CEFIR).
    448. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    449. Mansoor Maitah & Daniel Toth & Elena Kuzmenko & Karel r dl & Helena Rezbov & Petra nov, 2016. "Forecast of Employment in Switzerland: The Macroeconomic View," International Journal of Economics and Financial Issues, International Journal of Economics and Financial Issues, vol. 6(1), pages 132-138.
    450. Ulm, M. & Hambuckers, J., 2022. "Do interest rate differentials drive the volatility of exchange rates? Evidence from an extended stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 125-148.
    451. Cees Diks & Valentyn Panchenko & Oleg Sokolinskiy, & Dick van Dijk, 2013. "Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support," Tinbergen Institute Discussion Papers 13-061/III, Tinbergen Institute.
    452. Maria Gonzalez-Perez & Alfonso Novales, 2011. "The information content in a volatility index for Spain," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(2), pages 185-216, June.
    453. Avino, Davide & Nneji, Ogonna, 2014. "Are CDS spreads predictable? An analysis of linear and non-linear forecasting models," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 262-274.
    454. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    455. Javier Contreras-Reyes & Byron Idrovo, 2011. "En busca de un modelo Benchmark univariado para predecir la tasa de desempleo," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID.
    456. Medel, Carlos & Camilleri, Gilmour & Hsu, Hsiang-Ling & Kania, Stefan & Touloumtzoglou, Miltiadis, 2015. "Robustness in Foreign Exchange Rate Forecasting Models: Economics-based Modelling After the Financial Crisis," MPRA Paper 65290, University Library of Munich, Germany.
    457. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    458. Leonardo N. Ferreira, 2021. "Forecasting with VAR-teXt and DFM-teXt Models:exploring the predictive power of central bank communication," Working Papers Series 559, Central Bank of Brazil, Research Department.
    459. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    460. Giovanni De Luca & Giampiero M. Gallo & Danilo Carità, 2017. "Evaluating Combined Forecasts for Realized Volatility Using Asymmetric Loss Functions," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(2), pages 99-111, December.
    461. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
    462. García-Verdú Santiago & Ramos Francia Manuel & Sánchez-Martínez Manuel, 2018. "TIIE-28 Swaps as Risk-Adjusted Forecasts of Monetary Policy in Mexico," Working Papers 2018-16, Banco de México.
    463. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.
    464. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.
    465. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    466. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
    467. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    468. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
    469. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    470. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
    471. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    472. Opschoor, Anne & van Dijk, Dick & van der Wel, Michel, 2014. "Predicting volatility and correlations with Financial Conditions Indexes," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 435-447.
    473. Jung, Alexander & El-Shagi, Makram & Giesen, Sebastian, 2014. "Does the federal reserve staff still beat private forecasters?," Working Paper Series 1635, European Central Bank.
    474. Daniel Borup & Martin Thyrsgaard, 2017. "Statistical tests for equal predictive ability across multiple forecasting methods," CREATES Research Papers 2017-19, Department of Economics and Business Economics, Aarhus University.
    475. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    476. Gonzalo Calvo & Miguel Ricaurte, 2012. "Indicadores Sintéticos para la Proyección de Imacec en Chile," Working Papers Central Bank of Chile 656, Central Bank of Chile.
    477. Hännikäinen, Jari, 2014. "Zero lower bound, unconventional monetary policy and indicator properties of interest rate spreads," MPRA Paper 56737, University Library of Munich, Germany.
    478. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2010. "Does the macroeconomy predict U.K. asset returns in a nonlinear fashion? comprehensive out-of-sample evidence," Working Papers 2010-039, Federal Reserve Bank of St. Louis.
    479. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    480. Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting the French index of industrial production: A comparison from bridge and factor models," Economic Modelling, Elsevier, vol. 29(6), pages 2174-2182.
    481. Augustyniak, Maciej & Badescu, Alexandru & Bégin, Jean-François & Jayaraman, Sarath Kumar, 2025. "A general option pricing framework for affine fractionally integrated models," Journal of Banking & Finance, Elsevier, vol. 171(C).
    482. Chen, Yan & Luo, Qiong & Zhang, Feipeng, 2025. "Systemic risk and network effects in RCEP financial markets: Evidence from the TEDNQR model," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
    483. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    484. Dovern, Jonas & Ziegler, Christina, 2008. "Predicting growth rates and recessions: assessing US leading indicators under real-time conditions," Kiel Working Papers 1397, Kiel Institute for the World Economy.
    485. Almeida, Caio & Ardison, Kym & Kubudi, Daniela, 2014. "Approximating Risk Premium on a Parametric Arbitrage-free Term Structure Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 34(2), November.
    486. Ellington, Michael & Stamatogiannis, Michalis P. & Zheng, Yawen, 2022. "A study of cross-industry return predictability in the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 83(C).
    487. Qureshi, Fiza & Qureshi, Saba & Ismail, Izlin & Yarovaya, Larisa, 2025. "Unlocking economic insights: ESG integration, market dynamics and sustainable transitions," Energy Economics, Elsevier, vol. 145(C).
    488. Qitong Chen & Shuwen Lai, 2026. "Self-normalized tests for multistep conditional predictive ability," Papers 2605.07404, arXiv.org.
    489. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    490. Pablo Pincheira & Andrés Gatty, 2016. "Forecasting Chilean inflation with international factors," Empirical Economics, Springer, vol. 51(3), pages 981-1010, November.
    491. Pablo Pincheira & Roberto Álvarez, 2012. "Evaluation of Short Run Inflation Forecasts in Chile," Working Papers Central Bank of Chile 674, Central Bank of Chile.
    492. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Predictive ability tests with possibly overlapping models," Journal of Econometrics, Elsevier, vol. 241(1).
    493. Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pronósticos para una economía menos volátil: El caso colombiano," Borradores de Economia 821, Banco de la Republica de Colombia.
    494. Koo, Bonsoo & Anderson, Heather M. & Seo, Myung Hwan & Yao, Wenying, 2020. "High-dimensional predictive regression in the presence of cointegration," Journal of Econometrics, Elsevier, vol. 219(2), pages 456-477.
    495. Marcos à lvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez, 2019. "Estimating the economic impact of a political conflict on tourism: The case of the Catalan separatist challenge," Tourism Economics, , vol. 25(1), pages 34-50, February.
    496. Didier Nibbering, 2024. "A high‐dimensional multinomial logit model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 481-497, April.
    497. Tae-Hwy Lee & Yong Bao & Burak Saltoğlu, 2007. "Comparing density forecast models Previous versions of this paper have been circulated with the title, 'A Test for Density Forecast Comparison with Applications to Risk Management' since October 2003; see Bao et al. (2004)," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 203-225.
    498. Sergii Pypko, 2015. "Volatility Forecast in Crises and Expansions," JRFM, MDPI, vol. 8(3), pages 1-26, August.
    499. Labonne, Paul & Thorsrud, Leif Anders, 2026. "Risky news and credit market sentiment," Journal of Economic Dynamics and Control, Elsevier, vol. 187(C).
    500. Alain-Philippe Fortin & Jean-Guy Simonato & Georges Dionne, 2018. "Forecasting Expected Shortfall: Should we use a Multivariate Model for Stock Market Factors?," Working Papers 18-4, HEC Montreal, Canada Research Chair in Risk Management.
    501. Michael D. Bauer & Glenn D. Rudebusch, 2019. "Interest Rates Under Falling Stars," Working Paper Series 2017-16, Federal Reserve Bank of San Francisco.
    502. Duc Do, Nguyen, 2024. "Money/asset ratio as a predictor of inflation," The Quarterly Review of Economics and Finance, Elsevier, vol. 97(C).
    503. Hubert Dichtl, 2020. "Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?," Review of Financial Economics, John Wiley & Sons, vol. 38(2), pages 352-378, April.
    504. Yu‐Sheng Lai, 2018. "Estimation of the optimal futures hedge ratio for equity index portfolios using a realized beta generalized autoregressive conditional heteroskedasticity model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(11), pages 1370-1390, November.
    505. Feng, Jiabao & Wang, Yudong & Yin, Libo, 2017. "Oil volatility risk and stock market volatility predictability: Evidence from G7 countries," Energy Economics, Elsevier, vol. 68(C), pages 240-254.
    506. Steven Trypsteen, 2014. "The Importance of a Time-Varying Variance and Cross-Country Interactions in Forecast Models," Discussion Papers 2014/15, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    507. Brent Meyer & Guhan Venkatu, 2014. "Trimmed-Mean Inflation Statistics: Just Hit the One in the Middle," FRB Atlanta Working Paper 2014-3, Federal Reserve Bank of Atlanta.
    508. Anne Opschoor & Dewi Peerlings & Luca Rossini & Andre Lucas, 2024. "Density Forecasting for Electricity Prices under Tail Heterogeneity with the t-Riesz Distribution," Tinbergen Institute Discussion Papers 24-049/III, Tinbergen Institute.
    509. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    510. Kawakami, Kei, 2013. "Conditional forecast selection from many forecasts: An application to the Yen/Dollar exchange rate," Journal of the Japanese and International Economies, Elsevier, vol. 28(C), pages 1-18.
    511. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    512. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    513. Barrera, Carlos, 2013. "El sistema de predicción desagregada: Una evaluación de las proyecciones de inflación 2006-2011," Working Papers 2013-009, Banco Central de Reserva del Perú.
    514. Jana Eklund & George Kapetanios & Simon Price, 2013. "Robust Forecast Methods and Monitoring during Structural Change," Manchester School, University of Manchester, vol. 81, pages 3-27, October.
    515. Watanabe, Toshiaki & Nakajima, Jouchi, 2023. "High-frequency realized stochastic volatility model," Discussion paper series HIAS-E-127, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    516. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2015. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Tinbergen Institute Discussion Papers 15-140/III, Tinbergen Institute, revised 19 Apr 2017.
    517. Michael ARTIS & Ana Beatriz C. GALVÃO & Massimiliano MARCELLINO, 2003. "The transmission mechanism in a changing world," Economics Working Papers ECO2003/18, European University Institute.
    518. Shovon Sengupta & Sunny Kumar Singh & Tanujit Chakraborty, 2025. "Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks," Papers 2510.23347, arXiv.org.
    519. Nonejad, Nima, 2023. "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 91-122.
    520. Samuel W. Malone & Robert B. Gramacy & Enrique Ter Horst, 2016. "Timing Foreign Exchange Markets," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
    521. Arbués, Ignacio & Matilla-García, Mariano, 2024. "Multibenchmark reality checks," Economic Modelling, Elsevier, vol. 140(C).
    522. Thomadakis, Apostolos, 2016. "Do Combination Forecasts Outperform the Historical Average? Economic and Statistical Evidence," MPRA Paper 71589, University Library of Munich, Germany.
    523. Thomas Adjei Kuffour & Peterson Owusu Junior & Patrick Kwashie Akorsu, 2025. "Modeling value-at-risk for green bonds and clean energy investments," Risk Management, Palgrave Macmillan, vol. 27(4), pages 1-26, December.
    524. Serena Ng & Jonathan H. Wright, 2013. "Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1120-1154, December.
    525. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    526. Pablo Pincheira & Mauricio Calani, 2009. "Communicational Bias In Monetary Policy: Can Words Forecast Deeds?," Working Papers Central Bank of Chile 526, Central Bank of Chile.
    527. Chen, Cathy Yi-Hsuan & Chiang, Thomas C. & Härdle, Wolfgang Karl, 2018. "Downside risk and stock returns in the G7 countries: An empirical analysis of their long-run and short-run dynamics," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 21-32.
    528. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
    529. Todd E. Clark & Michael W. McCracken, 2007. "Tests of equal predictive ability with real-time data," Research Working Paper RWP 07-06, Federal Reserve Bank of Kansas City.
    530. Tsiotas, Georgios, 2012. "On generalised asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 151-172, January.
    531. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    532. Gabriel Rodríguez, 2016. "Modeling Latin-American Stock and Forex Markets Volatility: Empirical Application of a Model with Random Level Shifts and Genuine Long Memory [Modelando la volatilidad de los mercados bursátiles y cambiarios en América Latina: Aplicación empírica de ," Documentos de Trabajo / Working Papers 2016-416, Departamento de Economía - Pontificia Universidad Católica del Perú.
    533. Anthony Garratt & James Mitchell & Shaun P. Vahey, 2009. "Measuring output gap uncertainty," Reserve Bank of New Zealand Discussion Paper Series DP2009/15, Reserve Bank of New Zealand.
    534. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    535. Jaemin Son & Doojin Ryu, 2025. "Energy price shocks and stock market volatility in an energy-importing country," Energy & Environment, , vol. 36(8), pages 3737-3769, December.
    536. Jie Cheng, 2023. "Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies," Empirical Economics, Springer, vol. 65(2), pages 899-924, August.
    537. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    538. Luisa Bisaglia & Matteo Grigoletto, 2021. "A new time-varying model for forecasting long-memory series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 139-155, March.
    539. Todd E. Clark & Michael W. McCracken, 2011. "Tests of equal forecast accuracy for overlapping models," Working Papers 2011-024, Federal Reserve Bank of St. Louis.
    540. Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
    541. Marie Kratz & Yen H. Lok & Alexander J McNeil, 2016. "Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall," Papers 1611.04851, arXiv.org.
    542. Chu, Ba, 2011. "Recovering copulas from limited information and an application to asset allocation," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1824-1842, July.
    543. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    544. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    545. Emiliano Basco & Diego Elías & Maximiliano Gómez Aguirre & Luciana Pastore, 2025. "Soybean yield prediction in Argentina using climate data," BIS Working Papers 1278, Bank for International Settlements.
    546. Dimitriadis, Timo & Patton, Andrew J. & Schmidt, Patrick W., 2020. "Testing forecast rationality for measures of central tendency," Hohenheim Discussion Papers in Business, Economics and Social Sciences 12-2020, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    547. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    548. Qin Zhang & Yong Bian, 2026. "Understanding Machine Learning-based Methods in Macroeconomic Forecasting: Tracking Chinese GDP Growth Rate," Computational Economics, Springer;Society for Computational Economics, vol. 68(3), pages 1939-1991, September.
    549. Önder, Ali Sina & Yilmazkuday, Hakan, 2016. "Trade partner diversification and growth: How trade links matter," Journal of Macroeconomics, Elsevier, vol. 50(C), pages 241-258.
    550. Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention," Energies, MDPI, vol. 13(7), pages 1-29, April.
    551. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    552. Fabio Busetti & Juri Marcucci & Giovanni Veronese, 2009. "Comparing forecast accuracy: A Monte Carlo investigation," Temi di discussione (Economic working papers) 723, Bank of Italy, Economic Research and International Relations Area.
    553. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    554. Kopoin, Alexandre & Moran, Kevin & Paré, Jean-Pierre, 2013. "Forecasting regional GDP with factor models: How useful are national and international data?," Economics Letters, Elsevier, vol. 121(2), pages 267-270.
    555. Brent Meyer & Murat Tasci, 2015. "Lessons for forecasting unemployment in the United States: use flow rates, mind the trend," FRB Atlanta Working Paper 2015-1, Federal Reserve Bank of Atlanta.
    556. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
    557. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    558. Anatolyev, Stanislav & Gospodinov, Nikolay & Jamali, Ibrahim & Liu, Xiaochun, 2017. "Foreign exchange predictability and the carry trade: A decomposition approach," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 199-211.
    559. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    560. Carlos Medel, 2021. "Forecasting Brazilian Inflation with the Hybrid New Keynesian Phillips Curve: Assessing the Predictive Role of Trading Partners," Working Papers Central Bank of Chile 900, Central Bank of Chile.
    561. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    562. Giovanni Calice & Christos Ioannidis & Julian Williams, 2011. "Credit Derivatives and the Default Risk of Large Complex Financial Institutions," CESifo Working Paper Series 3583, CESifo.
    563. Masoud Soleimani, 2026. "Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target," Papers 2609.26303, arXiv.org.
    564. Muzi Chen & Difang Huang & Shouyang Wang & Xinghan Xia, 2026. "Do Carbon Price Forecasts Improve Compliance Procurement? Evidence from European Union Allowances," Papers 2607.23426, arXiv.org.
    565. Amendola, Alessandra & Storti, Giuseppe, 2008. "A GMM procedure for combining volatility forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3047-3060, February.
    566. Pincheira, Pablo, 2013. "A Bunch of Models, a Bunch of Nulls and Inference about Predictive Ability," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 26-43, October.
    567. Kauppi, Heikki & Virtanen, Timo, 2021. "Boosting nonlinear predictability of macroeconomic time series," International Journal of Forecasting, Elsevier, vol. 37(1), pages 151-170.
    568. Nicholas Taylor, 2015. "Realized volatility forecasting in an international context," Applied Economics Letters, Taylor & Francis Journals, vol. 22(6), pages 503-509, April.
    569. Anthony Garratt & Ivan Petrella, 2022. "Commodity prices and inflation risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 392-414, March.
    570. Bams, Dennis & Blanchard, Gildas & Lehnert, Thorsten, 2017. "Volatility measures and Value-at-Risk," International Journal of Forecasting, Elsevier, vol. 33(4), pages 848-863.
    571. Atsushi Inoue & Barbara Rossi, 2011. "Out-of-sample forecast tests robust to the choice of window size," Working Papers 11-31, Federal Reserve Bank of Philadelphia.
    572. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    573. Domenico Ferraro & Kenneth S. Rogoff & Barbara Rossi, 2011. "Can oil prices forecast exchange rates?," Working Papers 11-34, Federal Reserve Bank of Philadelphia.
    574. Filipović, Damir & Gourier, Elise & Mancini, Loriano, 2016. "Quadratic variance swap models," Journal of Financial Economics, Elsevier, vol. 119(1), pages 44-68.
    575. Ghimire, Sujan & Nguyen-Huy, Thong & Deo, Ravinesh C. & Casillas-Pérez, David & Masrur Ahmed, A.A. & Salcedo-Sanz, Sancho, 2025. "Novel deep hybrid model for electricity price prediction based on dual decomposition," Applied Energy, Elsevier, vol. 395(C).
    576. Diks, Cees & Fang, Hao, 2020. "Comparing density forecasts in a risk management context," International Journal of Forecasting, Elsevier, vol. 36(2), pages 531-551.
    577. Pincheira, Pablo & Neumann, Federico, 2018. "Can we beat the Random Walk? The case of survey-based exchange rate forecasts in Chile," MPRA Paper 90432, University Library of Munich, Germany.
    578. Ouysse, Rachida, 2016. "Bayesian model averaging and principal component regression forecasts in a data rich environment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 763-787.
    579. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    580. Juan Díaz Maureira & Gustavo Leyva Jiménez, 2009. "Proyección de la inflación chilena en tiempos difíciles," Monetaria, CEMLA, vol. 0(4), pages 491-522, octubre-d.
    581. Travis J. Berge, 2011. "Forecasting disconnected exchange rates," Research Working Paper RWP 11-12, Federal Reserve Bank of Kansas City.
    582. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    583. Lhuissier, Stéphane, 2022. "Financial conditions and macroeconomic downside risks in the euro area," European Economic Review, Elsevier, vol. 143(C).
    584. Nicholas Fawcett & Lena Koerber & Riccardo Masolo & Matthew Waldron, 2015. "Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis," Bank of England Staff Working Paper series 538, Bank of England.
    585. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Real-time forecast density combinations (forecasting US GDP growth using mixed-frequency data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    586. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.
    587. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    588. Albuquerque, Bruno & Baumann, Ursel, 2017. "Will US inflation awake from the dead? The role of slack and non-linearities in the Phillips curve," Journal of Policy Modeling, Elsevier, vol. 39(2), pages 247-271.
    589. Anne Opschoor & André Lucas & Istvan Barra & Dick van Dijk, 2019. "Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings," Tinbergen Institute Discussion Papers 19-013/IV, Tinbergen Institute, revised 23 Oct 2019.
    590. Todd E. Clark & Michael W. McCracken, 2010. "Reality checks and nested forecast model comparisons," Working Papers 2010-032, Federal Reserve Bank of St. Louis.
    591. Shirota, Shinichiro & Hizu, Takayuki & Omori, Yasuhiro, 2014. "Realized stochastic volatility with leverage and long memory," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 618-641.
    592. David T. Frazier & Donald S. Poskitt, 2025. "Sequential Scoring Rule Evaluation for Forecast Method Selection," Papers 2505.09090, arXiv.org.
    593. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    594. Wilmer Osvaldo Mart�nez-Rivera & Manuel Dario Hern�ndez-Bejarano & Juan Manuel Julio-Rom�n, 2014. "On Forecast Evaluation," Borradores de Economia 11604, Banco de la Republica.
    595. Bartosz Uniejewski, 2023. "Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices," Papers 2302.00411, arXiv.org, revised Nov 2024.
    596. Hie Joo Ahn & Matteo Luciani, 2026. "Common and Idiosyncratic Inflation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(2), pages 156-168, March.
    597. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, CEMLA, vol. 0(4), pages 517-589, octubre-d.
    598. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
    599. Maria Dolores Gadea Rivas & Gabriel Perez-Quiros, 2012. "The failure to predict the Great Recession. The failure of academic economics? A view focusing on the role of credit," Working Papers 1240, Banco de España.
    600. Lazar, Emese & Zhang, Ning, 2025. "Model Risk of Volatility Models," Econometrics and Statistics, Elsevier, vol. 35(C), pages 1-22.
    601. Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    602. Şener, Emrah & Baronyan, Sayad & Ali Mengütürk, Levent, 2012. "Ranking the predictive performances of value-at-risk estimation methods," International Journal of Forecasting, Elsevier, vol. 28(4), pages 849-873.
    603. Li, Jia & Patton, Andrew J., 2018. "Asymptotic inference about predictive accuracy using high frequency data," Journal of Econometrics, Elsevier, vol. 203(2), pages 223-240.
    604. Javier Contreras-Reyes & Wilfredo Palma, 2013. "Statistical analysis of autoregressive fractionally integrated moving average models in R," Computational Statistics, Springer, vol. 28(5), pages 2309-2331, October.
    605. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    606. Gubanova, Tatiana & Lohr, Luanne & Park, Timothy A., 2005. "Forecasting Organic Food Prices: Emerging Methods for Testing and Evaluating Conditional Predictive Ability," 2005 Conference, April 18-19, 2005, St. Louis, Missouri 19045, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    607. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    608. Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Theory-coherent forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 145-155.
    609. Pablo Pincheira, 2010. "A Real Time Evaluation of the Central Bank of Chile GDP Growth Forecasts," Working Papers Central Bank of Chile 556, Central Bank of Chile.
    610. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    611. Heiner Mikosch & Ying Zhang, 2014. "Forecasting Chinese GDP Growth with Mixed Frequency Data," KOF Working papers 14-359, KOF Swiss Economic Institute, ETH Zurich.
    612. Uniejewski, Bartosz & Ziel, Florian, 2026. "The role of probabilistic load and renewable prediction in enhancing day-ahead electricity price forecasts," Renewable Energy, Elsevier, vol. 269(C).
    613. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    614. Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," SSE/EFI Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
    615. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
    616. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.
    617. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    618. Jorge V Pérez-Rodríguez & María Santana-Gallego, 2020. "Modelling tourism receipts and associated risks, using long-range dependence models," Tourism Economics, , vol. 26(1), pages 70-96, February.
    619. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    620. Alessandra Canepa, & Karanasos, Menelaos & Paraskevopoulos, Athanasios & Chini, Emilio Zanetti, 2022. "Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202212, University of Turin.
    621. Jian Wang & Jason J. Wu, 2008. "The Taylor rule and forecast intervals for exchange rates," Globalization Institute Working Papers 22, Federal Reserve Bank of Dallas.
    622. Julia Ladeira Ferreira & Pedro L. Valls Pereira, 2025. "Transmuting Unequally Spaced Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 21(1), pages 25-48, November.
    623. Liu, Yang & Swanson, Norman R., 2024. "An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1391-1409.
    624. Johan Hagenbjörk & Jörgen Blomvall, 2019. "Simulation and evaluation of the distribution of interest rate risk," Computational Management Science, Springer, vol. 16(1), pages 297-327, February.
    625. Marcellino, Massimiliano & Jordà , Oscar, 2008. "Path Forecast Evaluation," CEPR Discussion Papers 7009, Centre for Economic Policy Research.
    626. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    627. Owusu Junior, Peterson & Tiwari, Aviral Kumar & Tweneboah, George & Asafo-Adjei, Emmanuel, 2022. "GAS and GARCH based value-at-risk modeling of precious metals," Resources Policy, Elsevier, vol. 75(C).
    628. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2014. "Causality and predictability in distribution: The ethanol–food price relation revisited," Energy Economics, Elsevier, vol. 42(C), pages 152-160.
    629. Marie Bessec, 2010. "Étalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Économie et Prévision, Programme National Persée, vol. 193(2), pages 77-99.
    630. Feng Zhao & Guofu Zhou & Xiaoneng Zhu, 2021. "Unspanned Global Macro Risks in Bond Returns," Management Science, INFORMS, vol. 67(12), pages 7825-7843, December.
    631. Christophe Boucher & Wassim Le Lann & Stéphane Matton & Sessi Tokpavi, 2024. "Are ESG ratings informative to forecast idiosyncratic risk?," Working Papers hal-04140193, HAL.
    632. Cash Looi & Ruben Loaiza-Maya & Didier Nibbering, 2026. "A Multinomial Probit Model for Asymmetric Choice Responses," Papers 2608.10336, arXiv.org.
    633. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    634. Jiménez Polanco, Miguel Alejandro & Lopez Hawa, Nabil, 2014. "Heterogeneidad y Racionalidad en las Expectativas de Inflación: Evidencia desagregada para República Dominicana [Heterogeneity and Rationality of Inflation Expectations: Disaggregated Evidence for the Dominican Republic]," MPRA Paper 75912, University Library of Munich, Germany.
    635. Stanislav Anatolyev & Nikolay Gospodinov & Ibrahim Jamali & Xiaochun Liu, 2015. "Foreign exchange predictability during the financial crisis: implications for carry trade profitability," FRB Atlanta Working Paper 2015-6, Federal Reserve Bank of Atlanta.
    636. Alexakis, Christos & Kenourgios, Dimitris & Pappas, Vasileios & Petropoulou, Athina, 2021. "From dotcom to Covid-19: A convergence analysis of Islamic investments," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    637. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2025. "Volatility forecasting and volatility-timing strategies: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 75(C).
    638. Travis J. Berge, 2013. "Predicting recessions with leading indicators: model averaging and selection over the business cycle," Research Working Paper RWP 13-05, Federal Reserve Bank of Kansas City.
    639. Pesaran, M. Hashem & Timmermann, Allan, 2005. "Small sample properties of forecasts from autoregressive models under structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 183-217.
    640. Oguzhan Akgun & Alain Pirotte & Giovanni Urga & Zhenlin Yang, 2025. "Testing Clustered Equal Predictive Ability with Unknown Clusters," Papers 2507.14621, arXiv.org, revised Jul 2026.
    641. Euler Pereira G. de Mello & Francisco Marcos R. Figueiredo, 2014. "Assessing the Short-term Forecasting Power of Confidence Indices," Working Papers Series 371, Central Bank of Brazil, Research Department.
    642. Ulrich Gunter, 2019. "Estimating and forecasting with a two-country DSGE model of the Euro area and the USA: the merits of diverging interest-rate rules," Empirical Economics, Springer, vol. 56(4), pages 1283-1323, April.
    643. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    644. Massimiliano Giacalone & Raffaele Mattera & Eugenia Nissi, 2020. "Economic indicators forecasting in presence of seasonal patterns: time series revision and prediction accuracy," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 67-84, February.
    645. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    646. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics.
    647. Tomasz Serafin & Bartosz Uniejewski & Rafał Weron, 2019. "Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 12(13), pages 1-12, July.
    648. Gibbs, Christopher G. & Vasnev, Andrey L., 2024. "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1734-1751.
    649. Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques," CREATES Research Papers 2011-27, Department of Economics and Business Economics, Aarhus University.
    650. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    651. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    652. Christian Dreger & Dieter Gerdesmeier & Barbara Roffia, 2016. "Re-vitalizing Money Demand in the Euro Area: Still Valid at the Zero Lower Bound," Discussion Papers of DIW Berlin 1606, DIW Berlin, German Institute for Economic Research.
    653. Antonio Martin Arroyo & Aranzazu de Juan Fernandez, 2020. "Split-then-Combine simplex combination and selection of forecasters," Papers 2012.11935, arXiv.org.
    654. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
    655. Barnichon, Regis & Garda, Paula, 2016. "Forecasting unemployment across countries: The ins and outs," European Economic Review, Elsevier, vol. 84(C), pages 165-183.
    656. George Tzagkarakis & Frantz Maurer & John P. Nolan, 2024. "Taming impulsive high-frequency data using optimal sampling periods," Annals of Operations Research, Springer, vol. 333(1), pages 393-415, February.
    657. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    658. Richard Luger, 2004. "Exact Tests of Equal Forecast Accuracy with an Application to the Term Structure of Interest Rates," Staff Working Papers 04-2, Bank of Canada.
    659. Raffaella Giacomini & Ivana Komunjer, 2003. "Evaluation and Combination of Conditional Quantile Forecasts," Boston College Working Papers in Economics 571, Boston College Department of Economics.
    660. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    661. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    662. Mariano, Roberto S. & Preve, Daniel, 2012. "Statistical tests for multiple forecast comparison," Journal of Econometrics, Elsevier, vol. 169(1), pages 123-130.
    663. Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2024. "Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates," International Journal of Forecasting, Elsevier, vol. 40(2), pages 626-640.
    664. Pablo Pincheira & Hernán Rubio, 2010. "The Low Predictive Power of Simple Phillips Curves in Chile: A Real-Time Evaluation," Working Papers Central Bank of Chile 559, Central Bank of Chile.
    665. Tarassow, Artur, 2019. "Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques," International Journal of Forecasting, Elsevier, vol. 35(2), pages 443-457.
    666. In Choi, 2012. "Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons," Working Papers 1209, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    667. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    668. Mu-Chun Wang, 2009. "Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 167-182.
    669. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    670. Sebastiano Manzan & Dawit Zerom, 2015. "Asymmetric Quantile Persistence and Predictability: the Case of US Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(2), pages 297-318, April.
    671. Roxana Halbleib & Valeri Voev, 2016. "Forecasting Covariance Matrices: A Mixed Approach," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 383-417.
    672. Neri, Marcelo Côrtes, 2014. "Brazil's middle classes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 759, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    673. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2010. "Survey data as coincident or leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 109-131.
    674. Mont'Alverne Duarte, Angelo & Gaglianone, Wagner Piazza & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor, 2021. "Commodity prices and global economic activity: A derived-demand approach," Energy Economics, Elsevier, vol. 96(C).
    675. Fabian Krüger & Sebastian Lerch & Thordis Thorarinsdottir & Tilmann Gneiting, 2021. "Predictive Inference Based on Markov Chain Monte Carlo Output," International Statistical Review, International Statistical Institute, vol. 89(2), pages 274-301, August.
    676. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    677. Patrick Marsh, 2019. "Nonparametric conditional density specification testing and quantile estimation; with application to S&P500 returns," Discussion Papers 19/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    678. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    679. Andrea Carriero & Raffaella Giacomini, 2011. "How useful are no-arbitrage restrictions for forecasting the term structure of interest rates?," Post-Print hal-00844809, HAL.
    680. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    681. Boero, Gianna & Smith, Jeremy & Wallis, Kenneth F., 2011. "Scoring rules and survey density forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 379-393, April.
    682. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.
    683. Maria Dolores Gadea Rivas & Gabriel Perez-Quiros, 2015. "The Failure To Predict The Great Recession—A View Through The Role Of Credit," Journal of the European Economic Association, European Economic Association, vol. 13(3), pages 534-559, June.
    684. Francesco Ravazzolo & Shaun P Vahey, 2010. "Measuring Core Inflation in Australia with Disaggregate Ensembles," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    685. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
    686. Pablo M. Pincheira & Carlos A. Medel, 2015. "Forecasting Inflation with a Simple and Accurate Benchmark: The Case of the US and a Set of Inflation Targeting Countries," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(1), pages 2-29, January.
    687. Ricardo Gimeno & José Manuel Marqués-Sevillano, 2009. "Incertidumbre y el precio del riesgo en un proceso de convergencia nominal," Monetaria, CEMLA, vol. 0(4), pages 451-489, octubre-d.
    688. Markku Lanne & Jani Luoto & Henri Nyberg, 2014. "Is the Quantity Theory of Money Useful in Forecasting U.S. Inflation?," CREATES Research Papers 2014-26, Department of Economics and Business Economics, Aarhus University.
    689. Chen Xilong & Ghysels Eric & Wang Fangfang, 2011. "HYBRID GARCH Models and Intra-Daily Return Periodicity," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-28, February.
    690. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2010. "Properties of Foreign Exchange Risk Premia," MPRA Paper 21302, University Library of Munich, Germany.
    691. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    692. Javier García-Cicco & Roque Montero, 2011. "Modeling Copper Price: A Regime-Switching Approach," Working Papers Central Bank of Chile 613, Central Bank of Chile.
    693. Kyungchul Song, 2009. "Testing Predictive Ability and Power Robustification," PIER Working Paper Archive 09-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    694. Francisco (F.) Blasques & Paolo Gorgi & Siem Jan (S.J.) Koopman, 2017. "Accelerating GARCH and Score-Driven Models: Optimality, Estimation and Forecasting," Tinbergen Institute Discussion Papers 17-059/III, Tinbergen Institute.
    695. Pablo Pincheira B., 2008. "Predictibilidad Encubierta en Economía: El Caso del Tipo de Cambio Nominal Chileno," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 11(1), pages 137-142, April.
    696. Marcos Álvarez-Díaz, 2020. "Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods," Empirical Economics, Springer, vol. 59(3), pages 1285-1305, September.
    697. Ida Wolden Bache & James Mitchell & Francesco Ravazzolo & Shaun P. Vahey, 2009. "Macro modelling with many models," Working Paper 2009/15, Norges Bank.
    698. Florackis, Chris & Giorgioni, Gianluigi & Kostakis, Alexandros & Milas, Costas, 2014. "On stock market illiquidity and real-time GDP growth," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 210-229.
    699. Li, Hong & Shi, Yanlin, 2021. "Forecasting mortality with international linkages: A global vector-autoregression approach," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 59-75.
    700. Grzegorz Marcjasz & Jesus Lago & Rafa{l} Weron, 2020. "Neural networks in day-ahead electricity price forecasting: Single vs. multiple outputs," Papers 2008.08006, arXiv.org.
    701. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    702. You, Yu & Liu, Xiaochun, 2020. "Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach," Journal of Banking & Finance, Elsevier, vol. 116(C).
    703. Andre Lucas & Anne Opschoor & Luca Rossini, 2021. "Tail Heterogeneity for Dynamic Covariance Matrices: the F-Riesz Distribution," Tinbergen Institute Discussion Papers 21-010/III, Tinbergen Institute, revised 11 Jul 2023.
    704. Bouwman, Kees E. & Jacobs, Jan P.A.M., 2011. "Forecasting with real-time macroeconomic data: The ragged-edge problem and revisions," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 784-792.
    705. Andrés Schneider, 2009. "Regímenes de flotación administrada: un enfoque de cartera," Monetaria, CEMLA, vol. 0(4), pages 549-584, octubre-d.
    706. Qiu, Yue & Wang, Yifan & Xie, Tian, 2021. "Forecasting Bitcoin realized volatility by measuring the spillover effect among cryptocurrencies," Economics Letters, Elsevier, vol. 208(C).
    707. Minchul Shin & Nathan Schor, 2026. "ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics," Papers 2603.07458, arXiv.org.
    708. Symitsi, Efthymia & Symeonidis, Lazaros & Kourtis, Apostolos & Markellos, Raphael, 2018. "Covariance forecasting in equity markets," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 153-168.
    709. Giannone, Domenico & Reichlin, Lucrezia & De Mol, Christine, 2006. "Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components?," Working Paper Series 700, European Central Bank.
    710. McMillan, David G., 2014. "Stock return, dividend growth and consumption growth predictability across markets and time: Implications for stock price movement," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 90-101.
    711. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2013. "Predicting Covariance Matrices with Financial Conditions Indexes," Tinbergen Institute Discussion Papers 13-113/III, Tinbergen Institute.
    712. Yannick Hoga & Timo Dimitriadis, 2021. "On Testing Equal Conditional Predictive Ability Under Measurement Error," Papers 2106.11104, arXiv.org.
    713. Tamara Burdisso & Eduardo Ariel Corso, 2011. "Incertidumbre y dolarización de cartera: el caso argentino en el último medio siglo," Monetaria, CEMLA, vol. 0(4), pages 461-515, octubre-d.
    714. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    715. Michele Bernardi & Jaqueson K. Galimberti, 2014. "A Note on the Representative Adaptive Learning Algorithm," KOF Working papers 14-356, KOF Swiss Economic Institute, ETH Zurich.
    716. Carlos Medel, 2021. "Searching for the Best Inflation Forecasters within a Consumer Perceptions Survey: Microdata Evidence from Chile," Working Papers Central Bank of Chile 899, Central Bank of Chile.
    717. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers ubs0504, University of Brescia, Department of Economics.
    718. Watanabe, Toshiaki & Nakajima, Jouchi, 2024. "High-frequency realized stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 79(C).
    719. Andrew J. Patton & Kevin Sheppard, 2008. "Evaluating Volatility and Correlation Forecasts," OFRC Working Papers Series 2008fe22, Oxford Financial Research Centre.
    720. Kang, Haijun & Zong, Xiangyu & Wang, Jianyong & Chen, Haonan, 2023. "Binary gravity search algorithm and support vector machine for forecasting and trading stock indices," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 507-526.
    721. Nonejad, Nima, 2022. "Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    722. Favero, Carlo A. & Aiolfi, Marco, 2003. "Model Uncertainty, Thick Modelling and the Predictability of Stock Returns," CEPR Discussion Papers 3997, Centre for Economic Policy Research.
    723. Kei Kawakami, 2008. "Forecast Selection by Conditional Predictive Ability Tests: An Application to the Yen/Dollar Exchange Rate," Bank of Japan Working Paper Series 08-E-1, Bank of Japan.
    724. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.
    725. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2011. "Multivariate High-Frequency-Based Volatility (HEAVY) Models," Economics Papers 2011-W01, Economics Group, Nuffield College, University of Oxford.
    726. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2019. "Do Any Economists Have Superior Forecasting Skills?," CEPR Discussion Papers 14112, Centre for Economic Policy Research.
    727. Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
    728. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
    729. Yu‐Sheng Lai, 2021. "Generalized autoregressive score model with high‐frequency data for optimal futures hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(12), pages 2023-2045, December.
    730. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    731. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, Centre for Economic Policy Research.
    732. Yizhou & Kuang, 2026. "Revision Risk in Real-Time Macroeconomic Forecasting," Papers 2607.05882, arXiv.org, revised Jul 2026.
    733. Martínez-Martin, Jaime & Morris, Richard & Onorante, Luca & Piersanti, Fabio M., 2019. "Merging structural and reduced-form models for forecasting: opening the DSGE-VAR box," Working Paper Series 2335, European Central Bank.
    734. Dur, Ayşe & Martínez García, Enrique, 2020. "Mind the gap!—A monetarist view of the open-economy Phillips curve," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    735. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
    736. Bianchi, Francesco & Ludvigson, Sydney & MA, Sai, 2020. "Belief Distortions and Macroeconomic Fluctuations," CEPR Discussion Papers 15003, Centre for Economic Policy Research.
    737. Mike Buckle & Jing Chen & Julian Williams, 2014. "How Predictable Are Equity Covariance Matrices? Evidence from High‐Frequency Data for Four Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(7), pages 542-557, November.
    738. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    739. Kaminska, Iryna & Roberts-Sklar, Matt, 2018. "Volatility in equity markets and monetary policy rate uncertainty," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 68-83.
    740. Nathan Goldstein & Ben‐Zion Zilberfarb, 2023. "The closer we get, the better we are?," Economic Inquiry, Western Economic Association International, vol. 61(2), pages 364-376, April.
    741. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    742. Fabrizio Iacone & Luca Rossini & Andrea Viselli, 2024. "Comparing predictive ability in presence of instability over a very short time," Papers 2405.11954, arXiv.org.
    743. Heikki Kauppi & Timo Virtanen, 2018. "Boosting Non-linear Predictabilityof Macroeconomic Time Series," Discussion Papers 124, Aboa Centre for Economics.
    744. Dreger, Christian & Wolters, Jürgen, 2014. "Money demand and the role of monetary indicators in forecasting euro area inflation," International Journal of Forecasting, Elsevier, vol. 30(2), pages 303-312.
    745. Alexandridis, Antonios K. & Apergis, Iraklis & Panopoulou, Ekaterini & Voukelatos, Nikolaos, 2023. "Equity premium prediction: The role of information from the options market," Journal of Financial Markets, Elsevier, vol. 64(C).
    746. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2017. "Density Forecasts With Midas Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 783-801, June.
    747. F. Della Marra, 2017. "A forecasting performance comparison of dynamic factor models based on static and dynamic methods," Economics Department Working Papers 2017-ME01, Department of Economics, Parma University (Italy).
    748. Zhu, Yinchu & Timmermann, Allan, 2022. "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, vol. 231(2), pages 329-347.
    749. Timmermann, Allan & Aiolfi, Marco & Rodriguez, Marius, 2010. "Understanding Analysts' Earnings Expectations: Biases, Nonlinearities and Predictability," CEPR Discussion Papers 7656, Centre for Economic Policy Research.
    750. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
    751. Yu‐Sheng Lai, 2023. "Optimal futures hedging by using realized semicovariances: The information contained in signed high‐frequency returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(5), pages 677-701, May.
    752. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    753. Monica Defend & Aleksey Min & Lorenzo Portelli & Franz Ramsauer & Francesco Sandrini & Rudi Zagst, 2021. "Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data," Forecasting, MDPI, vol. 3(1), pages 1-35, February.
    754. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
    755. Eric Hillebrand & Jakob Guldbæk Mikkelsen & Lars Spreng & Giovanni Urga, 2023. "Exchange rates and macroeconomic fundamentals: Evidence of instabilities from time‐varying factor loadings," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 857-877, September.
    756. Timo Dimitriadis & Julie Schnaitmann, 2019. "Forecast Encompassing Tests for the Expected Shortfall," Papers 1908.04569, arXiv.org, revised Aug 2020.
    757. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial indicators and density forecasts for US output and inflation," Temi di discussione (Economic working papers) 977, Bank of Italy, Economic Research and International Relations Area.
    758. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
    759. Abdymomunov, Azamat, 2013. "Predicting output using the entire yield curve," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 333-344.
    760. Zikes, Filip & Barunik, Jozef & Shenai, Nikhil, 2015. "Modeling and forecasting persistent financial durations," FinMaP-Working Papers 36, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    761. Pawel M. Krolikowski & Kurt G. Lunsford, 2024. "Advance layoff notices and aggregate job loss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 462-480, April.
    762. Iryna Kaminska & Matt Roberts-Sklar, 2015. "A global factor in variance risk premia and local bond pricing," Bank of England Staff Working Paper series 576, Bank of England.
    763. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    764. Arkadiusz Lipiecki & Nikolaos Kourentzes & Rafal Weron, 2026. "Stealing profits: Spread-based temporal hierarchy forecasting for day-ahead electricity markets," Papers 2609.23223, arXiv.org.
    765. Matteo Bagnara, 2024. "Asset Pricing and Machine Learning: A critical review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(1), pages 27-56, February.
    766. Paul D. McNelis & Salih N. Neftci, 2006. "Renminbi Revaluation, Euro Appreciation and Chinese Markets: What Can We Learn From Data?," Working Papers 012006, Hong Kong Institute for Monetary Research.
    767. Mao, Xiuping & Ruiz, Esther & Veiga, Helena, 2017. "Threshold stochastic volatility: Properties and forecasting," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1105-1123.
    768. Deniz Sevinc & Edgar Mata Flores, 2021. "Macroeconomic and financial implications of multi‐dimensional interdependencies between OECD countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 741-776, January.
    769. Zhang, Hanyu & Dufour, Alfonso, 2019. "Modeling intraday volatility of European bond markets: A data filtering application," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 131-146.
    770. Patton, Andrew J. & Zhang, Haozhe, 2026. "Bespoke realized volatility: Tailored measures of risk for volatility prediction," Journal of Econometrics, Elsevier, vol. 254(PA).
    771. Jia, Jian & Kang, Sang Baum, 2022. "Do the basis and other predictors of futures return also predict spot return with the same signs and magnitudes? Evidence from the LME," Journal of Commodity Markets, Elsevier, vol. 25(C).
    772. Massimo Guidolin & Allan Timmerman, 2007. "Forecasts of U.S. short-term interest rates: a flexible forecast combination approach," Working Papers 2005-059, Federal Reserve Bank of St. Louis.
    773. Amendola, Alessandra & Candila, Vincenzo & Scognamillo, Antonio, 2015. "On the influence of the U.S. monetary policy on the crude oil price volatility," 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy 207860, Italian Association of Agricultural and Applied Economics (AIEAA).
    774. Alexander Henzi & Johanna F Ziegel, 2022. "Valid sequential inference on probability forecast performance [A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems]," Biometrika, Biometrika Trust, vol. 109(3), pages 647-663.
    775. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 27-42, March.
    776. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    777. Theodore M. Crone & N. Neil K. Khettry & Loretta J. Mester & Jason A. Novak, 2011. "Core measures of inflation as predictors of total inflation," Working Papers 11-24, Federal Reserve Bank of Philadelphia.
    778. Kim, Young Min & Lee, Seojin, 2020. "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 117-134.
    779. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    780. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Online Appendix to "Financial conditions and density forecasts for US output and inflation"," Online Appendices 14-103, Review of Economic Dynamics.
    781. Ulrich Gunter & Bozana Zekan & Francesco Luigi Milone, 2025. "Modelling and forecasting European Airbnb occupancy during the pandemic: The specific merits of panel-data and Markov-switching models," Tourism Economics, , vol. 31(4), pages 695-713, June.
    782. Andrea Betancor & Pablo Pincheira, 2008. "Forecasting Inflation Forecast Errors," Working Papers Central Bank of Chile 477, Central Bank of Chile.
    783. Bak, Yuhyeon & Park, Cheolbeom, 2022. "Exchange rate predictability, risk premiums, and predictive system," Economic Modelling, Elsevier, vol. 116(C).
    784. Bretó, Carles & Veiga, Helena, 2011. "Forecasting volatility: does continuous time do better than discrete time?," DES - Working Papers. Statistics and Econometrics. WS ws112518, Universidad Carlos III de Madrid. Departamento de Estadística.
    785. Blaskowitz, Oliver & Herwartz, Helmut, 2014. "Testing the value of directional forecasts in the presence of serial correlation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 30-42.
    786. Jean-Yves Pitarakis, 2020. "A Novel Approach to Predictive Accuracy Testing in Nested Environments," Papers 2008.08387, arXiv.org, revised Oct 2023.
    787. Mustanen, Dmitri & Maaitah, Ahmad & Mishra, Tapas & Parhi, Mamata, 2022. "The power of investors’ optimism and pessimism in oil market forecasting," Energy Economics, Elsevier, vol. 114(C).
    788. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    789. Issler, João Victor & Lima, Luiz Renato Regis de Oliveira, 2007. "A panel data approach to economic forecasting: the bias-corrected average forecast," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 642, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    790. James Lightwood & Steve Anderson & Stanton A Glantz, 2020. "Predictive validation and forecasts of short-term changes in healthcare expenditure associated with changes in smoking behavior in the United States," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    791. Francisco Lasso-Valderrama & Héctor M. Zárate-Solano, 2019. "Forecasting the Colombian Unemployment Rate Using Labour Force Flows," Borradores de Economia 1073, Banco de la Republica de Colombia.
    792. Albuquerque, Bruno & Baumann, Ursel & Seitz, Franz, 2016. "What does money and credit tell us about real activity in the United States?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 328-347.
    793. Garegnani, Lorena & Gómez Aguirre, Maximiliano, 2018. "Forecasting Inflation in Argentina," IDB Publications (Working Papers) 8940, Inter-American Development Bank.
    794. Ando, Tomohiro & Tsay, Ruey, 2010. "Predictive likelihood for Bayesian model selection and averaging," International Journal of Forecasting, Elsevier, vol. 26(4), pages 744-763, October.
    795. Jamali, Ibrahim & Yamani, Ehab, 2019. "Out-of-sample exchange rate predictability in emerging markets: Fundamentals versus technical analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 241-263.
    796. Yen, Yu-Min & Yen, Tso-Jung, 2021. "Testing forecast accuracy of expectiles and quantiles with the extremal consistent loss functions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 733-758.
    797. Capistrán Carlos, 2007. "Optimality Tests for Multi-Horizon Forecasts," Working Papers 2007-14, Banco de México.
    798. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    799. Conrad, Christian & Glas, Alexander, 2018. "‘Déjà vol’ revisited: Survey forecasts of macroeconomic variables predict volatility in the cross-section of industry portfolios," Working Papers 0655, University of Heidelberg, Department of Economics.
    800. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    801. Emilio Blanco & Fiorella Dogliolo & Lorena Garegnani, 2022. "Nowcasting during the Pandemic: Lessons from Argentina," BCRA Working Paper Series 202299, Central Bank of Argentina, Economic Research Department.
    802. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.).
    803. Pavel Yaskov, 2010. "Testing for predictive ability in the presence of structural breaks (in Russian)," Quantile, Quantile, issue 8, pages 127-135, July.
    804. Chen, Chaoyi & Gospodinov, Nikolay & Maynard, Alex & Pesavento, Elena, 2022. "Long-horizon stock valuation and return forecasts based on demographic projections," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 190-215.
    805. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    806. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    807. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2024. "Reprint of: Out-of-sample tests for conditional quantile coverage: An application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 244(2).
    808. Bucci, Andrea, 2017. "Forecasting realized volatility: a review," MPRA Paper 83232, University Library of Munich, Germany.
    809. Elliott, Graham & Timmermann, Allan, 2016. "Forecasting in Economics and Finance," CEPR Discussion Papers 11354, Centre for Economic Policy Research.
    810. Allen, Sam & Koh, Jonathan & Segers, Johan & Ziegel, Johanna, 2024. "Tail calibration of probabilistic forecasts," LIDAM Discussion Papers ISBA 2024018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    811. Dimos Kambouroudis & David McMillan & Katerina Tsakou, 2019. "Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility," Working Papers 2019-03, Swansea University, School of Management.
    812. Almeida, Daniel de & Hotta, Luiz & Ruiz Ortega, Esther, 2015. "MGARCH models: tradeoff between feasibility and flexibility," DES - Working Papers. Statistics and Econometrics. WS ws1516, Universidad Carlos III de Madrid. Departamento de Estadística.
    813. Oleg Roshka, 2026. "Global Persistence, Local Residual Structure: Forecasting Heterogeneous Investment Panels," Papers 2604.09821, arXiv.org, revised May 2026.
    814. Dbouk, Wassim & Jamali, Ibrahim, 2018. "Predicting daily oil prices: Linear and non-linear models," Research in International Business and Finance, Elsevier, vol. 46(C), pages 149-165.
    815. Arbués, Ignacio, 2013. "Determining the MSE-optimal cross section to forecast," Journal of Econometrics, Elsevier, vol. 175(2), pages 61-70.
    816. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    817. Pham, Manh Cuong & Anderson, Heather Margot & Duong, Huu Nhan & Lajbcygier, Paul, 2020. "The effects of trade size and market depth on immediate price impact in a limit order book market," Journal of Economic Dynamics and Control, Elsevier, vol. 120(C).
    818. Zhu, Yinchu & Timmermann, Allan, 2021. "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers 15917, Centre for Economic Policy Research.
    819. Lukas Bauer & Ekaterina Kazak, 2025. "Conditional Method Confidence Set," Papers 2505.21278, arXiv.org.
    820. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    821. Guoshi Tong, 2017. "Market Timing under Limited Information: An Empirical Investigation in US Treasury Market," Annals of Economics and Finance, Society for AEF, vol. 18(2), pages 291-322, November.
    822. Krystian Jaworski, 2026. "AI‐Driven Inflation Forecasting in the Aftermath of COVID‐19," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(5), pages 2525-2548, August.
    823. Idrovo Aguirre, Byron & Tejada, Mauricio, 2010. "Modelos de predicción para la inflación de Chile [Inflation forecast models for Chile]," MPRA Paper 31586, University Library of Munich, Germany, revised 26 Mar 2010.
    824. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    825. Lorenzo Boldrini & Eric Hillebrand, 2015. "Supervision in Factor Models Using a Large Number of Predictors," CREATES Research Papers 2015-38, Department of Economics and Business Economics, Aarhus University.
    826. Santiago Cajiao Raigosa & Luis Fernando Melo Velandia & Daniel Parra Amado, 2014. "Pron�sticos para una econom�a menos vol�til: El caso colombiano," Borradores de Economia 11252, Banco de la Republica.
    827. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.
    828. Lukas Bauer, 2025. "Evaluating financial tail risk forecasts: Testing Equal Predictive Ability," Papers 2505.23333, arXiv.org.
    829. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    830. Emanuel Kohlscheen & Fernando Avalos & Andreas Schrimpf, 2016. "When the walk is not random: commodity prices and exchange rates," BIS Working Papers 551, Bank for International Settlements.
    831. Laurent, Sébastien & Rombouts, Jeroen V.K. & Violante, Francesco, 2013. "On loss functions and ranking forecasting performances of multivariate volatility models," Journal of Econometrics, Elsevier, vol. 173(1), pages 1-10.
    832. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
    833. Hecq, A.W. & Palm, F.C. & Laurent, S.F.J.A., 2011. "Common intraday periodicity," Research Memorandum 010, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    834. Rose Ellen Macabiog & Jennifer Dela Cruz, 2025. "Multifeature-Driven Multistep Wind Speed Forecasting Using NARXR and Modified VMD Approaches," Forecasting, MDPI, vol. 7(1), pages 1-24, March.
    835. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
    836. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
    837. Giuseppe Parigi & Roberto Golinelli, 2007. "The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 77-94.
    838. Timmermann, Allan & Zhu, Yinchu, 2019. "Comparing Forecasting Performance with Panel Data," CEPR Discussion Papers 13746, Centre for Economic Policy Research.
    839. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.
    840. William J. Procasky & Anwen Yin, 2022. "Forecasting high‐yield equity and CDS index returns: Does observed cross‐market informational flow have predictive power?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1466-1490, August.
    841. Granziera, Eleonora & Hubrich, Kirstin & Moon, Hyungsik Roger, 2014. "A predictability test for a small number of nested models," Journal of Econometrics, Elsevier, vol. 182(1), pages 174-185.
    842. Cosemans, Mathijs & Frehen, Rik & Schotman, Peter & Bauer, Rob, 2016. "Estimating security betas using prior information based on firm fundamentals," Other publications TiSEM f0f91c05-b59e-454c-a102-a, Tilburg University, School of Economics and Management.
    843. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    844. Mawuli Segnon & Bjorn Schulte-Tillmann & Riza Demirer & Rangan Gupta, 2025. "Deglobalization and Foreign Exchange Volatility: The Role of Supply Chain Pressures," Working Papers 202506, University of Pretoria, Department of Economics.
    845. Everett Grant & Julieta Yung, 2021. "The double‐edged sword of global integration: Robustness, fragility, and contagion in the international firm network," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 760-783, September.
    846. Opschoor, Anne & Lucas, André, 2023. "Time-varying variance and skewness in realized volatility measures," International Journal of Forecasting, Elsevier, vol. 39(2), pages 827-840.
    847. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    848. Osmani Teixeira de Carvalho Guillén & Alain Hecq & João Victor Issler & Diogo Saraiva, 2013. "Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions," Working Papers Series 330, Central Bank of Brazil, Research Department.
    849. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    850. Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.
    851. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.

  28. Raffaella Giacomini, 2002. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests: Asymptotic and Bootstrap Methods," Boston College Working Papers in Economics 583, Boston College Department of Economics.

    Cited by:

    1. Isao Ishida, 2005. "Scanning Multivariate Conditional Densities with Probability Integral Transforms," CARF F-Series CARF-F-045, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    3. James Mitchell & Stephen G. Hall, 2005. "Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR ‘Fan’ Charts of Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 995-1033, December.
    4. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    5. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
    6. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    7. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    8. van Dijk, D.J.C. & Franses, Ph.H.B.F., 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Econometric Institute Research Papers EI 2003-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Corradi, Valentina & Swanson, Norman R., 2004. "A test for the distributional comparison of simulated and historical data," Economics Letters, Elsevier, vol. 85(2), pages 185-193, November.
    10. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    11. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    12. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    13. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    14. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.).
    15. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.

  29. Raffaella Giacomini & Clive W.J. Granger, 2002. "Aggregation of Space-Time Processes," Boston College Working Papers in Economics 582, Boston College Department of Economics.

    Cited by:

    1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
    2. Patrick Doupe, 2014. "The Costs of Error in Setting Reference Rates for Reduced Deforestation," CCEP Working Papers 1415, Centre for Climate & Energy Policy, Crawford School of Public Policy, The Australian National University.
    3. Svetlana Borovkova & Hendrik P. Lopuhaä & Budi Nurani Ruchjana, 2008. "Consistency and asymptotic normality of least squares estimators in generalized STAR models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(4), pages 482-508, November.
    4. Gabriel Pino & J. D. Tena & Antoni Espasa, 2016. "Geographical disaggregation of sectoral inflation. Econometric modelling of the Euro area and Spanish economies," Applied Economics, Taylor & Francis Journals, vol. 48(9), pages 799-815, February.
    5. Kamarianakis, Yiannis & Prastacos, Poulicos, 2002. "Space-time modeling of traffic flow," ERSA conference papers ersa02p141, European Regional Science Association.
    6. Roger Bivand, 2008. "Implementing Representations Of Space In Economic Geography," Journal of Regional Science, Wiley Blackwell, vol. 48(1), pages 1-27, February.
    7. Elzbieta Szulc, 2008. "Modelling of the Dependence Between the Space-time Processes," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 8, pages 85-94.
    8. I Gede Nyoman Mindra Jaya & Henk Folmer, 2025. "Spatiotemporal forecasting models with and without a confounded covariate," Journal of Geographical Systems, Springer, vol. 27(1), pages 113-146, January.
    9. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    10. Percoco, Marco, 2015. "Temporal aggregation and spatio-temporal traffic modeling," Journal of Transport Geography, Elsevier, vol. 46(C), pages 244-247.
    11. Hendry, David F. & Hubrich, Kirstin, 2010. "Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate," Working Paper Series 1155, European Central Bank.
    12. Paulauskas, Vygantas, 2007. "On unit roots for spatial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 209-226, January.
    13. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
    14. Bhattacharjee, Arnab & Holly, Sean, 2009. "Structural Interactions in Spatial Panels," SIRE Discussion Papers 2009-39, Scottish Institute for Research in Economics (SIRE).
    15. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    16. Michael Beenstock & Daniel Felsenstein, 2010. "Spatial error correction and cointegration in nonstationary panel data: regional house prices in Israel," Journal of Geographical Systems, Springer, vol. 12(2), pages 189-206, June.
    17. Shoesmith, Gary L., 2013. "Space–time autoregressive models and forecasting national, regional and state crime rates," International Journal of Forecasting, Elsevier, vol. 29(1), pages 191-201.
    18. Carlomagno, Guillermo & Espasa, Antoni, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    20. Pino, Gabriel & Tena Horrillo, Juan de Dios & Espasa, Antoni, 2013. "Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain," DES - Working Papers. Statistics and Econometrics. WS ws130807, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Jiao, Xiaoying & Li, Gang & Chen, Jason Li, 2020. "Forecasting international tourism demand: a local spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 83(C).
    22. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas F. Quising, 2006. "Measuring Regional Market Integration by Dynamic Factor Error Correction Model (DF-ECM) Approach - The Case of Developing Asia," Working Papers 565, Queen Mary University of London, School of Economics and Finance.
    23. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB-Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    24. Bokun, Kathryn O. & Jackson, Laura E. & Kliesen, Kevin L. & Owyang, Michael T., 2023. "FRED-SD: A real-time database for state-level data with forecasting applications," International Journal of Forecasting, Elsevier, vol. 39(1), pages 279-297.
    25. Kajal Lahiri & Cheng Yang & Yimeng Yin, 2025. "Forecasting U.S. social security disability applications: a spatial dynamic panel data model approach," Empirical Economics, Springer, vol. 69(5), pages 2699-2725, November.
    26. Gopal K. Basak & Arnab Bhattacharjee & Samarjit Das, 2018. "Causal ordering and inference on acyclic networks," Empirical Economics, Springer, vol. 55(1), pages 213-232, August.
    27. Carlomagno, Guillermo & Espasa, Antoni, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    28. Edoardo Otranto & Massimo Mucciardi, 2019. "Clustering space-time series: FSTAR as a flexible STAR approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 175-199, March.
    29. Alexander Chudik & M. Hashem Pesaran, 2011. "Aggregation in large dynamic panels," Globalization Institute Working Papers 101, Federal Reserve Bank of Dallas.
    30. Cabral, Joilson de Assis & Legey, Luiz Fernando Loureiro & Freitas Cabral, Maria Viviana de, 2017. "Electricity consumption forecasting in Brazil: A spatial econometrics approach," Energy, Elsevier, vol. 126(C), pages 124-131.
    31. Doupe, Patrick, 2014. "The costs of error in setting reference rates for reduced deforestation," Working Papers 249497, Australian National University, Centre for Climate Economics & Policy.
    32. Blazej Mazur, 2015. "Density forecasts based on disaggregate data: nowcasting Polish inflation," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 71-87.
    33. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    34. Carlomagno, Guillermo & Eterovic, Nicolás & Hernández-Román, Luis G., 2024. "Disentangling demand and supply inflation shocks from electronic payments data," Economic Modelling, Elsevier, vol. 141(C).
    35. Nicholson, Alan, 2015. "Travel time reliability benefits: Allowing for correlation," Research in Transportation Economics, Elsevier, vol. 49(C), pages 14-21.
    36. Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger, 2011. "Forecasting (aggregate) demand for US commercial air travel," International Journal of Forecasting, Elsevier, vol. 27(3), pages 923-941, July.
    37. Bhattacharjee, Arnab & de Castro, Eduardo Anselmo & Marques, João Lourenço, 2011. "Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal," SIRE Discussion Papers 2011-45, Scottish Institute for Research in Economics (SIRE).
    38. Auffhammer, Maximilian & Carson, Richard T., 2007. "Forecasting the Path of China's CO2 Emissions Using Province Level Information," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt6d28j8rg, Department of Agricultural & Resource Economics, UC Berkeley.
    39. Helena Marques & Gabriel Pino & J.D.Tena, 2009. "Regional inflation dynamics using space-time models," DEA Working Papers 40, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    40. Carlomagno, Guillermo & Espasa, Antoni, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
    41. Marco Capasso & Koen Frenken & Tania Treibich, 2017. "Sectoral co-movements of employment growth at regional level," Economic Systems Research, Taylor & Francis Journals, vol. 29(1), pages 82-104, January.
    42. Hao, Yu & Zhang, Zong-Yong & Liao, Hua & Wei, Yi-Ming, 2015. "China’s farewell to coal: A forecast of coal consumption through 2020," Energy Policy, Elsevier, vol. 86(C), pages 444-455.
    43. Espasa, Antoni & Mayo-Burgos, Iván, 2013. "Forecasting aggregates and disaggregates with common features," International Journal of Forecasting, Elsevier, vol. 29(4), pages 718-732.
    44. Herrera Gómez, Marcos, 2017. "Fundamentos de Econometría Espacial Aplicada [Fundamentals of Applied Spatial Econometrics]," MPRA Paper 80871, University Library of Munich, Germany.
    45. Mao, Guangyu & Shen, Yan, 2019. "Bubbles or fundamentals? Modeling provincial house prices in China allowing for cross-sectional dependence," China Economic Review, Elsevier, vol. 53(C), pages 53-64.
    46. Badi H. Baltagi, 2007. "Forecasting with Panel Data," Center for Policy Research Working Papers 91, Center for Policy Research, Maxwell School, Syracuse University.
    47. M. Mayor-Fern ndez & R. Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Working Papers wp835, Dipartimento Scienze Economiche, Universita' di Bologna.
    48. Auffhammer, Maximilian & Steinhauser, Ralf, 2006. "The Future Trajectory of US CO2 Emissions: The Role of State vs. Aggregate Information," CUDARE Working Papers 7157, University of California, Berkeley, Department of Agricultural and Resource Economics.
    49. Juan de Dios Tena & Antoni Espasa & Gabriel Pino, 2010. "Forecasting Spanish Inflation Using the Maximum Disaggregation Level by Sectors and Geographical Areas," International Regional Science Review, , vol. 33(2), pages 181-204, April.
    50. E. Otranto & Mucciardi, 2016. "A Flexible Specification of Space–Time AutoRegressive Models," Working Paper CRENoS 201608, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    51. Frédérick Demers & David Dupuis, 2005. "Forecasting Canadian GDP: Region-Specific versus Countrywide Information," Staff Working Papers 05-31, Bank of Canada.
    52. Hengzhou Xu & Chuanrong Zhang & Weidong Li & Wenjing Zhang & Hongchun Yin, 2018. "Economic growth and carbon emission in China: a spatial econometric Kuznets curve?," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 36(1), pages 11-28.
    53. Arnab Bhattacharjee & Chris Jensen-Butler, 2011. "Estimation of the Spatial Weights Matrix under Structural Constraints," Dundee Discussion Papers in Economics 254, Economic Studies, University of Dundee.
    54. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    55. Marco Percoco, 2007. "Evaluating forecasting accuracy of the temporally aggregated space-time autoregressive model," Applied Economics Letters, Taylor & Francis Journals, vol. 14(9), pages 637-641.
    56. Lambert, Dayton M. & Malzer, Gary L. & Lowenberg-DeBoer, James, 2004. "General Moment And Quasi-Maximum Likelihood Estimation Of A Spatially Autocorrelated System Of Equations: An Empirical Example Using On-Farm Precision Agriculture Data," Staff Papers 28667, Purdue University, Department of Agricultural Economics.
    57. Karamaziotis, Panagiotis I. & Raptis, Achilleas & Nikolopoulos, Konstantinos & Litsiou, Konstantia & Assimakopoulos, Vassilis, 2020. "An empirical investigation of water consumption forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(2), pages 588-606.
    58. Guillermo Carlomagno & Nicolas Eterovic & L. G. Hernández-Román, 2023. "Disentangling Demand and Supply Inflation Shocks from Chilean Electronic Payment Data," Working Papers Central Bank of Chile 986, Central Bank of Chile.
    59. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    60. Tena Horrillo, Juan de Dios & Espasa, Antoni & Pino, Gabriel, 2008. "Forecasting Spanish inflation using information from different sectors and geographical areas," DES - Working Papers. Statistics and Econometrics. WS ws080101, Universidad Carlos III de Madrid. Departamento de Estadística.
    61. Bhattacharjee, Arnab & Holly, Sean, 2009. "Understanding Interactions in Social Networks and Committees," SIRE Discussion Papers 2009-53, Scottish Institute for Research in Economics (SIRE).
    62. Xiaoxia Shi & Peter C. B. Phillips, 2010. "Nonlinear Cointegrating Regression under Weak Identification," Cowles Foundation Discussion Papers 1768, Cowles Foundation for Research in Economics, Yale University.
    63. Ruben Hernandez-Murillo & Michael T. Owyang, 2004. "The information content of regional employment data for forecasting aggregate conditions," Working Papers 2004-005, Federal Reserve Bank of St. Louis.
    64. You, Jing, 2013. "China's challenge for decarbonized growth: Forecasts from energy demand models," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 652-668.
    65. Caporin Massimiliano & Paruolo Paolo, 2005. "Spatial effects in multivariate ARCH," Economics and Quantitative Methods qf0501, Department of Economics, University of Insubria.
    66. Peter M Robinson, 2009. "Developments in the Analysis of Spatial Data," STICERD - Econometrics Paper Series 531, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    67. Chun Liu & Gui-hua Nie, 2021. "Identifying the Driving Factors of Food Nitrogen Footprint in China, 2000–2018: Econometric Analysis of Provincial Spatial Panel Data by the STIRPAT Model," Sustainability, MDPI, vol. 13(11), pages 1-23, May.
    68. Prodosh Simlai, 2018. "Spatial Dependence, Idiosyncratic Risk, and the Valuation of Disaggregated Housing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 192-230, August.
    69. Youri Davydov & Vygantas Paulauskas, 2008. "On estimation of parameters for spatial autoregressive model," Statistical Inference for Stochastic Processes, Springer, vol. 11(3), pages 237-247, October.
    70. Auffhammer, Maximilian & Carson, Richard T., 2006. "Forecasting the Path of China's CO2 Emissions: Offsetting Kyoto - and Then Some," CUDARE Working Papers 7197, University of California, Berkeley, Department of Agricultural and Resource Economics.
    71. E. Otranto & Mucciardi, 2017. "Clustering Space-Time Series: A Flexible STAR Approach," Working Paper CRENoS 201707, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    72. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas F. Quising, 2007. "Measuring Regional Market Integration in Developing Asia: a Dynamic Factor Error Correction Model (DF-ECM) Approach," Working Papers on Regional Economic Integration 8, Asian Development Bank.
    73. Kristie M. Engemann & Ruben Hernandez-Murillo & Michael T. Owyang, 2011. "Regional aggregation in forecasting: an application to the Federal Reserve’s Eighth District," Review, Federal Reserve Bank of St. Louis, vol. 93(May), pages 207-222.
    74. Xueting Zhao & J. Burnett, 2014. "Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China," Letters in Spatial and Resource Sciences, Springer, vol. 7(3), pages 171-183, October.
    75. Massimiliano Agovino & Antonio Garofalo, 2013. "Dipendenza spaziale contemporanea e non contemporanea nei tassi di disoccupazione: un tentativo di analisi empirica dei dati provinciali italiani," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2013(3), pages 45-82.
    76. Xiangyu Ge & Zhimin Zhou & Yanli Zhou & Xinyue Ye & Songlin Liu, 2018. "A Spatial Panel Data Analysis of Economic Growth, Urbanization, and NO x Emissions in China," IJERPH, MDPI, vol. 15(4), pages 1-20, April.
    77. Yuxin Meng & Lu Liu & Qiying Ran, 2022. "Can Urban Green Transformation Reduce the Urban–Rural Income Gap? Empirical Evidence Based on Spatial Durbin Model and Mediation Effect Model," Sustainability, MDPI, vol. 14(24), pages 1-24, December.
    78. Robinson, Peter, 2008. "Developments in the analysis of spatial data," LSE Research Online Documents on Economics 25473, London School of Economics and Political Science, LSE Library.
    79. Girum D. Abate & Niels Haldrup, 2015. "Space-time modeling of electricity spot prices," CREATES Research Papers 2015-22, Department of Economics and Business Economics, Aarhus University.
    80. Beidi Diao & Lei Ding & Panda Su & Jinhua Cheng, 2018. "The Spatial-Temporal Characteristics and Influential Factors of NOx Emissions in China: A Spatial Econometric Analysis," IJERPH, MDPI, vol. 15(7), pages 1-19, July.

  30. Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal densities," Economics Working Papers 638, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. Alexandre Carvalho & Georgios Skoulakis, 2010. "Time Series Mixtures of Generalized t Experts: ML Estimation and an Application to Stock Return Density Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 642-687.

Articles

  1. Raffaella Giacomini & Toru Kitagawa, 2021. "Robust Bayesian Inference for Set‐Identified Models," Econometrica, Econometric Society, vol. 89(4), pages 1519-1556, July.
    See citations under working paper version above.
  2. Raffaella Giacomini & Vasiliki Skreta & Javier Turen, 2020. "Heterogeneity, Inattention, and Bayesian Updates," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(1), pages 282-309, January.

    Cited by:

    1. An, Zidong & Zheng, Xinye, 2023. "Diligent forecasters can make accurate predictions despite disagreeing with the consensus," Economic Modelling, Elsevier, vol. 125(C).
    2. Serafin Frache & Rodrigo Lluberas & Javier Turen, 2021. "Belief-Dependent Pricing Decisions," Documentos de Trabajo 564, Instituto de Economia. Pontificia Universidad Católica de Chile..
    3. Fiechter, Chad M. & Kuethe, Todd H. & Zhang, Wendong, 2022. "Information Rigidities in Farmland Value Expectations," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322070, Agricultural and Applied Economics Association.
    4. Ehrmann, Michael & Hubert, Paul, 2026. "Information acquisition ahead of monetary policy announcements," European Economic Review, Elsevier, vol. 184(C).
    5. Qiu, Yajie & Deschamps, Bruno, 2025. "Peer influence in macroeconomic predictions," Journal of Economic Behavior & Organization, Elsevier, vol. 236(C).
    6. Lahiri, Kajal & Zhao, Yongchen, 2020. "The Nordhaus test with many zeros," Economics Letters, Elsevier, vol. 193(C).
    7. An, Zidong & Sheng, Xuguang Simon & Zheng, Xinye, 2023. "What is the role of perceived oil price shocks in inflation expectations?," Energy Economics, Elsevier, vol. 126(C).
    8. Alexandra Belova & Philippe Gagnepain & Stéphane Gauthier, 2020. "An assessment of Nash equilibria in the airline industry," Working Papers halshs-02932780, HAL.
    9. Ghosh, Aniruddha & Khan, M. Ali, 2021. "On a diversity of perspectives and world views: Learning under Bayesian vis-á-vis DeGroot updating," Economics Letters, Elsevier, vol. 202(C).
    10. Czudaj, Robert L., 2022. "Heterogeneity of beliefs and information rigidity in the crude oil market: Evidence from survey data," European Economic Review, Elsevier, vol. 143(C).
    11. Christopher S Sutherland, 2022. "Forward guidance and expectation formation: A narrative approach," BIS Working Papers 1024, Bank for International Settlements.
    12. Gilboa, Itzhak & Minardi, Stefania & Samuelson, Larry, 2020. "Theories and cases in decisions under uncertainty," Games and Economic Behavior, Elsevier, vol. 123(C), pages 22-40.
    13. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    14. Yanwei Jia & Jussi Keppo & Ville Satopää, 2023. "Herding in Probabilistic Forecasts," Management Science, INFORMS, vol. 69(5), pages 2713-2732, May.
    15. Isaac Baley & Javier Turen, 2024. "Lumpy forecasts," Economics Working Papers 1898, Department of Economics and Business, Universitat Pompeu Fabra.
    16. Gilboa, Itzhak & Samuelson, Larry & Schmeidler, David, 2022. "Learning (to disagree?) in large worlds," Journal of Economic Theory, Elsevier, vol. 199(C).
    17. Qiu, Yajie & Deschamps, Bruno & Liu, Xiaoquan, 2024. "Uncertainty and macroeconomic forecasts: Evidence from survey data," Journal of Economic Behavior & Organization, Elsevier, vol. 224(C), pages 463-480.
    18. Brian Hill, 2022. "Updating confidence in beliefs," Post-Print hal-03503986, HAL.
    19. Christopher S. Sutherland, 2020. "Forward Guidance and Expectation Formation: A Narrative Approach," Staff Working Papers 20-40, Bank of Canada.
    20. Hill, Brian, 2022. "Updating confidence in beliefs," Journal of Economic Theory, Elsevier, vol. 199(C).
    21. Keppo, Jussi & Satopää, Ville A., 2024. "Bayesian herd detection for dynamic data," International Journal of Forecasting, Elsevier, vol. 40(1), pages 285-301.
    22. Zidong An & Salem Abo‐Zaid & Xuguang Simon Sheng, 2023. "Inattention and the impact of monetary policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 623-643, June.

  3. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2017. "Anchoring the yield curve using survey expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1055-1068, September.
    See citations under working paper version above.
  4. Gallant, A. Ronald & Giacomini, Raffaella & Ragusa, Giuseppe, 2017. "Bayesian estimation of state space models using moment conditions," Journal of Econometrics, Elsevier, vol. 201(2), pages 198-211.

    Cited by:

    1. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org, revised Jan 2026.
    2. Mike G. Tsionas & Dionisis Philippas, 2023. "Measures of global sensitivity in linear programming: applications in banking sector," Annals of Operations Research, Springer, vol. 330(1), pages 585-607, November.
    3. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    4. Thorsten Drautzburg & Jesus Fernandez-Villaverde & Pablo Guerron-Quintana & Dick Oosthuizen, 2024. "Filtering with Limited Information," PIER Working Paper Archive 24-016, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Tsionas, Mike G. & Malikov, Emir & Kumbhakar, Subal C., 2019. "Endogenous Dynamic Efficiency in the Intertemporal Optimization Models of Firm Behavior," MPRA Paper 97780, University Library of Munich, Germany.
    6. Andreas Tryphonides, 2018. "Tilting Approximate Models," Papers 1805.10869, arXiv.org, revised Mar 2024.
    7. Takashi Kano, 2026. "Distribution-Matching Posterior Inference for Incomplete Structural Models," Papers 2601.01077, arXiv.org, revised Oct 2026.
    8. Yasufumi Gemma & Takushi Kurozumi & Mototsugu Shintani, 2017. "Trend Inflation and Evolving Inflation Dynamics: A Bayesian GMM Analysis of the Generalized New Keynesian Phillips Curve," IMES Discussion Paper Series 17-E-10, Institute for Monetary and Economic Studies, Bank of Japan.
    9. Tsionas, Mike & Patel, Pankaj C. & Guedes, Maria João, 2022. "Endogenous efficiency of the dynamic profit maximization in the intertemporal production models of venture behavior," International Journal of Production Economics, Elsevier, vol. 246(C).
    10. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Productivity and Performance: A GMM approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(2), pages 331-344, April.
    11. Tsionas, Mike G., 2020. "Directional technology distance functions through duality," Economics Letters, Elsevier, vol. 190(C).
    12. Gagliardini, Patrick & Gouriéroux, Christian, 2019. "Identification by Laplace transforms in nonlinear time series and panel models with unobserved stochastic dynamic effects," Journal of Econometrics, Elsevier, vol. 208(2), pages 613-637.
    13. Kien C. Tran & Panayotis G. Michaelides, 2025. "A class of generalized autoregressive score panel stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 64(3), pages 235-253, December.
    14. A. Ronald Gallant, 2020. "Complementary Bayesian method of moments strategies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 422-439, June.

  5. Raffaella Giacomini & Barbara Rossi, 2016. "Model Comparisons In Unstable Environments," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(2), pages 369-392, May.
    See citations under working paper version above.
  6. Raffaella Giacomini, 2015. "Economic theory and forecasting: lessons from the literature," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 22-41, June.
    See citations under working paper version above.
  7. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    See citations under working paper version above.
  8. Carlo Altavilla & Raffaella Giacomini & Riccardo Costantini, 2014. "Bond Returns and Market Expectations," Journal of Financial Econometrics, Oxford University Press, vol. 12(4), pages 708-729.
    See citations under working paper version above.
  9. Giacomini, Raffaella & Ragusa, Giuseppe, 2014. "Theory-coherent forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 145-155.

    Cited by:

    1. Davide Pettenuzzo & Konstantinos Metaxoglou & Aaron Smith, 2016. "Option-Implied Equity Premium Predictions via Entropic TiltinG," Working Papers 99R, Brandeis School of Business and Economics, revised Aug 2016.
    2. Oh, Dong Hwan & Patton, Andrew J., 2024. "Better the devil you know: Improved forecasts from imperfect models," Journal of Econometrics, Elsevier, vol. 242(1).
    3. Gökhan Ider & Alexander Kriwoluzky & Frederik Kurcz & Ben Schumann, 2023. "The Energy-Price Channel of (European) Monetary Policy," Discussion Papers of DIW Berlin 2033, DIW Berlin, German Institute for Economic Research.
    4. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    5. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, Centre for Economic Policy Research.
    6. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," Working Papers 20-27, Federal Reserve Bank of Cleveland.
    7. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    8. Breitenlechner, Max & Georgiadis, Georgios & Schumann, Ben, 2022. "What goes around comes around: How large are spillbacks from US monetary policy?," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 45-60.
    9. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Working Papers 819, Barcelona School of Economics.
    10. Komunjer, Ivana & Ragusa, Giuseppe, 2016. "Existence And Characterization Of Conditional Density Projections," Econometric Theory, Cambridge University Press, vol. 32(4), pages 947-987, August.
    11. Carlo A. Favero & Arie E. Gozluklu & Haoxi Yang, 2016. "Demographics and the Behavior of Interest Rates," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 64(4), pages 732-776, November.
    12. Neely, Christopher J., 2022. "How persistent are unconventional monetary policy effects?," Journal of International Money and Finance, Elsevier, vol. 126(C).
    13. Montes-Galdón, Carlos & Paredes, Joan & Wolf, Elias, 2026. "A robust approach to tilting: parametric relative entropy," Working Paper Series 3200, European Central Bank.
    14. Clements, Michael P., 2016. "Long-run restrictions and survey forecasts of output, consumption and investment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 614-628.
    15. Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021. "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series 2543, European Central Bank.
    16. Rhys M. Bidder & Raffaella Giacomini & Andrew McKenna, 2016. "Stress Testing with Misspecified Models," Working Paper Series 2016-26, Federal Reserve Bank of San Francisco.
    17. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    18. Rhys M. Bidder & Andrew McKenna, 2015. "Robust stress testing," Working Paper Series 2015-13, Federal Reserve Bank of San Francisco.
    19. Georgiadis, Georgios & Müller, Gernot J. & Schumann, Ben, 2021. "Global risk and the dollar," Working Paper Series 2628, European Central Bank.
    20. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
    21. Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021. "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 798-815.
    22. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    23. Tae-Hwy Lee & He Wang & Zhou Xi & Ru Zhang, 2021. "Density Forecast of Financial Returns Using Decomposition and Maximum Entropy," Working Papers 202115, University of California at Riverside, Department of Economics.
    24. Montes-Galdón, Carlos & Paredes, Joan & Wolf, Elias, 2022. "Conditional density forecasting: a tempered importance sampling approach," Working Paper Series 2754, European Central Bank.

  10. Giacomini, Raffaella & Politis, Dimitris N. & White, Halbert, 2013. "A Warp-Speed Method For Conducting Monte Carlo Experiments Involving Bootstrap Estimators," Econometric Theory, Cambridge University Press, vol. 29(3), pages 567-589, June.
    See citations under working paper version above.
  11. Carriero, Andrea & Giacomini, Raffaella, 2011. "How useful are no-arbitrage restrictions for forecasting the term structure of interest rates?," Journal of Econometrics, Elsevier, vol. 164(1), pages 21-34, September.
    See citations under working paper version above.
  12. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    See citations under working paper version above.
  13. Raffaella Giacomini & Barbara Rossi, 2009. "Detecting and Predicting Forecast Breakdowns," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(2), pages 669-705.
    See citations under working paper version above.
  14. Giacomini, Raffaella & Gottschling, Andreas & Haefke, Christian & White, Halbert, 2008. "Mixtures of t-distributions for finance and forecasting," Journal of Econometrics, Elsevier, vol. 144(1), pages 175-192, May.
    See citations under working paper version above.
  15. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    See citations under working paper version above.
  16. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    See citations under working paper version above.
  17. Raffaella Giacomini & Barbara Rossi, 2006. "How Stable is the Forecasting Performance of the Yield Curve for Output Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 783-795, December.
    See citations under working paper version above.
  18. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    See citations under working paper version above.
  19. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
    See citations under working paper version above.

Chapters

  1. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.

    Cited by:

    1. Serafin, Tomasz & Weron, Rafał, 2025. "Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading," Energy Economics, Elsevier, vol. 148(C).
    2. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    3. Nicolau, Mihaela & Palomba, Giulio, 2015. "Dynamic relationships between spot and futures prices. The case of energy and gold commodities," Resources Policy, Elsevier, vol. 45(C), pages 130-143.
    4. Amendola, Alessandra & Candila, Vincenzo & Scognamillo, Antonio, 2015. "On the influence of the U.S. monetary policy on the crude oil price volatility," 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy 207860, Italian Association of Agricultural and Applied Economics (AIEAA).

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.