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Atsushi Inoue

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Yasuo Hirose & Atsushi Inoue, 2014. "The zero lower bound and parameter bias in an estimated DSGE model," Vanderbilt University Department of Economics Working Papers 14-00009, Vanderbilt University Department of Economics.

    Mentioned in:

    1. The zero lower bound and parameter bias in an estimated DSGE model
      by Christian Zimmermann in NEP-DGE blog on 2015-05-06 01:00:23

Working papers

  1. Atsushi Inoue & Tong Li & Qi Xu, 2021. "Two Sample Unconditional Quantile Effect," Papers 2105.09445, arXiv.org.

    Cited by:

    1. Julián Martínez-Iriarte & Gabriel Montes-Rojas & Yixiao Sun, 2022. "Location-Scale and Compensated Effects in Unconditional Quantile Regressions," Working Papers 127, Red Nacional de Investigadores en Economía (RedNIE).
    2. Javier Alejo & Antonio F. Galvao & Julian Martinez-Iriarte & Gabriel Montes-Rojas, 2023. "Unconditional Quantile Partial Effects via Conditional Quantile Regression," Papers 2301.07241, arXiv.org, revised Dec 2023.

  2. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.

    Cited by:

    1. Luca Baldo & Elisa Bonifacio & Marco Brandi & Michelina Lo Russo & Gianluca Maddaloni & Andrea Nobili & Giorgia Rocco & Gabriele Sene & Massimo Valentini, 2021. "Inside the black box: tools for understanding cash circulation," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 7, Bank of Italy, Directorate General for Markets and Payment System.
    2. 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.
    3. Kilian, Lutz, 2019. "Facts and Fiction in Oil Market Modeling," CEPR Discussion Papers 14047, C.E.P.R. Discussion Papers.
    4. Carrillo-Maldonado, Paul & Díaz-Cassou, Javier, 2023. "An anatomy of external shocks in the Andean region," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    5. Atsushi Inoue & Lutz Kilian, 2020. "Joint Bayesian Inference about Impulse Responses in VAR Models," Working Papers 2022, Federal Reserve Bank of Dallas.
    6. Kilian, Lutz, 2020. "Understanding the estimation of oil demand and oil supply elasticities," CFS Working Paper Series 649, Center for Financial Studies (CFS).
    7. Lukas Boer & Andrea Pescatori & Martin Stuermer, 2021. "Energy Transition Metals," Discussion Papers of DIW Berlin 1976, DIW Berlin, German Institute for Economic Research.
    8. Katarzyna Budnik & Gerhard Rünstler, 2023. "Identifying structural VARs from sparse narrative instruments: Dynamic effects of US macroprudential policies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 186-201, March.
    9. Ronicle, David, 2022. "Turning in the widening gyre: monetary and fiscal policy in interwar Britain," Bank of England working papers 968, Bank of England.
    10. 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).
    11. Khalil, Makram & Weber, Marc-Daniel, 2022. "Chinese supply chain shocks," Discussion Papers 44/2022, Deutsche Bundesbank.
    12. Erik Andres-Escayola & Juan Carlos Berganza & Rodolfo Campos & Luis Molina, 2021. "A BVAR toolkit to assess macrofinancial risks in Brazil and Mexico," Occasional Papers 2114, Banco de España.
    13. Martin Stuermer, 2022. "Non-renewable resource extraction over the long term: empirical evidence from global copper production," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(3), pages 617-625, December.
    14. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    15. 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).

  3. Atsushi Inoue & Lutz Kilian, 2020. "Joint Bayesian Inference about Impulse Responses in VAR Models," Working Papers 2022, Federal Reserve Bank of Dallas.

    Cited by:

    1. Finck, David & Tillmann, Peter, 2022. "The macroeconomic effects of global supply chain disruptions," BOFIT Discussion Papers 14/2022, Bank of Finland Institute for Emerging Economies (BOFIT).
    2. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    3. Berger, Tino & Richter, Julia & Wong, Benjamin, 2021. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," University of Göttingen Working Papers in Economics 415, University of Goettingen, Department of Economics.
    4. Lutz Kilian & Nikos Nomikos & Xiaoqing Zhou, 2020. "A Quantitative Model of the Oil Tanker Market in the Arabian Gulf," Working Papers 2015, Federal Reserve Bank of Dallas.
    5. Lucrezia Reichlin & Giovanni Ricco & Matthieu Tarbé, 2021. "Monetary-Fiscal Crosswinds in the European Monetary Union," SciencePo Working papers Main hal-03474950, HAL.
    6. Lutz Kilian & Xiaoqing Zhou, 2023. "Oil Price Shocks and Inflation," Working Papers 2312, Federal Reserve Bank of Dallas.
    7. Kilian, Lutz & Zhou, Xiaoqing, 2023. "A broader perspective on the inflationary effects of energy price shocks," CFS Working Paper Series 686, Center for Financial Studies (CFS).
    8. Lutz Kilian & Xiaoqing Zhou, 2019. "Oil Prices, Exchange Rates and Interest Rates," Working Papers 1914, Federal Reserve Bank of Dallas.
    9. Lutz Kilian & Xiaoqing Zhou, 2021. "The Impact of Rising Oil Prices on U.S. Inflation and Inflation Expectations in 2020-23," Working Papers 2116, Federal Reserve Bank of Dallas.
    10. 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).
    11. Lutz Kilian, 2023. "How to Construct Monthly VAR Proxies Based on Daily Futures Market Surprises," Working Papers 2310, Federal Reserve Bank of Dallas.
    12. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2023. "Identification and Inference under Narrative Restrictions," RBA Research Discussion Papers rdp2023-07, Reserve Bank of Australia.
    13. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    14. Diab, Sara & Karaki, Mohamad B., 2023. "Do increases in gasoline prices cause higher food prices?," Energy Economics, Elsevier, vol. 127(PB).
    15. Finck, David & Tillmann, Peter, 2023. "The macroeconomic effects of global supply chain disruptions," IMFS Working Paper Series 178, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    16. Bruns, Martin, 2021. "Proxy Vector Autoregressions in a Data-rich Environment," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).

  4. 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.

    Cited by:

    1. Luisa Corrado & Daniela Fantozzi & Simona Giglioli, 2022. "Real-time ineuqalities and policies during the pandemic in the US," Temi di discussione (Economic working papers) 1396, Bank of Italy, Economic Research and International Relations Area.
    2. Lance A. Fisher & Hyeon-seung Huh, 2022. "Systematic Monetary Policy in a SVAR for Australia," Working papers 2022rwp-194, Yonsei University, Yonsei Economics Research Institute.
    3. Yoosoon Chang & Fabio Gómez-Rodríguez & Christian Matthes, 2023. "The Influence of Fiscal and Monetary Policies on the Shape of the Yield Curve," CAMA Working Papers 2023-65, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. 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.
    5. De Santis, Roberto A., 2020. "Impact of the Asset Purchase Programme on euro area government bond yields using market news," Economic Modelling, Elsevier, vol. 86(C), pages 192-209.
    6. Ca' Zorzi, Michele & Dedola, Luca & Georgiadis, Georgios & Jarociński, Marek & Stracca, Livio & Strasser, Georg, 2020. "Monetary policy and its transmission in a globalised world," Working Paper Series 2407, European Central Bank.
    7. Chunya Bu & John Rogers & Wenbin Wu, 2019. "A Unified Measure of Fed Monetary Policy Shocks," Finance and Economics Discussion Series 2019-043, Board of Governors of the Federal Reserve System (U.S.).
    8. Robert Adamek & Stephan Smeekes & Ines Wilms, 2022. "Local Projection Inference in High Dimensions," Papers 2209.03218, arXiv.org, revised Apr 2024.
    9. Brubakk, Leif & ter Ellen, Saskia & Robstad, Ørjan & Xu, Hong, 2019. "The macroeconomic effects of forward communication," Working Paper 2019/20, Norges Bank.
    10. Rüth, Sebastian K., 2019. "Shifts in Monetary Policy and Exchange Rate Dynamics: Is Dornbusch's Overshooting Hypothesis Intact, After all?," Working Papers 0673, University of Heidelberg, Department of Economics.
    11. Jamie L. Cross & Aubrey Poon & Dan Zhu, 2023. "Uncertainty and the Term Structure of Interest Rates," Working Papers No 12/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    12. Oliver Holtemöller & Alexander Kriwoluzky & Boreum Kwak, 2020. "Exchange Rates and the Information Channel of Monetary Policy," Discussion Papers of DIW Berlin 1906, DIW Berlin, German Institute for Economic Research.
    13. Yuriy Kitsul & Oleg Sokolinskiy & Jonathan H. Wright, 2022. "Market Effects of Central Bank Credit Markets Support Programs in Europe," International Finance Discussion Papers 1357, Board of Governors of the Federal Reserve System (U.S.).
    14. Christina Anderl & Guglielmo Maria Caporale, 2023. "Functional Shocks to Inflation Expectations and Real Interest Rates and Their Macroeconomic Effects," CESifo Working Paper Series 10656, CESifo.
    15. Martin, Feldkircher & Thomas, Gruber & Florian, Huber, 2019. "International effects of a compression of euro area yield curves," Working Papers in Economics 2019-1, University of Salzburg.
    16. Alisdair McKay & Christian K. Wolf, 2023. "What Can Time-Series Regressions Tell Us About Policy Counterfactuals?," Staff Report 642, Federal Reserve Bank of Minneapolis.
    17. Daniel J. Lewis, 2019. "Announcement-Specific Decompositions of Unconventional Monetary Policy Shocks and Their Macroeconomic Effects," Staff Reports 891, Federal Reserve Bank of New York.
    18. Hilde C. Bjornland & Yoosoon Chang & Jamie L. Cross, 2023. "Oil and the Stock Market Revisited: A Mixed Functional VAR Approach," CAEPR Working Papers 2023-005 Classification-1, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    19. Rasmus Fatum & Naoko Hara & Yohei Yamamoto, 2019. "Negative Interest Rate Policy and the Influence of Macroeconomic News on Yields," IMES Discussion Paper Series 19-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    20. Leonardo Nogueira Ferreira, 2023. "Monetary Policy Surprises, Financial Conditions, and the String Theory Revisited," Working Papers Series 573, Central Bank of Brazil, Research Department.
    21. 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).
    22. Christina Anderl & Guglielmo Maria Caporale, 2024. "Functional Oil Price Expectations Shocks and Inflation," CESifo Working Paper Series 10998, CESifo.
    23. Shixuan Wang & Rangan Gupta & Matteo Bonato & Oguzhan Cepni, 2022. "The Effects of Conventional and Unconventional Monetary Policy Shocks on US REITs Moments: Evidence from VARs with Functional Shocks," Working Papers 202219, University of Pretoria, Department of Economics.
    24. Thi Bich Ngoc Tran & Hoang Cam Huong Pham, 2020. "The Spillover Effects of the US Unconventional Monetary Policy: New Evidence from Asian Developing Countries," JRFM, MDPI, vol. 13(8), pages 1-26, July.
    25. Zoe Venter, 2019. "The Interaction Between ConventionalMonetary Policy and Financial Stability: Chile, Colombia, Japan, Portugal and the UK," Working Papers REM 2019/96, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    26. Eva Ortega & Chiara Osbat, 2020. "Exchange rate pass-through in the euro area and EU countries," Occasional Papers 2016, Banco de España.
    27. Stavrakeva, Vania & Tang, Jenny, 2019. "The Dollar During the Great Recession: US Monetary Policy Signaling and The Flight To Safety," CEPR Discussion Papers 14034, C.E.P.R. Discussion Papers.
    28. Fisher, Lance A. & Huh, Hyeon-seung, 2023. "Systematic monetary policy in a SVAR for Australia," Economic Modelling, Elsevier, vol. 128(C).

  5. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.

    Cited by:

    1. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    2. Dake Li & Mikkel Plagborg-Møller & Christian K. Wolf, 2021. "Local Projections vs. VARs: Lessons From Thousands of DGPs," Working Papers 2021-55, Princeton University. Economics Department..
    3. Ke-Li Xu, 2022. "On Local Projection Based Inference," CAEPR Working Papers 2022-002 Classification-, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    4. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    5. Ke-Li Xu, 2023. "Local Projection Based Inference under General Conditions," CAEPR Working Papers 2023-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Lutz Kilian & Xiaoqing Zhou, 2020. "The Econometrics of Oil Market VAR Models," CESifo Working Paper Series 8153, CESifo.
    7. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Inference in Non-stationary High-Dimensional VARs," Papers 2302.01434, arXiv.org, revised Sep 2023.
    8. Yuanyuan Li & Dietmar Bauer, 2020. "Modeling I(2) Processes Using Vector Autoregressions Where the Lag Length Increases with the Sample Size," Econometrics, MDPI, vol. 8(3), pages 1-28, September.
    9. Olatunji Abdul Shobande & Joseph Onuche Enemona, 2021. "A Multivariate VAR Model for Evaluating Sustainable Finance and Natural Resource Curse in West Africa: Evidence from Nigeria and Ghana," Sustainability, MDPI, vol. 13(5), pages 1-15, March.

  6. Atsushi Inoue & Barbara Rossi, 2018. "The Effects of Conventional and Unconventional Monetary Policy on Exchange Rates," Working Papers 1078, Barcelona School of Economics.

    Cited by:

    1. Refet S. Gürkaynak & Burcin Kisacikoglu & Sang Seok Lee, 2022. "Exchange Rate and Inflation under Weak Monetary Policy: Turkey Verifies Theory," CESifo Working Paper Series 9748, CESifo.
    2. Callum Jones & Mariano Kulish & Daniel M. Rees, 2018. "International Spillovers of Forward Guidance Shocks," IMF Working Papers 2018/114, International Monetary Fund.
    3. Gelfer, Sacha & Gibbs, Christopher G., 2023. "Measuring the effects of large-scale asset purchases: The role of international financial markets and the financial accelerator," Journal of International Money and Finance, Elsevier, vol. 131(C).
    4. De Santis, Roberto A., 2020. "Impact of the Asset Purchase Programme on euro area government bond yields using market news," Economic Modelling, Elsevier, vol. 86(C), pages 192-209.
    5. 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.
    6. Silvia Miranda-Agrippino & Tsvetelina Nenova, 2021. "A Tale of Two Global Monetary Policies," NBER Chapters, in: NBER International Seminar on Macroeconomics 2021, National Bureau of Economic Research, Inc.
    7. Martin Feldkircher & Florian Huber, 2016. "Unconventional US Monetary Policy: New Tools, Same Channels?," Department of Economics Working Papers wuwp222, Vienna University of Economics and Business, Department of Economics.
    8. Dalhaus, Tatjana & Schaumburg, Julia & Sekhposyan, Tatevik, 2021. "Networking the yield curve: implications for monetary policy," Working Paper Series 2532, European Central Bank.
    9. Ca' Zorzi, Michele & Dedola, Luca & Georgiadis, Georgios & Jarociński, Marek & Stracca, Livio & Strasser, Georg, 2020. "Monetary policy and its transmission in a globalised world," Working Paper Series 2407, European Central Bank.
    10. Daisuke Ikeda & Shangshang Li & Sophocles Mavroeidis & Francesco Zanetti, 2022. "Testing the Effectiveness of Unconventional Monetary Policy in Japan and the United States," CAMA Working Papers 2022-68, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. Chunya Bu & John Rogers & Wenbin Wu, 2019. "A Unified Measure of Fed Monetary Policy Shocks," Finance and Economics Discussion Series 2019-043, Board of Governors of the Federal Reserve System (U.S.).
    12. Carlos Esteban Posada, 2023. "Inflation targeting strategy and its credibility," Papers 2301.11207, arXiv.org.
    13. Brubakk, Leif & ter Ellen, Saskia & Robstad, Ørjan & Xu, Hong, 2019. "The macroeconomic effects of forward communication," Working Paper 2019/20, Norges Bank.
    14. Daniel Gründler & Eric Mayer & Johann Scharler, 2021. "Monetary Policy Announcements, Information Schocks, and Exchange Rate Dynamics," Working Papers 2021-16, Faculty of Economics and Statistics, Universität Innsbruck.
    15. Julian di Giovanni & Galina Hale, 2020. "Stock market spillovers via the global production network: Transmission of U.S. monetary policy," Economics Working Papers 1747, Department of Economics and Business, Universitat Pompeu Fabra.
    16. Shang, Fei, 2022. "The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    17. Rüth, Sebastian K., 2019. "Shifts in Monetary Policy and Exchange Rate Dynamics: Is Dornbusch's Overshooting Hypothesis Intact, After all?," Working Papers 0673, University of Heidelberg, Department of Economics.
    18. Mirela Miescu, 2022. "Forward guidance shocks," Working Papers 352591340, Lancaster University Management School, Economics Department.
    19. Madison Terrell & Qazi Haque & Jamie L. Cross & Firmin Doko Tchatoka, 2023. "Monetary policy shocks and exchange rate dynamics in small open economies," School of Economics and Public Policy Working Papers 2023-04 Classification-C3, University of Adelaide, School of Economics and Public Policy.
    20. Oliver Holtemöller & Alexander Kriwoluzky & Boreum Kwak, 2020. "Exchange Rates and the Information Channel of Monetary Policy," Discussion Papers of DIW Berlin 1906, DIW Berlin, German Institute for Economic Research.
    21. Coenen, Günter & Montes-Galdón, Carlos & Saint Guilhem, Arthur & Hutchinson, John & Motto, Roberto, 2022. "Rate forward guidance in an environment of large central bank balance sheets: a Eurosystem stock-taking assessment," Occasional Paper Series 290, European Central Bank.
    22. Shahriyar Aliev & Evžen Kočenda, 2022. "ECB monetary policy and commodity prices," FFA Working Papers 4.008, Prague University of Economics and Business, revised 21 Jun 2022.
    23. Radeef Chundakkadan & Subash Sasidharan, 2021. "Monetary Policy Announcement and Stock Returns - Evidence From Long-Term Repo Operations in India," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 0(-), pages 1-5.
    24. Martin, Feldkircher & Thomas, Gruber & Florian, Huber, 2019. "International effects of a compression of euro area yield curves," Working Papers in Economics 2019-1, University of Salzburg.
    25. Bhattarai, Saroj & Chatterjee, Arpita & Park, Woong Yong, 2021. "Effects of US quantitative easing on emerging market economies," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
    26. Maximilian Bock & Martin Feldkircher & Pierre L. Siklos, 2020. "International effects of euro area forward guidance," CAMA Working Papers 2020-54, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    27. Nagao, Ryoya & Kondo, Yoshihiro & Nakazono, Yoshiyuki, 2021. "The macroeconomic effects of monetary policy: Evidence from Japan," Journal of the Japanese and International Economies, Elsevier, vol. 61(C).
    28. Dr. Enzo Rossi & Vincent Wolff, 2020. "Spillovers to exchange rates from monetary and macroeconomic communications events," Working Papers 2020-18, Swiss National Bank.
    29. Kim, Kyoung-Gon, 2022. "Financial Crisis and the Global Transmission of U.S. Monetary Policy Surprises," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 63(2), pages 104-125, December.
    30. Daniel J. Lewis, 2019. "Announcement-Specific Decompositions of Unconventional Monetary Policy Shocks and Their Macroeconomic Effects," Staff Reports 891, Federal Reserve Bank of New York.
    31. B De Rezende, Rafael & Ristiniemi, Annukka, 2020. "A shadow rate without a lower bound constraint," Bank of England working papers 864, Bank of England.
    32. Philipp Hartman & Frank Smets, 2018. "The European Central Bank’s Monetary Policy during Its First 20 Years," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 49(2 (Fall)), pages 1-146.
    33. Rasmus Fatum & Naoko Hara & Yohei Yamamoto, 2019. "Negative Interest Rate Policy and the Influence of Macroeconomic News on Yields," IMES Discussion Paper Series 19-E-02, Institute for Monetary and Economic Studies, Bank of Japan.
    34. Kerstin Bernoth & Helmut Herwartz & Lasse Trienens, 2023. "The Impacts of Global Risk and US Monetary Policy on US Dollar Exchange Rates and Excess Currency Returns," Discussion Papers of DIW Berlin 2037, DIW Berlin, German Institute for Economic Research.
    35. Pinchetti, Marco & Szczepaniak, Andrzej, 2021. "Global spillovers of the Fed information effect," Bank of England working papers 952, Bank of England.
    36. Teti̇k, Metin, 2020. "Testing of leader-follower interaction between fed and emerging countries’ central banks," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    37. Gábor Dávid Kiss & Mercédesz Mészáros, 2020. "Gravity Among Central Bank Balance Sheets: Monetary Policy Spill-Over on FX Volatility," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 5(1), pages 33-57, June.
    38. Kortela, Tomi & Nelimarkka, Jaakko, 2020. "The effects of conventional and unconventional monetary policy: Identification through the yield curve," Bank of Finland Research Discussion Papers 3/2020, Bank of Finland.
    39. 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).
    40. Schmitt-Grohé, Stephanie & Uribe, Martín, 2022. "The effects of permanent monetary shocks on exchange rates and uncovered interest rate differentials," Journal of International Economics, Elsevier, vol. 135(C).
    41. Shixuan Wang & Rangan Gupta & Matteo Bonato & Oguzhan Cepni, 2022. "The Effects of Conventional and Unconventional Monetary Policy Shocks on US REITs Moments: Evidence from VARs with Functional Shocks," Working Papers 202219, University of Pretoria, Department of Economics.
    42. Behera, Harendra & Gunadi, Iman & Rath, Badri Narayan, 2023. "COVID-19 uncertainty, financial markets and monetary policy effects in case of two emerging Asian countries," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 173-189.
    43. Thi Bich Ngoc Tran & Hoang Cam Huong Pham, 2020. "The Spillover Effects of the US Unconventional Monetary Policy: New Evidence from Asian Developing Countries," JRFM, MDPI, vol. 13(8), pages 1-26, July.
    44. Zoe Venter, 2019. "The Interaction Between ConventionalMonetary Policy and Financial Stability: Chile, Colombia, Japan, Portugal and the UK," Working Papers REM 2019/96, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    45. Eva Ortega & Chiara Osbat, 2020. "Exchange rate pass-through in the euro area and EU countries," Occasional Papers 2016, Banco de España.
    46. Wei, Xiaoyun & Han, Liyan, 2021. "The impact of COVID-19 pandemic on transmission of monetary policy to financial markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    47. Benjamin K. Johannsen & Elmar Mertens, 2016. "A Time Series Model of Interest Rates With the Effective Lower Bound," Finance and Economics Discussion Series 2016-033, Board of Governors of the Federal Reserve System (U.S.).
    48. Gan‐Ochir Doojav & Davaasukh Damdinjav, 2023. "The macroeconomic effects of unconventional monetary policies in a commodity‐exporting economy: Evidence from Mongolia," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4627-4654, October.
    49. David KRIZEK & Josef BRCAK, 2021. "Support for export as a non-standard Central Bank policy: foreign exchange interventions in the case of the Czech Republic," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 12, pages 191-218, June.
    50. Luisa Corrado & Stefano Grassi & Enrico Minnella, 2021. "The Transmission Mechanism of Quantitative Easing: A Markov-Switching FAVAR Approach," CEIS Research Paper 520, Tor Vergata University, CEIS, revised 21 Oct 2021.
    51. Ryuzo Miyao & Tatsuyoshi Okimoto, 2020. "Regime shifts in the effects of Japan’s unconventional monetary policies," Manchester School, University of Manchester, vol. 88(6), pages 749-772, December.
    52. Stavrakeva, Vania & Tang, Jenny, 2019. "The Dollar During the Great Recession: US Monetary Policy Signaling and The Flight To Safety," CEPR Discussion Papers 14034, C.E.P.R. Discussion Papers.
    53. Lucélia Vaz & Rodrigo Raad, 2021. "Functional data analysis for brazilian term structure of interest rate," Textos para Discussão Cedeplar-UFMG 638, Cedeplar, Universidade Federal de Minas Gerais.
    54. Chaturvedi, Priya & Kumar, Kuldeep, 2022. "Econometric modelling of exchange rate volatility using mixed-frequency data," MPRA Paper 115222, University Library of Munich, Germany.
    55. Meng, Xiangcai & Huang, Chia-Hsing, 2021. "The time-frequency analysis of conventional and unconventional monetary policy: Evidence from Japan," Japan and the World Economy, Elsevier, vol. 59(C).
    56. Ur Rehman, Mobeen & Al Rababa'a, Abdel Razzaq & El-Nader, Ghaith & Alkhataybeh, Ahmad & Vo, Xuan Vinh, 2022. "Modelling the quantile cross-coherence between exchange rates: Does the COVID-19 pandemic change the interlinkage structure?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).

  7. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence intervals for bias and size distortion in IV and local projections — IV models," Working Papers 1841, Banco de España.

    Cited by:

    1. Germano Ruisi, 2019. "Time-Varying Local Projections," Working Papers 891, Queen Mary University of London, School of Economics and Finance.
    2. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence Intervals for Bias and Size Distortion in IV and Local Projections–IV Models," Working Papers 1077, Barcelona School of Economics.
    3. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    4. Daniel J. Lewis & Karel Mertens, 2022. "A Robust Test for Weak Instruments with Multiple Endogenous Regressors," Staff Reports 1020, Federal Reserve Bank of New York.
    5. Barbara Rossi, 2019. "Identifying and Estimating the Effects of Unconventional Monetary Policy in the Data: How to Do It and What Have We Learned?," Working Papers 1081, Barcelona School of Economics.
    6. Rossi, Barbara, 2019. "Identifying and Estimating the Effects of Unconventional Monetary Policy: How to Do It And What Have We Learned?," CEPR Discussion Papers 14064, C.E.P.R. Discussion Papers.
    7. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "Assessing the strength of many instruments with the first-stage F and Cragg-Donald statistics," Papers 2302.14423, arXiv.org.

  8. Pablo Guerron-Quintana & Atsushi Inoue & Lutz Kilian, 2016. "Impulse Response Matching Estimators for DSGE Models," CESifo Working Paper Series 5730, CESifo.

    Cited by:

    1. Minford, Patrick & Wickens, Michael R. & Xu, Yongdeng, 2017. "Comparing different data descriptors in Indirect Inference tests on DSGE models," CEPR Discussion Papers 11816, C.E.P.R. Discussion Papers.
    2. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2021. "Uncertainty and Monetary Policy during the Great Recession," Economics Working Papers 2021-05, Department of Economics and Business Economics, Aarhus University.
    3. Meenagh, David & Minford, Patrick & Xu, Yongdeng, 2022. "Targeting moments for calibration compared with indirect inference," Cardiff Economics Working Papers E2022/12, Cardiff University, Cardiff Business School, Economics Section.
    4. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2020. "Uncertainty and Monetary Policy during Extreme Events," Economics Working Papers 2020-11, Department of Economics and Business Economics, Aarhus University.
    5. Mario Martinoli & Alessio Moneta & Gianluca Pallante, 2022. "Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search," LEM Papers Series 2022/33, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    6. Lucy Minford & David Meenagh, 2020. "Supply-Side Policy and Economic Growth: A Case Study of the UK," Open Economies Review, Springer, vol. 31(1), pages 159-193, February.
    7. Giovanni Angelini & Marco M. Sorge, 2021. "Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks," Working Papers wp1160, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    9. Povilas Lastauskas & Julius Stakėnas, 2022. "Dancing Alone or Together: The Dynamic Effects of Independent and Common Monetary Policies," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 217-241, Emerald Group Publishing Limited.
    10. Efrem Castelnuovo & Giovanni Pellegrino, 2018. "Uncertainty-dependent Effects of Monetary Policy Shocks: A New Keynesian Interpretation," Melbourne Institute Working Paper Series wp2018n02, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    12. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "The small sample properties of Indirect Inference in testing and estimating DSGE models," Cardiff Economics Working Papers E2018/7, Cardiff University, Cardiff Business School, Economics Section.
    13. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    14. Meenagh, David & Minford, Patrick & Xu, Yongdeng, 2023. "Indirect Inference and Small Sample Bias - Some Recent Results," Cardiff Economics Working Papers E2023/15, Cardiff University, Cardiff Business School, Economics Section.
    15. Daniil Lomonosov, 2023. "Shocks of Business Activity and Specific Shocks to Oil Market in DSGE Model of Russian Economy and Their Influence Under Different Monetary Policy Regimes," Russian Journal of Money and Finance, Bank of Russia, vol. 82(4), pages 44-79, December.
    16. Inoue, Atsushi & Kilian, Lutz, 2016. "Joint confidence sets for structural impulse responses," Journal of Econometrics, Elsevier, vol. 192(2), pages 421-432.
    17. Ruge-Murcia, Francisco, 2020. "Estimating nonlinear dynamic equilibrium models by matching impulse responses," Economics Letters, Elsevier, vol. 197(C).
    18. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "Testing DSGE Models by indirect inference: a survey of recent findings," Cardiff Economics Working Papers E2018/14, Cardiff University, Cardiff Business School, Economics Section.
    19. Ryan Chahrour & Sanjay K. Chugh & Tristan Potter, 2023. "Anticipated productivity and the labor market," Quantitative Economics, Econometric Society, vol. 14(3), pages 897-934, July.
    20. Inês da Cunha Cabral & João Nicolau, 2022. "Inflation in the G7 and the expected time to reach the reference rate: A nonparametric approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1608-1620, April.
    21. Massimo Ferrari Minesso & Maria Sole Pagliari, 2022. "DSGE Nash: solving Nash Games in Macro Models With an application to optimal monetary policy under monopolistic commodity pricing," Working papers 884, Banque de France.
    22. Lin, Boqiang & Xu, Bin, 2018. "Growth of industrial CO2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis," Energy, Elsevier, vol. 151(C), pages 167-177.
    23. Giulia Piccillo & Poramapa Poonpakdee, 2023. "Ambiguous Business Cycles, Recessions and Uncertainty: A Quantitative Analysis," CESifo Working Paper Series 10646, CESifo.
    24. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    25. Minford, Patrick & Xu, Yongdeng, 2024. "Indirect Inference- a methodological essay on its role and applications," Cardiff Economics Working Papers E2024/1, Cardiff University, Cardiff Business School, Economics Section.
    26. Ferrari Minesso, Massimo & Pagliari, Maria Sole, 2022. "DSGE Nash: solving Nash games in macro models," Working Paper Series 2678, European Central Bank.
    27. Zviadadze, Irina, 2018. "Term Structure of Risk in Expected Returns," CEPR Discussion Papers 13414, C.E.P.R. Discussion Papers.

  9. Atsushi Inoue & Lutz Kilian, 2016. "Joint Confidence Sets for Structural Impulse Responses," CESifo Working Paper Series 5746, CESifo.

    Cited by:

    1. Nikola Kutin & Zakaria Moussa & Thomas Vallée, 2018. "Factors behind the Freight Rates in the Liner Shipping Industry," Working Papers halshs-01828633, HAL.
    2. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    3. Anna Staszewska-Bystrova & Peter Winker, 2014. "Measuring Forecast Uncertainty of Corporate Bond Spreads by Bonferroni-Type Prediction Bands," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(2), pages 89-104, June.
    4. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    5. Neil Kellard & Denise Osborn & Jerry Coakley & Simone D. Grose & Gael M. Martin & Donald S. Poskitt, 2015. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 721-740, September.
    6. Grabowski, Daniel & Staszewska-Bystrova, Anna, 2018. "Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181590, Verein für Socialpolitik / German Economic Association.
    7. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2019. "Co-integration and common trends analysis with score-driven models : an application to the federal funds effective rate and US inflation rate," UC3M Working papers. Economics 28451, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Atsushi Inoue & Lutz Kilian, 2020. "Joint Bayesian Inference about Impulse Responses in VAR Models," Working Papers 2022, Federal Reserve Bank of Dallas.
    9. Lütkepohl, Helmut & Staszewska-Bystrova, Anna & Winker, Peter, 2020. "Constructing joint confidence bands for impulse response functions of VAR models – A review," Econometrics and Statistics, Elsevier, vol. 13(C), pages 69-83.
    10. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    11. Fève, Patrick & Beaudry, Paul & Collard, Fabrice & Guay, Alain & Portier, Franck, 2022. "Dynamic Identification in VARs," TSE Working Papers 22-1384, Toulouse School of Economics (TSE).
    12. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    13. Carsten Trenkler & Enzo Weber, 2020. "Identifying shocks to business cycles with asynchronous propagation," Empirical Economics, Springer, vol. 58(4), pages 1815-1836, April.
    14. Atsushi Inoue & `Oscar Jord`a & Guido M. Kuersteiner, 2023. "Significance Bands for Local Projections," Papers 2306.03073, arXiv.org.
    15. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    16. Bojaj, Martin M. & Muhadinovic, Milica & Bracanovic, Andrej & Mihailovic, Andrej & Radulovic, Mladen & Jolicic, Ivan & Milosevic, Igor & Milacic, Veselin, 2022. "Forecasting macroeconomic effects of stablecoin adoption: A Bayesian approach," Economic Modelling, Elsevier, vol. 109(C).
    17. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    18. Kholodilin, Konstantin A. & Netsunajev, Aleksei, 2017. "Crimea and punishment: the impact of sanctions on Russian and European economies," Bank of Estonia Working Papers wp2017-5, Bank of Estonia, revised 11 Sep 2017.
    19. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2018. "Calculating joint confidence bands for impulse response functions using highest density regions," Empirical Economics, Springer, vol. 55(4), pages 1389-1411, December.
    20. 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.
    21. Hafner, Christian M. & Herwartz, Helmut & Wang, Shu, 2023. "Causal inference with (partially) independent shocks and structural signals on the global crude oil market," LIDAM Discussion Papers ISBA 2023004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    22. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    23. Lieb, Lenard & Smeekes, Stephan, 2017. "Inference for Impulse Responses under Model Uncertainty," Research Memorandum 022, Maastricht University, Graduate School of Business and Economics (GSBE).
    24. Bojaj, Martin M. & Djurovic, Gordana & Fabris, Nikola & Milovic, Nikola, 2023. "Top 1% and inequality connectedness in the EMU and WB," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 139-155.

  10. Atsushi Inoue & Chun-Huong Kuo & Barbara Rossi, 2015. "Identifying the Sources of Model Misspecification," Working Papers 821, Barcelona School of Economics.

    Cited by:

    1. F. Canova & F. Ferroni & C. Matthes, 2015. "Approximating time varying structural models with time invariant structures," Working papers 578, Banque de France.
    2. Francesca Monti, 2015. "Can a data-rich environment help identify the sources of model misspecification?," Discussion Papers 1505, Centre for Macroeconomics (CFM).
    3. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    4. Guido Ascari & Qazi Haque & Leandro M. Magnusson & Sophocles Mavroeidis, 2021. "Empirical evidence on the Euler equation for investment in the US," CAMA Working Papers 2021-65, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Fabio Canova & Christian Matthes, 2021. "Dealing with misspecification in structural macroeconometric models," Quantitative Economics, Econometric Society, vol. 12(2), pages 313-350, May.
    6. Loria, Francesca & Matthes, Christian & Wang, Mu-Chun, 2022. "Economic theories and macroeconomic reality," Journal of Monetary Economics, Elsevier, vol. 126(C), pages 105-117.
    7. Den Haan, Wouter & Drechsel, Thomas, 2018. "Agnostic Structural Disturbances (ASDs): Detecting and Reducing Misspecification in Empirical Macroeconomic Models," CEPR Discussion Papers 13145, C.E.P.R. Discussion Papers.
    8. Hatcher, Michael & Minford, Patrick, 2023. "Chameleon models in economics: A note," Cardiff Economics Working Papers E2023/10, Cardiff University, Cardiff Business School, Economics Section.
    9. Stefano Grassi & Miguel Leon-Ledesma & Filippo Ferroni, 2016. "Fundamental shock selection in DSGE models," 2016 Meeting Papers 47, Society for Economic Dynamics.
    10. Filippo Ferroni & Jonas D. M. Fisher & Leonardo Melosi, 2022. "Usual Shocks in our Usual Models," Working Paper Series WP 2022-39, Federal Reserve Bank of Chicago.
    11. Helena Marques & Gabriel Pino & J. D. Tena, 2018. "Voting with your feet: migration flows and happiness," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(2), pages 163-187, June.
    12. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    13. Filippo Ferroni & Stefano Grassi & Miguel A. León-Ledesma, 2017. "Selecting Primal Innovations in DSGE models," Working Paper Series WP-2017-20, Federal Reserve Bank of Chicago.

  11. Emily Anderson & Atsushi Inoue & Barbara Rossi, 2015. "Heterogeneous Consumers and Fiscal Policy Shocks," Working Papers 822, Barcelona School of Economics.

    Cited by:

    1. Bernd Hayo & Matthias Uhl, 2015. "Regional effects of federal tax shocks," Southern Economic Journal, John Wiley & Sons, vol. 82(2), pages 343-360, October.
    2. Pedro Brinca & Miguel H. Ferreira & Francesco Franco & Hans A. Holter & Laurence Malafry, 2017. "Fiscal Consolidation Programs and Income Inequality," CEF.UP Working Papers 1703, Universidade do Porto, Faculdade de Economia do Porto.
    3. Rossi, Barbara & Inoue, Atsushi & Anderson, Emily, 2013. "Heterogeneous Consumers and Fiscal Policy Shocks," CEPR Discussion Papers 9631, C.E.P.R. Discussion Papers.
    4. Mauro Napoletano & Andrea Roventini & Jean-Luc Gaffard, 2015. "Time-Varying Fiscal Multipliers in an Agent-Based Model with Credit Rationing," LEM Papers Series 2015/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Agovino, Massimiliano & Ferrara, Maria, 2015. "Disabilità e povertà: il ruolo delle pensioni di invalidità civile. Un'analisi DSGE per i dati italiani [Disability and poverty: the role of civilian disability pensions. A DSGE analysis for Italia," MPRA Paper 65616, University Library of Munich, Germany.
    6. Eunseong Ma, 2023. "Monetary Policy And Inequality: How Does One Affect The Other?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(2), pages 691-725, May.
    7. Puonti, Päivi, 2023. "Effective Fiscal Policy in an Aging Economy: Evidence from a BVAR Analysis," ETLA Working Papers 110, The Research Institute of the Finnish Economy.
    8. Giorgio Motta & Patrizio Tirelli, 2013. "Limited Asset Market Participation, Income Inequality and Macroeconomic Volatility," Working Papers 261, University of Milano-Bicocca, Department of Economics, revised Dec 2013.
    9. Alice Albonico & Alessia Paccagnini & Patrizio Tirelli, 2018. "Limited Asset Market Participation and the Euro Area Crisis. An Empirical DSGE Model," Working Papers 391, University of Milano-Bicocca, Department of Economics, revised Nov 2018.
    10. Christian Bredemeier & Falko Juessen & Roland Winkler, 2023. "Bringing Back the Jobs Lost to Covid‐19: The Role of Fiscal Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(7), pages 1703-1747, October.
    11. Bessho, Shun-ichiro, 2021. "Local fiscal multipliers and population aging in Japan," Japan and the World Economy, Elsevier, vol. 60(C).
    12. Fonseca, Miguel, 2020. "Fiscal Consolidations: Welfare Effects of the Adjustment Speed," MPRA Paper 98902, University Library of Munich, Germany, revised 02 Mar 2020.
    13. Givens, Gregory, 2019. "Unemployment, Partial Insurance, and the Multiplier Effects of Government Spending," MPRA Paper 96811, University Library of Munich, Germany.
    14. Masud Alam, 2021. "Output, Employment, and Price Effects of U.S. Narrative Tax Changes: A Factor-Augmented Vector Autoregression Approach," Papers 2106.10844, arXiv.org.
    15. Henrique S. Basso & Omar Rachedi, 2021. "The Young, the Old, and the Government: Demographics and Fiscal Multipliers," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(4), pages 110-141, October.
    16. Luisa Corrado & Edgar Silgado-Gómez, 2018. "Assessing the Effects of Fiscal Policy News under Imperfect Information: Evidence from Aggregate and Individual Data," CEIS Research Paper 447, Tor Vergata University, CEIS, revised 06 Nov 2018.
    17. Kerim Peren Arin & Juan A. Lacomba & Francisco Lagos & Ana I. Moro-Egido & Marcel Thum, 2021. "Socio-Economic Attitudes in the Era of Social Distancing and Lockdowns," CESifo Working Paper Series 8845, CESifo.
    18. Klein, Mathias & Winkler, Roland, 2017. "Austerity, Inequality, and Private Debt Overhang," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168076, Verein für Socialpolitik / German Economic Association.
    19. Surico, Paolo & Cloyne, James, 2013. "Household Debt and the Dynamic Effects of Income Tax Changes," CEPR Discussion Papers 9649, C.E.P.R. Discussion Papers.
    20. Ferrara, Maria & Tirelli, Patrizio, 2017. "Equitable fiscal consolidations," Economic Modelling, Elsevier, vol. 61(C), pages 207-223.
    21. Alica Ida Bonk & Laure Simon, 2022. "From He-Cession to She-Stimulus? The labor market impact of fiscal policy across gender," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 309-334, May.
    22. Pooyan Amir-Ahmadi & Thorsten Drautzburg, 2017. "Identification through Heterogeneity," CESifo Working Paper Series 6359, CESifo.
    23. Wifag Adnan & Kerim Peren Arin & Aysegul Corakci & Nicola Spagnolo, 2022. "On the heterogeneous effects of tax policy on labor market outcomes," Southern Economic Journal, John Wiley & Sons, vol. 88(3), pages 991-1036, January.
    24. Javier Andrés & José E. Boscá & Javier Ferri & Cristina Fuentes-Albero, 2018. "Households' balance sheets and the effect of fiscal policy," Working Papers 1831, Banco de España.
    25. Grancini, Stefano, 2021. "Risk Aversion and Fiscal Consolidation Programs," MPRA Paper 105500, University Library of Munich, Germany.
    26. Brinca, Pedro & Holter, Hans A. & Krusell, Per & Malafry, Laurence, 2016. "Fiscal multipliers in the 21st century," Journal of Monetary Economics, Elsevier, vol. 77(C), pages 53-69.
    27. Alice Albonico & Alessia Paccagnini & Patrizio Tirelli, 2014. "Estimating a DSGE model with Limited Asset Market Participation for the Euro Area," Working Papers 286, University of Milano-Bicocca, Department of Economics, revised Nov 2014.
    28. Laure Simon, 2023. "Fiscal Stimulus and Skill Accumulation over the Life Cycle," Staff Working Papers 23-9, Bank of Canada.
    29. Jüßen, Falko & Bredemeier, Christian & Winkler, Roland, 2017. "Fiscal Policy and Occupational Employment Dynamics," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168193, Verein für Socialpolitik / German Economic Association.
    30. Samuel Federico Kaplan & Arin Kerim Peren & Polyzos Efstathios & Spagnolo Nicola, 2022. "Stock Market Responses to Monetary Policy Shocks: Universal Firm-Level Evidence," Asociación Argentina de Economía Política: Working Papers 4571, Asociación Argentina de Economía Política.
    31. Freitas, Bruno, 2020. "Labour Share Heterogeneity and Fiscal Consolidation Programs," MPRA Paper 98973, University Library of Munich, Germany.
    32. Massimiliano Agovino & Maria Ferrara, 2022. "Disabilit?: diseguaglianza sociale ed economica. Un?analisi empirica e teorica," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2022(1), pages 11-42.
    33. Polyzos, Efstathios, 2022. "Examining the asymmetric impact of macroeconomic policy in the UAE: Evidence from quartile impulse responses and machine learning," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    34. Piotr Krajewski & Agata Szymanska, 2019. "The effectiveness of fiscal policy within business cycle-Ricardians vs. non-Ricardians approach," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 19(2), pages 195-215.
    35. Minsu Chang & Frank Schorfheide, 2024. "On the Effects of Monetary Policy Shocks on Income and Consumption Heterogeneity," PIER Working Paper Archive 24-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    36. Heer, Burkhard & Scharrer, Christian, 2018. "The age-specific burdens of short-run fluctuations in government spending," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 45-75.
    37. Steven Fazzari & James Morley & Irina Panovska, 2014. "State-Dependent Effects of Fiscal Policy," Discussion Papers 2012-27C, School of Economics, The University of New South Wales.
    38. Maria Ferrara & Patrizio Tirelli, 2014. "Fiscal Consolidations: Can We Reap the Gain and Escape the Pain?," Working Papers 283, University of Milano-Bicocca, Department of Economics, revised Oct 2014.
    39. Eunseong Ma, 2019. "The Heterogeneous Responses of Consumption between Poor and Rich to Government Spending Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(7), pages 1999-2028, October.
    40. Morita, Hiroshi, 2020. "Fiscal multipliers in the most aged country: Empirical evidence and theoretical interpretation," Discussion paper series HIAS-E-100, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    41. Aursland, Thor Andreas & Frankovic, Ivan & Kanik, Birol & Saxegaard, Magnus, 2020. "State-dependent fiscal multipliers in NORA - A DSGE model for fiscal policy analysis in Norway," Economic Modelling, Elsevier, vol. 93(C), pages 321-353.
    42. Rüth, Sebastian K. & Simon, Camilla, 2022. "How do income and the debt position of households propagate fiscal stimulus into consumption?," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    43. Massimiliano Agovino & Maria Ferrara, 2017. "Can civilian disability pensions overcome the poverty issue? A DSGE analysis for Italian data," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(4), pages 1469-1491, July.
    44. Daniel R. Carroll, 2014. "Why Do Economists Still Disagree over Government Spending Multipliers?," Economic Commentary, Federal Reserve Bank of Cleveland, issue May.

  12. Atsushi Inoue & Mototsugu Shintania, 2014. "Quasi-Bayesian Model Selection," Departmental Working Papers 1402, Southern Methodist University, Department of Economics.

    Cited by:

    1. Akihisa Shibata & Mototsugu Shintani & Takayuki Tsuruga, 2018. "Current account dynamics under information rigidity and imperfect capital mobility," CAMA Working Papers 2018-56, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. 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.
    3. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    4. Iwasaki, Yuto & Muto, Ichiro & Shintani, Mototsugu, 2021. "Missing wage inflation? Estimating the natural rate of unemployment in a nonlinear DSGE model," European Economic Review, Elsevier, vol. 132(C).
    5. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    6. Prosper Dovonon & Firmin Doko Tchatoka & Michael Aguessy, 2019. "Relevant moment selection under mixed identification strength," School of Economics and Public Policy Working Papers 2019-04, University of Adelaide, School of Economics and Public Policy.
    7. KANO, Takashi, 2023. "Posterior Inferences on Incomplete Structural Models : The Minimal Econometric Interpretation," Discussion paper series HIAS-E-128, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    8. Hirano, Keisuke & Wright, Jonathan H., 2022. "Analyzing cross-validation for forecasting with structural instability," Journal of Econometrics, Elsevier, vol. 226(1), pages 139-154.

  13. Rossi, Barbara & Inoue, Atsushi & Jin, Lu, 2014. "Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," CEPR Discussion Papers 10168, C.E.P.R. Discussion Papers.

    Cited by:

    1. Xiu Xu & Andrija Mihoci & Wolfgang Karl Hardle, 2020. "lCARE -- localizing Conditional AutoRegressive Expectiles," Papers 2009.13215, arXiv.org.
    2. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "Financial stress transmission in EMU sovereign bond market volatility: A connectedness analysis," Working Papers del Instituto Complutense de Estudios Internacionales 1501, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
    3. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "Volatility spillovers in EMU sovereign bond markets," Working Papers del Instituto Complutense de Estudios Internacionales 1504, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
    4. Xu, Xiu & Mihoci, Andrija & Härdle, Wolfgang Karl, 2018. "lCARE - localizing conditional autoregressive expectiles," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 198-220.
    5. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    6. 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.
    7. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2020. "How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics," Energy Economics, Elsevier, vol. 90(C).

  14. Atsushi Inoue & Lu Jin & Barbara Rossi, 2014. "Rolling Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," Working Papers 768, Barcelona School of Economics.

    Cited by:

    1. 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).
    2. Cai, Yuxin & Lu, Xinsheng & Ren, Yongping & Qu, Ling, 2019. "Exploring the dynamic relationship between crude oil price and implied volatility indices: A MF-DCCA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    3. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    4. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2020. "Can systemic risk measures predict economic shocks? Evidence from China," China Economic Review, Elsevier, vol. 64(C).
    5. Xiu Xu & Andrija Mihoci & Wolfgang Karl Hardle, 2020. "lCARE -- localizing Conditional AutoRegressive Expectiles," Papers 2009.13215, arXiv.org.
    6. 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.
    7. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "Financial stress transmission in EMU sovereign bond market volatility: A connectedness analysis," Working Papers del Instituto Complutense de Estudios Internacionales 1501, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
    8. Luca Nocciola, "undated". "Finite sample forecast properties and window length under breaks in cointegrated systems," Discussion Papers 19/07, University of Nottingham, Granger Centre for Time Series Econometrics.
    9. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    10. Philip Hans Franses & Eva Janssens, 2018. "This Time It Is Different! Or Not? Discounting Past Data When Predicting The Future," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-34, June.
    11. Xu Xiaojie, 2018. "Using Local Information to Improve Short-Run Corn Price Forecasts," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 16(1), pages 1-15, January.
    12. 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.
    13. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    14. Zhang, Yaojie & Wang, Yudong, 2023. "Forecasting crude oil futures market returns: A principal component analysis combination approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 659-673.
    15. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    16. Khowaja, Kainat & Saef, Danial & Sizov, Sergej & Härdle, Wolfgang Karl, 2020. "Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition," IRTG 1792 Discussion Papers 2020-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    18. Sun, Yuying & Wang, Shouyang & Zhang, Xun, 2018. "How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach," Economic Modelling, Elsevier, vol. 68(C), pages 506-513.
    19. Chang, Chih-Hao & Chen, Zih-Bing & Huang, Shih-Feng, 2022. "Forecasting of high-resolution electricity consumption with stochastic climatic covariates via a functional time series approach," Applied Energy, Elsevier, vol. 309(C).
    20. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    21. Davide De Gaetano, 2018. "Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets," Mathematics, MDPI, vol. 6(3), pages 1-19, March.
    22. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.
    23. Liu, Jing & Ma, Feng & Yang, Ke & Zhang, Yaojie, 2018. "Forecasting the oil futures price volatility: Large jumps and small jumps," Energy Economics, Elsevier, vol. 72(C), pages 321-330.
    24. Prakash, Navendu & Srivastava, Bhavya & Singh, Shveta & Sharma, Seema & Jain, Sonali, 2022. "Effectiveness of social distancing interventions in containing COVID-19 incidence: International evidence using Kalman filter," Economics & Human Biology, Elsevier, vol. 44(C).
    25. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    26. Fernando Fernández-Rodríguez & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2015. "Volatility spillovers in EMU sovereign bond markets," Working Papers del Instituto Complutense de Estudios Internacionales 1504, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
    27. 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.
    28. Hany Guirguis & Vaneesha Boney Dutra & Zoe McGreevy, 2022. "The impact of global economies on US inflation: A test of the Phillips curve," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(3), pages 575-592, July.
    29. Mengxi He & Yudong Wang & Yaojie Zhang, 2023. "The predictability of iron ore futures prices: A product‐material lead–lag effect," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1289-1304, September.
    30. 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.
    31. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
    32. 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.
    33. Zhang, Yue-Jun & Li, Zhao-Chen, 2021. "Forecasting the stock returns of Chinese oil companies: Can investor attention help?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 531-555.
    34. Shahriyar Aliev & Evžen Kočenda, 2022. "ECB monetary policy and commodity prices," FFA Working Papers 4.008, Prague University of Economics and Business, revised 21 Jun 2022.
    35. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
    36. Liang, Chao & Ma, Feng & Li, Ziyang & Li, Yan, 2020. "Which types of commodity price information are more useful for predicting US stock market volatility?," Economic Modelling, Elsevier, vol. 93(C), pages 642-650.
    37. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Optimal Forecast under Structural Breaks," Working Papers 202208, University of California at Riverside, Department of Economics.
    38. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    39. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
    40. He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023. "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, vol. 65(C).
    41. Xu, Xiu & Mihoci, Andrija & Härdle, Wolfgang Karl, 2018. "lCARE - localizing conditional autoregressive expectiles," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 198-220.
    42. Bhattacharya, Rudrani & Chakravarti, Parma & Mundle, Sudipto, 2018. "Forecasting India's Economic Growth: A Time-Varying Parameter Regression Approach," Working Papers 18/238, National Institute of Public Finance and Policy.
    43. Stephen G. Hall & George S. Tavlas & Yongli Wang, 2023. "Forecasting inflation: The use of dynamic factor analysis and nonlinear combinations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 514-529, April.
    44. Rachid Guennouni Hassani & Alexis Gilles & Emmanuel Lassalle & Arthur Dénouveaux, 2020. "Predicting Stock Returns with Batched AROW," Working Papers hal-02496048, HAL.
    45. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    46. Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022. "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, vol. 83(C).
    47. Franses, Ph.H.B.F. & Janssens, E., 2017. "This time it is different! Or not?," Econometric Institute Research Papers EI2017-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    48. Arango-Castillo, Lenin & Orraca, María José & Molina, G. Stefano, 2023. "The global component of headline and core inflation in emerging market economies and its ability to improve forecasting performance," Economic Modelling, Elsevier, vol. 120(C).
    49. Gantungalag Altansukh & Denise R. Osborn, 2022. "Using structural break inference for forecasting time series," Empirical Economics, Springer, vol. 63(1), pages 1-41, July.
    50. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    51. Wen, Danyan & Wang, Yudong & Zhang, Yaojie, 2021. "Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism," Economic Modelling, Elsevier, vol. 96(C), pages 209-219.
    52. Zhang, Yaojie & Ma, Feng & Liao, Yin, 2020. "Forecasting global equity market volatilities," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1454-1475.
    53. Dent, Kieran & Hacıoğlu Hoke, Sinem & Panagiotopoulos, Apostolos, 2021. "Solvency and wholesale funding cost interactions at UK banks," Journal of Financial Stability, Elsevier, vol. 52(C).
    54. Zhang, Yaojie & Lei, Likun & Wei, Yu, 2020. "Forecasting the Chinese stock market volatility with international market volatilities: The role of regime switching," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    55. Zhang, Zhikai & He, Mengxi & Zhang, Yaojie & Wang, Yudong, 2021. "Realized skewness and the short-term predictability for aggregate stock market volatility," Economic Modelling, Elsevier, vol. 103(C).
    56. 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.
    57. 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.
    58. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    59. Subhamitra Patra & Gourishankar S. Hiremath, 2022. "An Entropy Approach to Measure the Dynamic Stock Market Efficiency," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(2), pages 337-377, June.
    60. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2022. "Smooth Robust Multi-Horizon Forecasts," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 143-165, Emerald Group Publishing Limited.
    61. Dong, Dayong & Yue, Sishi & Cao, Jiawei, 2020. "Site visit information content and return predictability: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    62. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
    63. Li, Xiafei & Guo, Qiang & Liang, Chao & Umar, Muhammad, 2023. "Forecasting gold volatility with geopolitical risk indices," Research in International Business and Finance, Elsevier, vol. 64(C).
    64. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.
    65. Tae-Hwy Lee & Ekaterina Seregina & Yaojue Xu, 2023. "Elicitability and Encompassing for Volatility Forecasts by Bregman Functions," Working Papers 202311, University of California at Riverside, Department of Economics.
    66. Yi, Yongsheng & He, Mengxi & Zhang, Yaojie, 2022. "Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    67. 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.
    68. Artur Tarassow, 2017. "Forecasting growth of U.S. aggregate and household-sector M2 after 2000 using economic uncertainty measures," Macroeconomics and Finance Series 201702, University of Hamburg, Department of Socioeconomics.
    69. Peng, Zhen & Dong, Chaohua, 2022. "Augmented cointegrating linear models with possibly strongly correlated stationary and nonstationary regressors," Finance Research Letters, Elsevier, vol. 47(PB).
    70. Deryugina, Elena & Ponomarenko, Alexey & Rozhkova, Anna, 2020. "When are credit gap estimates reliable?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 221-238.
    71. Sixian Tang & Jackie Li & Leonie Tickle, 2022. "A New Fourier Approach under the Lee-Carter Model for Incorporating Time-Varying Age Patterns of Structural Changes," Risks, MDPI, vol. 10(8), pages 1-24, July.
    72. Algieri, Bernardina & Leccadito, Arturo, 2019. "Ask CARL: Forecasting tail probabilities for energy commodities," Energy Economics, Elsevier, vol. 84(C).
    73. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2020. "How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics," Energy Economics, Elsevier, vol. 90(C).
    74. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    75. Ghani, Usman & Zhu, Bo & Ghani, Maria & Khan, Nasir & khan, Raja Danish Akbar, 2023. "Role of oil shocks in US stock market volatility: A new insight from GARCH-MIDAS perspective," Resources Policy, Elsevier, vol. 85(PB).
    76. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    77. Shikha Gupta & Nand Kumar, 2022. "Globalization Versus Slowbalization: A Perspective on the Indian Economy," Journal of South Asian Development, , vol. 17(1), pages 84-107, April.
    78. Damiano B. Silipo & Giovanni Verga & Sviatlana Hlebik, 2023. "Managerial Beliefs and Banking Behavior," Journal of Financial Services Research, Springer;Western Finance Association, vol. 64(3), pages 401-431, December.
    79. Li, Xishu & Zuidwijk, Rob & de Koster, M.B.M, 2023. "Optimal competitive capacity strategies: Evidence from the container shipping market," Omega, Elsevier, vol. 115(C).
    80. Liu, Guangqiang & Guo, Xiaozhu, 2022. "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, vol. 75(C).
    81. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).
    82. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    83. Xiafei Li & Yu Wei & Xiaodan Chen & Feng Ma & Chao Liang & Wang Chen, 2022. "Which uncertainty is powerful to forecast crude oil market volatility? New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4279-4297, October.
    84. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    85. Hirano, Keisuke & Wright, Jonathan H., 2022. "Analyzing cross-validation for forecasting with structural instability," Journal of Econometrics, Elsevier, vol. 226(1), pages 139-154.
    86. 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).
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    88. Peng, Huan & Chen, Ruoxun & Mei, Dexiang & Diao, Xiaohua, 2018. "Forecasting the realized volatility of the Chinese stock market: Do the G7 stock markets help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 78-85.
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  15. Yasuo Hirose & Atsushi Inoue, 2013. "Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," CAMA Working Papers 2013-60, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Charles Ka Yui Leung & Joe Cho Yiu Ng, 2018. "Macro Aspects of Housing," Globalization Institute Working Papers 340, Federal Reserve Bank of Dallas.
    2. Yasuo Hirose & Takeki Sunakawa, 2016. "Parameter Bias in an Estimated DSGE Model," Working Papers halshs-01661908, HAL.
    3. Yasuo Hirose & Atsushi Inoue, 2013. "Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," TERG Discussion Papers 308, Graduate School of Economics and Management, Tohoku University.
    4. Best Gabriela & Kapinos Pavel, 2016. "Monetary policy and news shocks: are Taylor rules forward-looking?," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(2), pages 335-360, June.
    5. Julien Albertini & Hong Lan, 2016. "The importance of time-varying parameters in new Keynesian models with zero lower bound," SFB 649 Discussion Papers SFB649DP2016-013, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Alfred Duncan & Charles Nola, 2017. "Disputes , Debt And Equity," Working Papers 2017_08, Business School - Economics, University of Glasgow.
    7. Chen, Xiaoshan & Kirsanova, Tatiana & Leith, Campbell, 2014. "An Empirical Assessment of Optimal Monetary Policy Delegation in the Euro Area," Stirling Economics Discussion Papers 2014-11, University of Stirling, Division of Economics.
    8. Alice Albonico & Alessia Paccagnini & Patrizio Tirelli, 2018. "Limited Asset Market Participation and the Euro Area Crisis. An Empirical DSGE Model," Working Papers 391, University of Milano-Bicocca, Department of Economics, revised Nov 2018.
    9. Jeff Fuhrer, 2017. "Japanese and U.S. Inflation Dynamics in the 21st Century," IMES Discussion Paper Series 17-E-05, Institute for Monetary and Economic Studies, Bank of Japan.
    10. Hills, Timothy S. & Nakata, Taisuke & Schmidt, Sebastian, 2019. "Effective lower bound risk," European Economic Review, Elsevier, vol. 120(C).
    11. MATSUMAE Tatsuyoshi & HASUMI Ryo, 2016. "Impacts of Government Spending on Unemployment: Evidence from a Medium-scale DSGE Model(in Japanese)," ESRI Discussion paper series 329, Economic and Social Research Institute (ESRI).
    12. Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2018. "Estimating a Nonlinear New Keynesian Model with a Zero Lower Bound for Japan," Working Papers e120, Tokyo Center for Economic Research.
    13. Yasuo Hirose & Takeki Sunakawa, 2015. "Parameter bias in an estimated DSGE model: does nonlinearity matter?," CAMA Working Papers 2015-46, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Yasuo Hirose & Takeki Sunakawa, 2023. "The Natural Rate of Interest in a Non-linear DSGE Model," International Journal of Central Banking, International Journal of Central Banking, vol. 19(1), pages 301-340, March.
    15. Yasuo Hirose & Takeki Sunakawa, 2019. "Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound," The Japanese Economic Review, Japanese Economic Association, vol. 70(1), pages 51-104, March.
    16. Go Kotera & Saisuke Sakai, 2017. "Complementarity between Merit Goods and Private Consumption: Evidence from estimated DSGE model for Japan," KIER Working Papers 978, Kyoto University, Institute of Economic Research.
    17. Kang, Hyunju & Park, Jaevin & Suh, Hyunduk, 2020. "The rise of part-time employment in the great recession: Its causes and macroeconomic effects," Journal of Macroeconomics, Elsevier, vol. 66(C).
    18. Marcin Bielecki & Michał Brzoza-Brzezina & Marcin Kolasa & Krzysztof Makarski, 2017. "Could the boom-bust in the eurozone periphery have been prevented?," NBP Working Papers 263, Narodowy Bank Polski.
    19. Eijffinger, Sylvester & Uras, Burak & Grajales, Anderson, 2015. "Heterogeneity in Wage Setting Behavior in a New-Keynesian Model," CEPR Discussion Papers 10532, C.E.P.R. Discussion Papers.
    20. Chun-Hung Kuo & Hiroaki Miyamoto, 2016. "Unemployment and Wage Rigidity in Japan: A DSGE Model Perspective," Working Papers EMS_2016_06, Research Institute, International University of Japan.
    21. 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.
    22. Nicoletta Batini & Alessandro Cantelmo & Giovanni Melina & Stefania Villa, 2020. "How Loose, how tight? A measure of monetary and fiscal stance for the euro area," Temi di discussione (Economic working papers) 1295, Bank of Italy, Economic Research and International Relations Area.
    23. Iwasaki, Yuto & Muto, Ichiro & Shintani, Mototsugu, 2021. "Missing wage inflation? Estimating the natural rate of unemployment in a nonlinear DSGE model," European Economic Review, Elsevier, vol. 132(C).
    24. Neri, Stefano & Gerali, Andrea, 2019. "Natural rates across the Atlantic," Journal of Macroeconomics, Elsevier, vol. 62(C).
    25. Alexander W. Richter & Nathaniel A. Throckmorton, 2016. "Are nonlinear methods necessary at the zero lower bound?," Working Papers 1606, Federal Reserve Bank of Dallas.
    26. Hasumi, Ryo & Iiboshi, Hirokuni & Nakamura, Daisuke, 2017. "R&D Growth and Business Cycles Measured with an Endogenous Growth DSGE Model," MPRA Paper 85525, University Library of Munich, Germany.
    27. Hasumi, Ryo & Iibsoshi, Hirokuni & Nakamura, Daisuke, 2018. "Trends, Cycles and Lost Decades: Decomposition from a DSGE Model with Endogenous Growth," MPRA Paper 85521, University Library of Munich, Germany.
    28. Charles Ka Yui Leung & Edward Chi Ho Tang, 2023. "The dynamics of the house price‐to‐income ratio: Theory and evidence," Contemporary Economic Policy, Western Economic Association International, vol. 41(1), pages 61-78, January.
    29. Roberta Cardani & Alessia Paccagnini & Stelios D. Bekiros, 2017. "The Effectiveness of Forward Guidance in an Estimated DSGE Model for the Euro Area: the Role of Expectations," Working Papers 201701, School of Economics, University College Dublin.
    30. Tyler Atkinson & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "The Zero Lower Bound and Estimation Accuracy," Working Papers 1804, Federal Reserve Bank of Dallas.
    31. Muto, Ichiro & Sudo, Nao & Yoneyama, Shunichi, 2013. "Productivity Slowdown in Japan’s Lost Decades: How Much of It is Attributed to Financial Factors?," Dynare Working Papers 28, CEPREMAP.
    32. Damioli, Giacomo & Gregori, Wildmer Daniel, 2021. "Diplomatic relations and cross-border investments in the European Union," Working Papers 2021-02, Joint Research Centre, European Commission.
    33. Bianca Barbaro & Giorgio Massari & Patrizio Tirelli, 2022. "Who killed business dynamism in the U.S.?," Working Papers 494, University of Milano-Bicocca, Department of Economics, revised Aug 2022.
    34. Shirota, Toyoichiro, 2018. "What is the major source of business cycles: Spillovers from land prices, investment shocks, or anything else?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 138-149.
    35. Ichiro Muto & Nao Sudo & Shunichi Yoneyama, 2023. "Productivity Slowdown in Japan's Lost Decades: How Much of It Can Be Attributed to Damaged Balance Sheets?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(1), pages 159-207, February.
    36. Giovannini, Massimo & Pfeiffer, Philipp & Ratto, Marco, 2021. "Efficient and robust inference of models with occasionally binding constraints," Working Papers 2021-03, Joint Research Centre, European Commission.
    37. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2015. "Forecasting in a DSGE Model with Banking Intermediation: Evidence from the US," Working Papers 292, University of Milano-Bicocca, Department of Economics, revised Feb 2015.
    38. Calo, Silvia & Gregori, Wildmer Daniel & Petracco Giudici, Marco & Rancan, Michela, 2021. "Has the Comprehensive Assessment made the European financial system more resilient?," Working Papers 2021-08, Joint Research Centre, European Commission.

  16. Xu Han & Atsushi Inoue, 2013. "Tests for Parameter Instability in Dynamic Factor Models," DSSR Discussion Papers 10, Graduate School of Economics and Management, Tohoku University.

    Cited by:

    1. Duan, Jiangtao & Bai, Jushan & Han, Xu, 2023. "Quasi-maximum likelihood estimation of break point in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 233(1), pages 209-236.
    2. Su, Liangjun & Wang, Xia, 2017. "On time-varying factor models: Estimation and testing," Journal of Econometrics, Elsevier, vol. 198(1), pages 84-101.
    3. Chou, Ray Yeutien & Yen, Tso-Jung & Yen, Yu-Min, 2020. "Macroeconomic forecasting using approximate factor models with outliers," International Journal of Forecasting, Elsevier, vol. 36(2), pages 267-291.
    4. Badi H. Baltagi & Chihwa Kao & Fa Wang, 2016. "The Identification and Estimation of a Large Factor Model with Structural Instability," Center for Policy Research Working Papers 194, Center for Policy Research, Maxwell School, Syracuse University.
    5. Bonsoo Koo & Benjamin Wong & Ze-Yu Zhong, 2023. "Disentangling Structural Breaks in High Dimensional Factor Models," CAMA Working Papers 2023-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Hartigan, Luke & Morley, James, 2019. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," Working Papers 2019-10, University of Sydney, School of Economics, revised Nov 2019.
    7. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
    8. Matteo Barigozzi & Lorenzo Trapani, 2018. "Sequential testing for structural stability in approximate factor models," Discussion Papers 18/04, University of Nottingham, Granger Centre for Time Series Econometrics.
    9. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    10. Chen, Liang, 2012. "Identifying observed factors in approximate factor models: estimation and hypothesis testing," MPRA Paper 37514, University Library of Munich, Germany.
    11. Jaeheon Jung, 2019. "Estimating a Large Covariance Matrix in Time-varying Factor Models," Papers 1910.11965, arXiv.org.
    12. Horváth, Lajos & Rice, Gregory, 2019. "Asymptotics for empirical eigenvalue processes in high-dimensional linear factor models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 138-165.
    13. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    14. Han, Chirok & Kim, Dukpa, 2020. "Testing for the null of block zero restrictions in common factor models," Economics Letters, Elsevier, vol. 188(C).
    15. Bai, Jushan & Li, Kunpeng, 2012. "Maximum likelihood estimation and inference for approximate factor models of high dimension," MPRA Paper 42099, University Library of Munich, Germany, revised 19 Oct 2012.
    16. Chen, Liang & Dolado, Juan José & Gonzalo, Jesús, 2011. "Detecting big structural breaks in large factor models," UC3M Working papers. Economics we1141, Universidad Carlos III de Madrid. Departamento de Economía.
    17. Wang, Lu & Wu, Jianhong, 2022. "Estimation of high-dimensional factor models with multiple structural changes," Economic Modelling, Elsevier, vol. 108(C).
    18. Byungsoo Kim & Junmo Song & Changryong Baek, 2021. "Robust test for structural instability in dynamic factor models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 821-853, August.
    19. Aslanidis, Nektarios & Hartigan, Luke, 2021. "Is the assumption of constant factor loadings too strong in practice?," Economic Modelling, Elsevier, vol. 98(C), pages 100-108.
    20. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    21. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    22. Tatsushi Oka & Pierre Perron, 2018. "Testing for common breaks in a multiple equations system," Monash Econometrics and Business Statistics Working Papers 3/18, Monash University, Department of Econometrics and Business Statistics.
    23. YAMAMOTO, Yohei & 山本, 庸平 & TANAKA, Shinya & 田中, 晋也, 2013. "Testing for Factor Loading Structural Change under Common Breaks," Discussion Papers 2013-17, Graduate School of Economics, Hitotsubashi University.
    24. Changryong Baek & Benjamin Leinwand & Kristen A. Lindquist & Seok-Oh Jeong & Joseph Hopfinger & Katheleen M. Gates & Vladas Pipiras, 2023. "Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 636-655, June.
    25. Jushan Bai & Jiangtao Duan & Xu Han, 2022. "Likelihood ratio test for structural changes in factor models," Papers 2206.08052, arXiv.org, revised Dec 2023.
    26. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    27. Chen, Sanpan & Cui, Guowei & Zhang, Jianhua, 2017. "On testing for structural break of coefficients in factor-augmented regression models," Economics Letters, Elsevier, vol. 161(C), pages 141-145.
    28. Markus Pelger & Ruoxuan Xiong, 2022. "State-Varying Factor Models of Large Dimensions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1315-1333, June.
    29. Mehmet Balcilar & Riza Demirer & Festus V. Bekun, 2021. "Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    30. Ma, Shujie & Su, Liangjun, 2018. "Estimation of large dimensional factor models with an unknown number of breaks," Journal of Econometrics, Elsevier, vol. 207(1), pages 1-29.
    31. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    32. Wenting Liao & Jun Ma & Chengsi Zhang, 2023. "Identifying exchange rate effects and spillovers of US monetary policy shocks in the presence of time‐varying instrument relevance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 989-1006, November.
    33. 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.
    34. 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.
    35. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
    36. Fu, Zhonghao & Hong, Yongmiao & Wang, Xia, 2023. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," Journal of Econometrics, Elsevier, vol. 233(1), pages 302-331.
    37. Steland, Ansgar, 2020. "Testing and estimating change-points in the covariance matrix of a high-dimensional time series," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
    38. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    39. Luke Hartigan, 2015. "Changes in the Factor Structure of the U.S. Economy: Permanent Breaks or Business Cycle Regimes?," Discussion Papers 2015-17, School of Economics, The University of New South Wales.
    40. Chen, Liang, 2015. "Estimating the common break date in large factor models," Economics Letters, Elsevier, vol. 131(C), pages 70-74.
    41. Baltagi, Badi H. & Kao, Chihwa & Wang, Fa, 2016. "Estimating and testing high dimensional factor models with multiple structural changes," MPRA Paper 98489, University Library of Munich, Germany, revised 26 Jul 2019.
    42. Giovanni Caggiano & Efrem Castelnuovo, 2023. "Global financial uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 432-449, April.
    43. Monika Bours & Ansgar Steland, 2021. "Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 610-654, June.
    44. Ma, Chenchen & Tu, Yundong, 2023. "Shrinkage estimation of multiple threshold factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1876-1892.
    45. 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.
    46. Ma, Chenchen & Tu, Yundong, 2023. "Group fused Lasso for large factor models with multiple structural breaks," Journal of Econometrics, Elsevier, vol. 233(1), pages 132-154.
    47. Dante Amengual & Luca Repetto, 2014. "Testing a Large Number of Hypotheses in Approximate Factor Models," Working Papers wp2014_1410, CEMFI.
    48. Baek, Changryong & Gates, Katheleen M. & Leinwand, Benjamin & Pipiras, Vladas, 2021. "Two sample tests for high-dimensional autocovariances," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

  17. Kilian, Lutz & Inoue, Atsushi, 2011. "Inference on Impulse Response Functions in Structural VAR Models," CEPR Discussion Papers 8419, C.E.P.R. Discussion Papers.

    Cited by:

    1. 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.
    2. Morita, Hiroshi, 2014. "External shocks and Japanese business cycles: Evidence from a sign-restricted VAR model," Japan and the World Economy, Elsevier, vol. 30(C), pages 59-74.
    3. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    4. Stephan B. Bruns & Alessio Moneta & David I. Stern, 2019. "Estimating the Economy-Wide Rebound Effect Using Empirically Identified Structural Vector Autoregressions," LEM Papers Series 2019/27, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Thorsten Drautzburg, 2016. "A narrative approach to a fiscal DSGE model," Working Papers 16-11, Federal Reserve Bank of Philadelphia.
    6. Amir-Ahmadi, Pooyan & Matthes, Christian & Wang, Mu-Chun, 2017. "Measurement errors and monetary policy: Then and now," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 66-78.
    7. Sangyup Choi, 2021. "Bank Lending Standards, Loan Demand, and the Macroeconomy: Evidence from the Korean Bank Loan Officer Survey," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-45, December.
    8. Fengler, Matthias & Polivka, Jeannine, 2022. "Structural Volatility Impulse Response Analysis," Economics Working Paper Series 2211, University of St. Gallen, School of Economics and Political Science.
    9. Alfred Haug & Syed Basher & Perry Sadorsky, 2016. "The impact of oil price shocks on exchange rates: A non-linear smooth-transition approach," EcoMod2016 9226, EcoMod.
    10. Alexander Chudik & M. Hashem Pesaran, 2014. "Theory and practice of GVAR modeling," Globalization Institute Working Papers 180, Federal Reserve Bank of Dallas.
    11. Helmut Herwartz & Martin Plödt, 2016. "Simulation Evidence on Theory-based and Statistical Identification under Volatility Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 94-112, February.
    12. Herwartz, Helmut & Plödt, Martin, 2014. "Sign restrictions and statistical identification under volatility breaks -- Simulation based evidence and an empirical application to monetary policy analysis," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100326, Verein für Socialpolitik / German Economic Association.
    13. Uhrin, Gábor B. & Herwartz, Helmut, 2016. "Monetary policy shocks, set-identifying restrictions, and asset prices: A benchmarking approach for analyzing set-identified models," University of Göttingen Working Papers in Economics 295, University of Goettingen, Department of Economics.
    14. Hiroshi Morita, 2017. "Effects of Anticipated Fiscal Policy Shock on Macroeconomic Dynamics in Japan," The Japanese Economic Review, Springer, vol. 68(3), pages 364-393, September.
    15. Dirk-Jan van de Ven & Roger Fouquet, 2014. "Historical energy price shocks and their changing effects on the economy," GRI Working Papers 153, Grantham Research Institute on Climate Change and the Environment.
    16. Grabowski, Daniel & Staszewska-Bystrova, Anna, 2018. "Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181590, Verein für Socialpolitik / German Economic Association.
    17. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying Volatility, Financial Intermediation and Monetary Policy," IWH Discussion Papers 19/2016, Halle Institute for Economic Research (IWH).
    18. Jamie L. Cross & Bao H. Nguyen & Trung Duc Tran, 2021. "The Role of Precautionary and Speculative Demand in the Global Market for Crude Oil," Working Papers No 06/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    19. Marianna Riggi & Fabrizio Venditti, 2015. "The time varying effect of oil price shocks on euro-area exports," Temi di discussione (Economic working papers) 1035, Bank of Italy, Economic Research and International Relations Area.
    20. Kilian, Lutz, 2019. "Facts and Fiction in Oil Market Modeling," CEPR Discussion Papers 14047, C.E.P.R. Discussion Papers.
    21. Francesco Furlanetto & Francesco Ravazzolo & Samad Sarferaz, 2014. "Identification of financial factors in economic fluctuations," KOF Working papers 14-364, KOF Swiss Economic Institute, ETH Zurich.
    22. Nicolas Legrand, 2019. "The Empirical Merit Of Structural Explanations Of Commodity Price Volatility: Review And Perspectives," Journal of Economic Surveys, Wiley Blackwell, vol. 33(2), pages 639-664, April.
    23. Konstantinos Chisiridis & Kostas Mouratidis & Theodore Panagiotidis, 2018. "The North-South Divide, the Euro and the World," Working Papers 377, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
    24. Joscha Beckmann & Robert L. Czudaj, 2018. "Monetary Policy Shocks, Expectations, And Information Rigidities," Economic Inquiry, Western Economic Association International, vol. 56(4), pages 2158-2176, October.
    25. Christiane Baumeister & James D. Hamilton, 2018. "Inference in Structural Vector Autoregressions when the Identifying Assumptions are not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations," CESifo Working Paper Series 7048, CESifo.
    26. Atsushi Inoue & Lutz Kilian, 2020. "Joint Bayesian Inference about Impulse Responses in VAR Models," Working Papers 2022, Federal Reserve Bank of Dallas.
    27. Benjamin Wong, 2013. "Inflation Dynamics and The Role of Oil Shocks: How Different Were the 1970s?," CAMA Working Papers 2013-59, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    28. Lütkepohl, Helmut & Staszewska-Bystrova, Anna & Winker, Peter, 2020. "Constructing joint confidence bands for impulse response functions of VAR models – A review," Econometrics and Statistics, Elsevier, vol. 13(C), pages 69-83.
    29. Lutz Kilian & Xiaoqing Zhou, 2018. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks: Comment," CESifo Working Paper Series 7166, CESifo.
    30. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    31. Bassam Fattouh, Lutz Kilian, and Lavan Mahadeva, 2013. "The Role of Speculation in Oil Markets: What Have We Learned So Far?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    32. Leonardo N. Ferreira, 2020. "Forward Guidance Matters: Disentangling Monetary Policy Shocks," Working Papers 912, Queen Mary University of London, School of Economics and Finance.
    33. Mansur, Alfan, 2019. "Sharia Banking Dynamics and the Macroeconomic Responses: Evidence from Indonesia," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 53(2), pages 139-152.
    34. Etienne Vaccaro-Grange, 2019. "Quantitative Easing and the Term Premium as a Monetary Policy Instrument," Working Papers halshs-02359503, HAL.
    35. Kilian, Lutz & Zhou, Xiaoqing, 2020. "Does drawing down the U.S. strategic petroleum reserve help stabilize oil prices?," CFS Working Paper Series 647, Center for Financial Studies (CFS).
    36. Kilian, Lutz, 2020. "Understanding the estimation of oil demand and oil supply elasticities," CFS Working Paper Series 649, Center for Financial Studies (CFS).
    37. Oladunni, Sunday, 2019. "External Shocks and Business Cycle Fluctuations in Oil-exporting Small Open Economies: The Case of Nigeria," MPRA Paper 98639, University Library of Munich, Germany.
    38. Pascal Towbin & Mr. Sebastian Weber, 2015. "Price Expectations and the U.S. Housing Boom," IMF Working Papers 2015/182, International Monetary Fund.
    39. Daniil Lomonosov & Andrey Polbin & Nikita Fokin, 2021. "The Impact of Global Economic Activity, Oil Supply and Speculative Oil Shocks on the Russian Economy," HSE Economic Journal, National Research University Higher School of Economics, vol. 25(2), pages 227-262.
    40. Tachibana, Minoru, 2013. "How have inflation-targeting central banks responded to supply shocks?," Economics Letters, Elsevier, vol. 121(1), pages 1-3.
    41. Yayi Yan & Jiti Gao & Bin Peng, 2021. "On Time-Varying VAR Models: Estimation, Testing and Impulse Response Analysis," Papers 2111.00450, arXiv.org.
    42. Lutz Kilian & Xiaoqing Zhou, 2019. "Oil Prices, Exchange Rates and Interest Rates," Working Papers 1914, Federal Reserve Bank of Dallas.
    43. Fabrice Dabiré, 2022. "Forward guidance and the exchange rate: A theoretical sign restricted VAR analysis," Cahiers de recherche 22-03, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    44. Hairault, Jean-Olivier & Zhutova, Anastasia, 2018. "The cyclicality of labor-market flows: A multiple-shock approach," European Economic Review, Elsevier, vol. 103(C), pages 150-172.
    45. Güntner, Jochen & Öhlinger, Peter, 2022. "Oil price shocks and the hedging benefit of airline investments," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    46. David S. Jacks & Martin Stuermer, 2021. "Dry Bulk Shipping and the Evolution of Maritime Transport Costs, 1850-2020," Working Papers 2102, Federal Reserve Bank of Dallas.
    47. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    48. Zheng, Xinwei & Su, Dan, 2017. "Impacts of oil price shocks on Chinese stock market liquidity," International Review of Economics & Finance, Elsevier, vol. 50(C), pages 136-174.
    49. Victor Pontines, 2020. "The real effects of loan-to-value limits: Empirical evidence from Korea," CAMA Working Papers 2020-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    50. Atsushi Inoue & `Oscar Jord`a & Guido M. Kuersteiner, 2023. "Significance Bands for Local Projections," Papers 2306.03073, arXiv.org.
    51. Liu, Donghui & Meng, Lingjie & Wang, Yudong, 2020. "Oil price shocks and Chinese economy revisited: New evidence from SVAR model with sign restrictions," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 20-32.
    52. Marek A. Dąbrowski & Łukasz Kwiatkowski & Justyna Wróblewska, 2020. "Sources of Real Exchange Rate Variability in Central and Eastern European Countries: Evidence from Structural Bayesian MSH-VAR Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(4), pages 369-412, December.
    53. Pooyan Amir-Ahmadi & Thorsten Drautzburg, 2017. "Identification through Heterogeneity," CESifo Working Paper Series 6359, CESifo.
    54. Benjamin Poignard & Manabu Asai, 2021. "Estimation of High Dimensional Vector Autoregression via Sparse Precision Matrix," Discussion Papers in Economics and Business 21-03, Osaka University, Graduate School of Economics.
    55. Alfred A. Haug & Syed Abul Basher, 2019. "Exchange rates of oil exporting countries and global oil price shocks: a nonlinear smooth-transition approach," Applied Economics, Taylor & Francis Journals, vol. 51(48), pages 5282-5296, October.
    56. Fernando J. Pérez Forero & Marco Vega, 2016. "Asymmetric Exchange Rate Pass-through: Evidence from Nonlinear SVARs," Working Papers 63, Peruvian Economic Association.
    57. Lucas Hafemann & Paul Rudel & Joerg Schmidt, 2017. "Moving Closer or Drifting Apart: Distributional Effects of Monetary Policy," MAGKS Papers on Economics 201721, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    58. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2017. "The impact of oil-market shocks on stock returns in major oil-exporting countries: A Markov-switching approach," MPRA Paper 81638, University Library of Munich, Germany.
    59. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2014. "Confidence Bands for Impulse Responses: Bonferroni versus Wald," CESifo Working Paper Series 4634, CESifo.
    60. K. Istrefi & B. Vonnak, 2015. "Delayed Overshooting Puzzle in Structural Vector Autoregression Models," Working papers 576, Banque de France.
    61. Rubio-Ramírez, Juan Francisco & Antolin-Diaz, Juan, 2016. "Narrative Sign Restrictions for SVARs," CEPR Discussion Papers 11517, C.E.P.R. Discussion Papers.
    62. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2018. "Narrative Sign Restrictions for SVARs," American Economic Review, American Economic Association, vol. 108(10), pages 2802-2829, October.
    63. Jonas E. Arias & Dario Caldara & Juan F. Rubio-Ramirez, 2016. "The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure," FRB Atlanta Working Paper 2016-15, Federal Reserve Bank of Atlanta.
    64. Matthew Greenwood‐Nimmo & Viet Hoang Nguyen & Eliza Wu, 2021. "On the International Spillover Effects of Country‐Specific Financial Sector Bailouts and Sovereign Risk Shocks," The Economic Record, The Economic Society of Australia, vol. 97(317), pages 285-309, June.
    65. Lomonosov, Daniil, 2021. "Роль Коронавирусной Пандемии И Развала Сделки Опек+ В Динамике Цены На Нефть В 2020 Году [The role of the coronavirus pandemic and the collapse of the OPEC + deal in the dynamics of oil prices in 2," MPRA Paper 109319, University Library of Munich, Germany.
    66. Gong, Xu & Lin, Boqiang, 2018. "Time-varying effects of oil supply and demand shocks on China's macro-economy," Energy, Elsevier, vol. 149(C), pages 424-437.
    67. Maghyereh, Aktham & Abdoh, Hussein, 2021. "The effect of structural oil shocks on bank systemic risk in the GCC countries," Energy Economics, Elsevier, vol. 103(C).
    68. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
    69. Lutz Kilian & Xiaoqing Zhou, 2020. "The Econometrics of Oil Market VAR Models," CESifo Working Paper Series 8153, CESifo.
    70. Sangyup Choi, 2018. "Bank Lending Standards, Loan Demand, and the Macroeconomy: Evidence from the Emerging Market Bank Loan Officer Survey," Working papers 2018rwp-126, Yonsei University, Yonsei Economics Research Institute.
    71. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    72. 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.
    73. Alessio Volpicella, 2019. "SVARs Identification through Bounds on the Forecast Error Variance," Working Papers 890, Queen Mary University of London, School of Economics and Finance.
    74. Inoue, Atsushi & Kilian, Lutz, 2016. "Joint confidence sets for structural impulse responses," Journal of Econometrics, Elsevier, vol. 192(2), pages 421-432.
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    88. Morita, Hiroshi, 2015. "State-dependent effects of fiscal policy in Japan: Do rule-of-thumb households increase the effects of fiscal policy?," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 49-61.
    89. Steven M. Fazzari & James Morley & Irina B. Panovska, 2017. "When Do Discretionary Changes in Government Spending or Taxes Have Larger Effects?," Discussion Papers 2017-04, School of Economics, The University of New South Wales.
    90. Donayre, Luiggi & Panovska, Irina, 2016. "State-dependent exchange rate pass-through behavior," Journal of International Money and Finance, Elsevier, vol. 64(C), pages 170-195.
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    92. Keating, John W., 2013. "What do we learn from Blanchard and Quah decompositions of output if aggregate demand may not be long-run neutral?," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 203-217.
    93. Alsamara, Mouyad Kassm & Mrabet, Zouhair & Elafif, Mohamed & Gangopadhyay, Partha, 2017. "The asymmetric effects of oil price on economic growth in Turkey and Saudi Arabia: new evidence from nonlinear ARDL approach," International Journal of Development and Conflict, Gokhale Institute of Politics and Economics, vol. 7(2), pages 97-118.
    94. Rangan Gupta & Mampho P. Modise, 2013. "Does the Source of Oil Price Shocks Matter for South African Stock Returns? A Structural VAR Approach," Working Papers 201318, University of Pretoria, Department of Economics.
    95. Josué Diwambuena & Francesco Ravazzolo, 2022. "What are the drivers of Labor Productivity?," BEMPS - Bozen Economics & Management Paper Series BEMPS86, Faculty of Economics and Management at the Free University of Bozen.
    96. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2021. "The dynamic effects of international oil price shocks on economic fluctuation," Resources Policy, Elsevier, vol. 74(C).
    97. Yayi Yan & Jiti Gao & Bin Peng, 2021. "On Time-Varying VAR models: Estimation, Testing and Impulse Response Analysis," Monash Econometrics and Business Statistics Working Papers 17/21, Monash University, Department of Econometrics and Business Statistics.
    98. Gehrke, Britta & Yao, Fang, 2013. "Sources of Real Exchange Rate Fluctuations: The Role of Supply Shocks Revisited," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79821, Verein für Socialpolitik / German Economic Association.
    99. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Structural Interpretation of Vector Autoregressions with Incomplete Information: Revisiting the Role of Oil Supply and Demand S," CEPR Discussion Papers 13068, C.E.P.R. Discussion Papers.
    100. Raffaella Giacomini & Toru Kitagawa, 2014. "Inference about Non-Identified SVARs," CeMMAP working papers 45/14, Institute for Fiscal Studies.
    101. Rubio-Ramírez, Juan Francisco & Caldara, Dario & Arias, Jonas E., 2016. "The Systematic Component of Monetary Policy in SVARs: An Agnostic Identi," CEPR Discussion Papers 11674, C.E.P.R. Discussion Papers.
    102. Dany, Geraldine, 2016. "The credit channel during times of financial stress: A time varying VAR analysis," VfS Annual Conference 2016 (Augsburg): Demographic Change 145899, Verein für Socialpolitik / German Economic Association.
    103. Claudia Foroni & Livio Stracca, 2023. "The shale oil revolution and the global oil supply curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 370-387, April.
    104. Georgiadis, Georgios, 2015. "Examining asymmetries in the transmission of monetary policy in the euro area: Evidence from a mixed cross-section global VAR model," European Economic Review, Elsevier, vol. 75(C), pages 195-215.
    105. Markku Lanne & Jani Luoto, 2016. "Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression," CREATES Research Papers 2016-04, Department of Economics and Business Economics, Aarhus University.
    106. Ferhat Citak, 2018. "Exchange Rate and Turkish Tourism Trade," International Journal of Economics and Financial Issues, Econjournals, vol. 8(4), pages 206-213.
    107. Rausser, Gordon & Stuermer, Martin, 2020. "A Dynamic Analysis of Collusive Action: The Case of the World Copper Market, 1882-2016," MPRA Paper 104708, University Library of Munich, Germany.
    108. Richard T. Baillie & Kun Ho Kim, 2016. "Is Robust Inference with OLS Sensible in Time Series Regressions? Investigating Bias and MSE Trade-offs with Feasible GLS and VAR Approaches," Working Paper series 16-04, Rimini Centre for Economic Analysis.
    109. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    110. Pooyan Amir‐Ahmadi & Thorsten Drautzburg, 2021. "Identification and inference with ranking restrictions," Quantitative Economics, Econometric Society, vol. 12(1), pages 1-39, January.
    111. Cengiz Tunc & Denis Pelletier, 2013. "Endogenous Life-Cycle Housing Investment and Portfolio Allocation," Working Papers 1345, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
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    115. Lomonosov Daniil, 2021. "Роль Пандемии Коронавируса И Развала Сделки Опек+ В Динамике Цены На Нефть В 2020 Г," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 8, pages 23-28, August.
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  18. Barbara Rossi & Atsushi Inoue, 2011. "Out-of-Sample Forecast Tests Robust to Window Size Choice," Working Papers 11-04, Duke University, Department of Economics.

    Cited by:

    1. Tae-Hwy Lee & Weiping Yang, 2012. "Money–Income Granger-Causality in Quantiles," Advances in Econometrics, in: 30th Anniversary Edition, pages 385-409, Emerald Group Publishing Limited.
    2. Dai, Zhifeng & Zhang, Xiaotong & Li, Tingyu, 2023. "Forecasting stock return volatility in data-rich environment: A new powerful predictor," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    3. 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.
    4. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers (Old Series) 1120, Federal Reserve Bank of Cleveland.
    5. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    6. 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.
    7. 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.
    8. Barbara Rossi, 2012. "Comment on "Taylor Rule Exchange Rate Forecasting during the Financial Crisis"," NBER Chapters, in: NBER International Seminar on Macroeconomics 2012, pages 106-116, National Bureau of Economic Research, Inc.
    9. Yuntong Liu & Yu Wei & Yi Liu & Wenjuan Li, 2020. "Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-12, December.

  19. Rossi, Barbara & Inoue, Atsushi, 2011. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," CEPR Discussion Papers 8542, C.E.P.R. Discussion Papers.

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    1. Rossi, José Luiz Júnior, 2013. "Liquidity and Exchange Rates," Insper Working Papers wpe_325, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    2. 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).
    3. Tae-Hwy Lee & Weiping Yang, 2012. "Money–Income Granger-Causality in Quantiles," Advances in Econometrics, in: 30th Anniversary Edition, pages 385-409, Emerald Group Publishing Limited.
    4. Dai, Zhifeng & Zhang, Xiaotong & Li, Tingyu, 2023. "Forecasting stock return volatility in data-rich environment: A new powerful predictor," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    5. Chen, Shiu-Sheng & Chou, Yu-Hsi, 2023. "Liquidity yield and exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 137(C).
    6. 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).
    7. Theologos Dergiades & Panos K. Pouliasis, 2021. "Should Stock Returns Predictability be hooked on Long Horizon Regressions?," Discussion Paper Series 2021_03, Department of Economics, University of Macedonia, revised Feb 2021.
    8. Nicolas S. Magner & Nicolás Hardy & Tiago Ferreira & Jaime F. Lavin, 2023. "“Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis," Mathematics, MDPI, vol. 11(7), pages 1-16, March.
    9. Li, Dongxin & Zhang, Li & Li, Lihong, 2023. "Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model," International Review of Financial Analysis, Elsevier, vol. 88(C).
    10. Martin Enilov & Yuan Wang, 2022. "Tourism and economic growth: Multi-country evidence from mixed-frequency Granger causality tests," Tourism Economics, , vol. 28(5), pages 1216-1239, August.
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    12. Luis F. Melo Velandia & Rubén A. Loaiza Maya & Mauricio Villamizar-Villegas, 2014. "Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates," Borradores de Economia 853, Banco de la Republica de Colombia.
    13. Chen, Jian & Jiang, Fuwei & Liu, Yangshu & Tu, Jun, 2017. "International volatility risk and Chinese stock return predictability," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 183-203.
    14. Barbara Rossi, 2013. "Exchange rate predictability," Economics Working Papers 1369, Department of Economics and Business, Universitat Pompeu Fabra.
    15. Firmin Doko Tchatoka & Qazi Haque, 2020. "On bootstrapping tests of equal forecast accuracy for nested models," Economics Discussion / Working Papers 20-06, The University of Western Australia, Department of Economics.
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    41. Papahristodoulou, Christos, 2019. "Is there any theory that explains the SEK?," MPRA Paper 95072, University Library of Munich, Germany, revised 08 Jul 2019.
    42. 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.
    43. Guo, Xiaozhu & Huang, Dengshi & Li, Xiafei & Liang, Chao, 2023. "Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 672-693.
    44. 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.
    45. Tian, Guangning & Peng, Yuchao & Meng, Yuhao, 2023. "Forecasting crude oil prices in the COVID-19 era: Can machine learn better?," Energy Economics, Elsevier, vol. 125(C).
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    47. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
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    49. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
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    4. Zhongjun Qu, 2011. "Inference and Speci?cation Testing in DSGE Models with Possible Weak Identification," Boston University - Department of Economics - Working Papers Series WP2011-058, Boston University - Department of Economics.
    5. Andrle, Michal, 2010. "A note on identification patterns in DSGE models," Working Paper Series 1235, European Central Bank.
    6. Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," Working Papers w201032, Banco de Portugal, Economics and Research Department.

  22. Inoue, Atsushi & Rossi, Barbara, 2008. "Which Structural Parameters Are "Structural"? Identifying the Sources of Instabilities in Economic Models," Working Papers 08-02, Duke University, Department of Economics.

    Cited by:

    1. Samuel Hurtado, 2013. "DSGE Models and the Lucas critique," Working Papers 1310, Banco de España.
    2. Emilian DOBRESCU, 2017. "Modelling an Emergent Economy and Parameter Instability Problem," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-28, June.
    3. Bacchetta, Philippe & van Wincoop, Eric, 2013. "On the unstable relationship between exchange rates and macroeconomic fundamentals," Journal of International Economics, Elsevier, vol. 91(1), pages 18-26.

  23. Hall, Alastair & Inoue, Atsushi & Nason M, James & Rossi, Barbara, 2007. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Working Papers 07-04, Duke University, Department of Economics.

    Cited by:

    1. Müller, Gernot & Wolf, Martin & Hettig, Thomas, 2019. "Exchange Rate Undershooting: Evidence and Theory," CEPR Discussion Papers 13597, C.E.P.R. Discussion Papers.
    2. Patrick Fève & Julien Matheron & Jean‐Guillaume Sahuc, 2009. "Minimum Distance Estimation and Testing of DSGE Models from Structural VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 883-894, December.
    3. Rochelle M. Edge & Thomas Laubach & John C. Williams, 2010. "Welfare‐maximizing monetary policy under parameter uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 129-143, January.
    4. Karel Mertens & Morten Overgaard Ravn, 2010. "Online Appendix to "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks"," Online Appendices 09-221, Review of Economic Dynamics.
    5. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2021. "Uncertainty and Monetary Policy during the Great Recession," Economics Working Papers 2021-05, Department of Economics and Business Economics, Aarhus University.
    6. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    7. James Cloyne, 2014. "Government spending shocks, wealth effects and distortionary taxes," Discussion Papers 1413, Centre for Macroeconomics (CFM).
    8. Danthine, Jean-Pierre & Kurmann, André, 2010. "The business cycle implications of reciprocity in labor relations," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 837-850, October.
    9. 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.
    10. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2020. "Uncertainty and Monetary Policy during Extreme Events," Economics Working Papers 2020-11, Department of Economics and Business Economics, Aarhus University.
    11. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    12. Mario Martinoli & Alessio Moneta & Gianluca Pallante, 2022. "Calibration and Validation of Macroeconomic Simulation Models by Statistical Causal Search," LEM Papers Series 2022/33, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Alastair R. Hall & Atsushi Inoue & James M Nason & Barbara Rossi, 2009. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Centre for Growth and Business Cycle Research Discussion Paper Series 127, Economics, The University of Manchester.
    14. Poghosyan, Karen & Boldea, Otilia, 2013. "Structural versus matching estimation: Transmission mechanisms in Armenia," Economic Modelling, Elsevier, vol. 30(C), pages 136-148.
    15. Lucy Minford & David Meenagh, 2020. "Supply-Side Policy and Economic Growth: A Case Study of the UK," Open Economies Review, Springer, vol. 31(1), pages 159-193, February.
    16. Anna Kormilitsina, 2009. "Oil Price Shocks and the Optimality of Monetary Policy," Departmental Working Papers 0901, Southern Methodist University, Department of Economics.
    17. Morten O. Ravn & Stephanie Schmitt-Grohė & Martín Uribe & Lenno Uusküla, 2010. "Deep Habits and the Dynamic Effects of Monetary Policy Shocks," NBER Chapters, in: Sticky Prices and Inflation Dynamics (NBER-TCER-CEPR), pages 236-258, National Bureau of Economic Research, Inc.
    18. Rui Faustino, 2019. "Endogenous Quality and Firm Entry," Working Papers REM 2019/0107, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    19. Minford, Patrick & Wickens, Michael R. & Meenagh, David, 2012. "Testing macroeconomic models by indirect inference on unfiltered data," CEPR Discussion Papers 9058, C.E.P.R. Discussion Papers.
    20. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    21. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    22. Karel Mertens & Morten O. Ravn, 2008. "The Aggregate Effects of Anticipated and Unanticipated U.S. Tax Policy Shocks: Theory and Empirical Evidence," Economics Working Papers ECO2008/05, European University Institute.
    23. Efrem Castelnuovo & Giovanni Pellegrino, 2018. "Uncertainty-dependent Effects of Monetary Policy Shocks: A New Keynesian Interpretation," Melbourne Institute Working Paper Series wp2018n02, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    24. Ronayne, David, 2011. "Which Impulse Response Function?," Economic Research Papers 270753, University of Warwick - Department of Economics.
    25. Özer Karagedikli & Troy Matheson & Christie Smith & Shaun P. Vahey, 2007. "RBCs and DSGEs:The Computational Approach to Business Cycle Theory and Evidence," Reserve Bank of New Zealand Discussion Paper Series DP2007/15, Reserve Bank of New Zealand.
    26. Riccardo DiCecio & Edward Nelson, 2007. "An estimated DSGE model for the United Kingdom," Review, Federal Reserve Bank of St. Louis, vol. 89(Jul), pages 215-232.
    27. Anna Mikusheva, 2014. "Estimation of dynamic stochastic general equilibrium models (in Russian)," Quantile, Quantile, issue 12, pages 1-21, February.
    28. Pablo A. Guerrón-Quintana & James M. Nason, 2013. "Bayesian estimation of DSGE models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 21, pages 486-512, Edward Elgar Publishing.
    29. Òscar Jordà & Sharon Kozicki, 2007. "Estimation and Inference by the Method of Projection Minimum Distance," Staff Working Papers 07-56, Bank of Canada.
    30. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "The small sample properties of Indirect Inference in testing and estimating DSGE models," Cardiff Economics Working Papers E2018/7, Cardiff University, Cardiff Business School, Economics Section.
    31. Michiru Sakane, 2010. "News-Driven International Business Cycles: Effects of the US News Shock on the Canadian Economy," Global COE Hi-Stat Discussion Paper Series gd09-129, Institute of Economic Research, Hitotsubashi University.
    32. Davidson, James & Meenagh, David & Minford, Patrick & Wickens, Michael, 2010. "Why crises happen - nonstationary macroeconomics," Cardiff Economics Working Papers E2010/13, Cardiff University, Cardiff Business School, Economics Section.
    33. Morten O. Ravn & Karel Mertens, 2009. "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy shocks," 2009 Meeting Papers 480, Society for Economic Dynamics.
    34. Punnoose Jacob & Lenno Uuskula, 2016. "Deep habits and exchange rate pass-through," CAMA Working Papers 2016-17, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    35. Daniil Lomonosov, 2023. "Shocks of Business Activity and Specific Shocks to Oil Market in DSGE Model of Russian Economy and Their Influence Under Different Monetary Policy Regimes," Russian Journal of Money and Finance, Bank of Russia, vol. 82(4), pages 44-79, December.
    36. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2011. "How much nominal rigidity is there in the US economy? Testing a new Keynesian DSGE model using indirect inference," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2078-2104.
    37. Ruge-Murcia, Francisco, 2020. "Estimating nonlinear dynamic equilibrium models by matching impulse responses," Economics Letters, Elsevier, vol. 197(C).
    38. Meenagh, David & Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2018. "Testing DSGE Models by indirect inference: a survey of recent findings," Cardiff Economics Working Papers E2018/14, Cardiff University, Cardiff Business School, Economics Section.
    39. Luca Brugnolini, 2018. "About Local Projection Impulse Response Function Reliability," CEIS Research Paper 440, Tor Vergata University, CEIS, revised 09 Jun 2018.
    40. Michael Creel & Dennis Kristensen, 2015. "On Selection of Statistics for Approximate Bayesian Computing or the Method of Simulated Moments," UFAE and IAE Working Papers 950.15, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC), revised 27 Feb 2015.
    41. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    42. Fève, Patrick & Matheron, Julien & Sahuc, Jean-Guillaume, 2007. "Optimal Monetary Policy and Technological Shocks in the Post-War US Business Cycle," IDEI Working Papers 484, Institut d'Économie Industrielle (IDEI), Toulouse.
    43. Tayebeh Sadat Tabatabaei & Pedram Asef, 2021. "Evaluation of Energy Price Liberalization in Electricity Industry: A Data-Driven Study on Energy Economics," Energies, MDPI, vol. 14(22), pages 1-19, November.
    44. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    45. Guay, Alain & Pelgrin, Florian, 2023. "Structural VAR models in the Frequency Domain," Journal of Econometrics, Elsevier, vol. 236(1).
    46. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    47. Matteo Barigozzi & Roxana Halbleib & David Veredas, 2012. "Which model to match?," Working Papers 1229, Banco de España.
    48. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian Model: a formal test of backward- and forward-looking expectations," MPRA Paper 40278, University Library of Munich, Germany.
    49. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.
    50. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian model: A formal test of backward- and forward-looking behavior," Economics Working Papers 2012-07, Christian-Albrechts-University of Kiel, Department of Economics.
    51. Cengiz Tunc & Denis Pelletier, 2013. "Endogenous Life-Cycle Housing Investment and Portfolio Allocation," Working Papers 1345, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    52. Francisco RUGE-MURCIA, 2014. "Indirect Inference Estimation of Nonlinear Dynamic General Equilibrium Models : With an Application to Asset Pricing under Skewness Risk," Cahiers de recherche 15-2014, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    53. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    54. Jang, Tae-Seok, 2012. "Structural estimation of the New-Keynesian Model: a formal test of backward- and forward-looking expectations," MPRA Paper 39669, University Library of Munich, Germany.

  24. Jim Nason & Barbara Rossi & Atsushi Inoue & Alastair Hall, 2007. "Information Criteria for Impulse Response Function Matching Estimation," 2007 Meeting Papers 293, Society for Economic Dynamics.

    Cited by:

    1. Patrick Fève & Julien Matheron & Jean‐Guillaume Sahuc, 2009. "Minimum Distance Estimation and Testing of DSGE Models from Structural VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 883-894, December.
    2. Rochelle M. Edge & Thomas Laubach & John C. Williams, 2010. "Welfare‐maximizing monetary policy under parameter uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 129-143, January.
    3. Danthine, Jean-Pierre & Kurmann, André, 2010. "The business cycle implications of reciprocity in labor relations," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 837-850, October.
    4. Alastair R. Hall & Atsushi Inoue & James M Nason & Barbara Rossi, 2009. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Centre for Growth and Business Cycle Research Discussion Paper Series 127, Economics, The University of Manchester.
    5. Anna Kormilitsina, 2009. "Oil Price Shocks and the Optimality of Monetary Policy," Departmental Working Papers 0901, Southern Methodist University, Department of Economics.
    6. Morten O. Ravn & Stephanie Schmitt-Grohė & Martín Uribe & Lenno Uusküla, 2010. "Deep Habits and the Dynamic Effects of Monetary Policy Shocks," NBER Chapters, in: Sticky Prices and Inflation Dynamics (NBER-TCER-CEPR), pages 236-258, National Bureau of Economic Research, Inc.
    7. Karel Mertens & Morten Overgaard Ravn, 2011. "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 27-54, January.
    8. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    9. Karel Mertens & Morten O. Ravn, 2008. "The Aggregate Effects of Anticipated and Unanticipated U.S. Tax Policy Shocks: Theory and Empirical Evidence," Economics Working Papers ECO2008/05, European University Institute.
    10. Ronayne, David, 2011. "Which Impulse Response Function?," Economic Research Papers 270753, University of Warwick - Department of Economics.
    11. Özer Karagedikli & Troy Matheson & Christie Smith & Shaun P. Vahey, 2007. "RBCs and DSGEs:The Computational Approach to Business Cycle Theory and Evidence," Reserve Bank of New Zealand Discussion Paper Series DP2007/15, Reserve Bank of New Zealand.
    12. Riccardo DiCecio & Edward Nelson, 2007. "An estimated DSGE model for the United Kingdom," Review, Federal Reserve Bank of St. Louis, vol. 89(Jul), pages 215-232.
    13. Òscar Jordà & Sharon Kozicki, 2007. "Estimation and Inference by the Method of Projection Minimum Distance," Staff Working Papers 07-56, Bank of Canada.
    14. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    15. Fève, Patrick & Matheron, Julien & Sahuc, Jean-Guillaume, 2007. "Optimal Monetary Policy and Technological Shocks in the Post-War US Business Cycle," IDEI Working Papers 484, Institut d'Économie Industrielle (IDEI), Toulouse.

  25. Kilian, Lutz & Inoue, Atsushi & ,, 2006. "Do Actions Speak Louder than Words? Household Expectations of Inflation Based on Micro Consumption Data," CEPR Discussion Papers 5790, C.E.P.R. Discussion Papers.

    Cited by:

    1. Rossi, Barbara & Inoue, Atsushi & Anderson, Emily, 2013. "Heterogeneous Consumers and Fiscal Policy Shocks," CEPR Discussion Papers 9631, C.E.P.R. Discussion Papers.
    2. Eda Gulsen & Hakan Kara, 2020. "Formation of inflation expectations: Does macroeconomic and policy environment matter?," Koç University-TUSIAD Economic Research Forum Working Papers 2017, Koc University-TUSIAD Economic Research Forum.
    3. Carrera, César, 2012. "Estimating Information Rigidity using Firms’ Survey Data," Working Papers 2012-004, Banco Central de Reserva del Perú.
    4. Wilbert van der Klaauw & Wandi Bruine de Bruin & Giorgio Topa & Basit Zafar & Olivier Armantier, 2012. "Inflation Expectations and Behavior: Do Survey Respondents Act on their Beliefs?," 2012 Meeting Papers 121, Society for Economic Dynamics.
    5. Menz, Jan-Oliver & Poppitz, Philipp, 2013. "Household`s Disagreement on Inflation Expectations and Socioeconomic Media Exposure in Germany," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 80006, Verein für Socialpolitik / German Economic Association.
    6. Fabio Canova & Luca Gambetti, 2007. "Do expectations matter? The Great Moderation revisited," Economics Working Papers 1084, Department of Economics and Business, Universitat Pompeu Fabra, revised Jan 2009.
    7. 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.
    8. N. Gregory Mankiw & Ricardo Reis, 2010. "Imperfect Information and Aggregate Supply," NBER Working Papers 15773, National Bureau of Economic Research, Inc.
    9. MURASAWA Yasutomo, 2010. "Measuring Inflation Expectations Using Interval-Coded Data," ESRI Discussion paper series 236, Economic and Social Research Institute (ESRI).
    10. Yavari, Kazem & Najjarzade, Reza & Tavakolian, Hossein & Bahador, Ali, 2016. "Effect of Nominal Exchange Rate Volatility on Output in Iran’s Economy," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 11(4), pages 419-442, October.
    11. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
    12. Michael J. Lamla & Sarah Lein, 2010. "The Euro Cash Changeover, Inflation Perceptions and the Media," KOF Working papers 10-254, KOF Swiss Economic Institute, ETH Zurich.
    13. Gbaguidi, David, 2012. "La courbe de Phillips : temps d’arbitrage et/ou arbitrage de temps," L'Actualité Economique, Société Canadienne de Science Economique, vol. 88(1), pages 87-119, mars.

  26. Atsushi Inoue & Gary Solon, 2005. "A Portmanteau Test for Serially Correlated Errors in Fixed Effects Models," NBER Technical Working Papers 0310, National Bureau of Economic Research, Inc.

    Cited by:

    1. Okui, Ryo, 2009. "Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2897-2909.
    2. Jochmans, Koen, 2020. "A Portmanteau Test For Correlation In Short Panels," Econometric Theory, Cambridge University Press, vol. 36(6), pages 1159-1166, December.
    3. Alessandra Cepparulo & Giuseppe Eusepi & Luisa Giuriato, 2021. "Public finances and Public Private Partnerships in the European Union," Working Papers in Public Economics 195, University of Rome La Sapienza, Department of Economics and Law.
    4. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    5. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    6. Boto-García, David, 2023. "Investigating the two-way relationship between mobility flows and COVID-19 cases," Economic Modelling, Elsevier, vol. 118(C).
    7. Walter Sosa Escudero, 2007. "Testing for Persistence in the Error Component Model:A One-Sided Approach," Working Papers 94, Universidad de San Andres, Departamento de Economia, revised Feb 2007.
    8. Jochmans, K., 2019. "Testing Correlation in Error-Component Models," Cambridge Working Papers in Economics 1993, Faculty of Economics, University of Cambridge.
    9. Gregory A. Falls & Paul A. Natke & Linlan Xiao, 2022. "College football attendance in the long run: The Football Championship Subdivision," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2172-2183, September.
    10. Koen Jochmans, 2020. "Testing for correlation in error‐component models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 860-878, November.
    11. Cariolle, Joël, 2021. "International connectivity and the digital divide in Sub-Saharan Africa," Information Economics and Policy, Elsevier, vol. 55(C).
    12. Srivastava, Preety & Trinh, Trong-Anh, 2021. "The effect of parental smoking on children’s cognitive and non-cognitive skills," Economics & Human Biology, Elsevier, vol. 41(C).
    13. Jochmans, K. & Verardi, V., 2019. "xtserialpm: A portmanteau test for serial correlation in a linear panel model," Cambridge Working Papers in Economics 1944, Faculty of Economics, University of Cambridge.
    14. Yamagata, Takashi, 2008. "A joint serial correlation test for linear panel data models," Journal of Econometrics, Elsevier, vol. 146(1), pages 135-145, September.
    15. Paul M. Guest, 2021. "Risk Management in Financial Institutions: A Replication," Journal of Finance, American Finance Association, vol. 76(5), pages 2689-2707, October.
    16. Juhl, Ted & Sosa-Escudero, Walter, 2014. "Testing for heteroskedasticity in fixed effects models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 484-494.
    17. Okui Ryo, 2014. "Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 1-53, July.
    18. Attila, Joseph G., 2022. "Does bank deposits volatility react to political instability in developing countries?," Finance Research Letters, Elsevier, vol. 49(C).
    19. Renz, Franziska M. & Vogel, Julian U.N. & Xie, Feixue, 2023. "Do as they say or do as they do? — Uncovering the effects of inappropriate methods and unreliable data in boardroom diversity research," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 410-420.
    20. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.
    21. Alejo, Javier & Montes-Rojas, Gabriel & Sosa-Escudero, Walter, 2018. "Testing for serial correlation in hierarchical linear models," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 101-116.
    22. Tijl Hendrich & Jennifer Buurma-Olsen & Judith Bayer, 2022. "Entries and Regional Growth: The Role of Relatedness," CPB Discussion Paper 433, CPB Netherlands Bureau for Economic Policy Analysis.

  27. Atsushi Inoue & Gary Solon, 2005. "Two-Sample Instrumental Variables Estimators," NBER Technical Working Papers 0311, National Bureau of Economic Research, Inc.

    Cited by:

    1. Simon Freyaldenhoven & Christian Hansen & Jesse M. Shapiro, 2019. "Pre-event Trends in the Panel Event-Study Design," American Economic Review, American Economic Association, vol. 109(9), pages 3307-3338, September.
    2. Benjamin Scharadin & Edward C. Jaenicke, 2020. "Time spent on childcare and the household Healthy Eating Index," Review of Economics of the Household, Springer, vol. 18(2), pages 357-386, June.
    3. Michele Cantarella & Chiara Strozzi, 2018. "Labour market effects of crowdwork in the US and EU: an empirical investigation," Center for Economic Research (RECent) 140, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    4. Nancy A. Daza Báez, 2021. "Intergenerational Earnings Mobility in Mexico," DoQSS Working Papers 21-10, Quantitative Social Science - UCL Social Research Institute, University College London.
    5. Peter Siminski, 2013. "Employment Effects of Army Service and Veterans' Compensation: Evidence from the Australian Vietnam-Era Conscription Lotteries," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 87-97, March.
    6. Evgenia Kogan Dechter, 2014. "Maternity Leave, Effort Allocation, and Postmotherhood Earnings," Journal of Human Capital, University of Chicago Press, vol. 8(2), pages 97-125.
    7. Cervini-Plá, María, 2012. "Exploring the sources of earnings transmission in Spain," MPRA Paper 36093, University Library of Munich, Germany.
    8. Bertrand GARBINTI & Frédérique SAVIGNAC, 2020. "Accounting for Intergenerational Wealth Mobility in France over the 20th Century: Method and Estimations," Working Papers 2020-16, Center for Research in Economics and Statistics.
    9. Anja Gaentzsch & Gabriela Zapata Román, 2018. "More educated, less mobile? Diverging trends in income and educational mobility in Chile and Peru," Global Development Institute Working Paper Series 312018, GDI, The University of Manchester.
    10. Erik Meijer & Edward Oczkowski & Tom Wansbeek, 2021. "How measurement error affects inference in linear regression," Empirical Economics, Springer, vol. 60(1), pages 131-155, January.
    11. Christian Dustmann & Patrick A. Puhani & Uta Schönberg, 2012. "The Long-term Effects of School Quality on Labor Market Outcomes and Educational Attainment," RF Berlin - CReAM Discussion Paper Series 1208, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    12. Devereux, Paul J. & Hart, Robert A., 2008. "Forced to Be Rich? Returns to Compulsory Schooling in Britain," IZA Discussion Papers 3305, Institute of Labor Economics (IZA).
    13. Gerard J. van den Berg & Pia R. Pinger & Johannes Schoch, 2014. "Instrumental Variable Estimation of the Causal Effect of Hunger Early in Life on Health Later in Life," SOEPpapers on Multidisciplinary Panel Data Research 710, DIW Berlin, The German Socio-Economic Panel (SOEP).
    14. Card, David & Lee, David S. & Pei, Zhuan & Weber, Andrea, 2015. "Inference on Causal Effects in a Generalized Regression Kink Design," IZA Discussion Papers 8757, Institute of Labor Economics (IZA).
    15. Xiang, Di & Zhan, Lue & Bordignon, Massimo, 2020. "A reconsideration of the sugar sweetened beverage tax in a household production model," Food Policy, Elsevier, vol. 95(C).
    16. Markus Jantti & Stephen P. Jenkins, 2014. "Income Mobility," Working Papers 319, ECINEQ, Society for the Study of Economic Inequality.
    17. Kevin Lang & Rashmi Barua, 2010. "School Entry, Educational Attainment and Quarter of Birth: A Cautionary Tale of LATE," Boston University - Department of Economics - Working Papers Series WP2010-019, Boston University - Department of Economics.
    18. Hans van Kippersluis & Owen O'Donnell & Eddy van Doorslaer, 0000. "Long Run Returns to Education: Does Schooling Lead to an Extended Old Age?," Tinbergen Institute Discussion Papers 09-037/3, Tinbergen Institute.
    19. Rania Gihleb, 2015. "Nuns and the Effects of Catholic Schools Evidence from Vatican II," Working Paper 5857, Department of Economics, University of Pittsburgh.
    20. Bauer, Thomas K. & Bender, Stefan & Paloyo, Alfredo R. & Schmidt, Christoph M., 2014. "Do guns displace books? The impact of compulsory military service on educational attainment," Economics Letters, Elsevier, vol. 124(3), pages 513-515.
    21. Chen, Jiaying & Park, Albert, 2021. "School entry age and educational attainment in developing countries: Evidence from China's compulsory education law," Journal of Comparative Economics, Elsevier, vol. 49(3), pages 715-732.
    22. Henry S. Farber & Daniel Herbst & Ilyana Kuziemko & Suresh Naidu, 2021. "Unions and Inequality Over the Twentieth Century: New Evidence from Survey Data," Working Papers 2021-91, Princeton University. Economics Department..
    23. Frisvold, David E., 2015. "Nutrition and cognitive achievement: An evaluation of the School Breakfast Program," Journal of Public Economics, Elsevier, vol. 124(C), pages 91-104.
    24. Kadir Atalay & Garry F. Barrett & Peter Siminski, 2019. "Pension incentives and the joint retirement of couples: evidence from two natural experiments," Journal of Population Economics, Springer;European Society for Population Economics, vol. 32(3), pages 735-767, July.
    25. Olivetti, Claudia & Paserman, M. Daniele & Salisbury, Laura, 2018. "Three-generation mobility in the United States, 1850–1940: The role of maternal and paternal grandparents," Explorations in Economic History, Elsevier, vol. 70(C), pages 73-90.
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    75. Keller, Wolfgang & Shiue, Carol, 2014. "The Link Between Fundamentals and Proximate Factors in Development," CEPR Discussion Papers 9883, C.E.P.R. Discussion Papers.
    76. Kumari, Meena & Bao, Yanchun & S. Clarke, Paul & Smart, Melissa, 2018. "A comparison of robust methods for Mendelian randomization using multiple genetic variants," ISER Working Paper Series 2018-08, Institute for Social and Economic Research.
    77. Poy, Samuele & Schüller, Simone, 2020. "Internet and voting in the social media era: Evidence from a local broadband policy," Munich Reprints in Economics 84757, University of Munich, Department of Economics.
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    81. Justine Hastings & Ryan Kessler & Jesse M. Shapiro, 2021. "The Effect of SNAP on the Composition of Purchased Foods: Evidence and Implications," American Economic Journal: Economic Policy, American Economic Association, vol. 13(3), pages 277-315, August.
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    86. Michele Cantarella & Chiara Strozzi, 2018. "Labour market effects of crowdwork in US and EU: an empirical investigation," Department of Economics 0139, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    87. Doan, Quang Hung & Nguyen, Ngoc Anh, 2016. "Intergenerational Income Mobility in Vietnam," MPRA Paper 70603, University Library of Munich, Germany.
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  28. Kilian, Lutz & Inoue, Atsushi, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.

    Cited by:

    1. Carlos Capistrán & Allan Timmermann, 2008. "Forecast Combination With Entry and Exit of Experts," CREATES Research Papers 2008-55, Department of Economics and Business Economics, Aarhus University.
    2. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    3. Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussão 604, Department of Economics PUC-Rio (Brazil).
    4. Francesco Audrino & Marcelo C. Medeiros, 2008. "Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process," University of St. Gallen Department of Economics working paper series 2008 2008-16, Department of Economics, University of St. Gallen.
    5. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
    6. António Rua & Francisco Craveiro Dias, 2008. "Forecasting Using Targeted Diffusion Indexes," Working Papers w200807, Banco de Portugal, Economics and Research Department.
    7. Ina Nurmalia Kurniati, 2015. "Forecasting Growth Of Third Party Funds," Working Papers WP/10/2015, Bank Indonesia.
    8. Clements, Michael P. & Galvao, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation," Economic Research Papers 269743, University of Warwick - Department of Economics.
    9. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2011. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Working Papers 201122, University of Pretoria, Department of Economics.
    10. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," Advances in Econometrics, in: 30th Anniversary Edition, pages 171-196, Emerald Group Publishing Limited.
    11. Xu Cheng & Bruce E. Hansen, 2012. "Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach," PIER Working Paper Archive 12-046, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    12. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    13. 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.
    14. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.

  29. Inoue, Atsushi & Rossi, Barbara, 2005. "Monitoring and Forecasting Currency Crises," Working Papers 05-02, Duke University, Department of Economics.

    Cited by:

    1. Andrea Cipollini & George Kapetanios, 2005. "Forecasting Financial Crises and Contagion in Asia Using Dynamic Factor Analysis," Working Papers 538, Queen Mary University of London, School of Economics and Finance.
    2. Mirjana Jemović & Srđan Marinković, 2021. "Determinants of financial crises—An early warning system based on panel logit regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 103-117, January.
    3. Hyeyoen Kim, 2011. "Large Data Sets, Nonlinearity and the Speed of Adjustment to Real Exchange Rate Shocks," Post-Print hal-00665456, HAL.
    4. Teuta Ismaili Muharremi, 2015. "Currency Crisis Revisited: A Literature Review," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 11(6), pages 117-124, December.
    5. Ryota Nakatani, 2014. "The Effects of Financial and Real Shocks, Structural Vulnerability and Monetary Policy on Exchange Rates from the Perspective of Currency Crises Models," UTokyo Price Project Working Paper Series 043, University of Tokyo, Graduate School of Economics.
    6. Boonman, Tjeerd M. & Jacobs, Jan P.A.M. & Kuper, Gerard H., 2012. "The Global Financial Crisis and currency crises in Latin America," Research Report 12005-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    7. Yucel, Eray, 2011. "A Review and Bibliography of Early Warning Models," MPRA Paper 32893, University Library of Munich, Germany.
    8. Gatopoulos, Georgios & Loubergé, Henri, 2013. "Combined use of foreign debt and currency derivatives under the threat of currency crises: The case of Latin American firms," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 54-75.

  30. Alastair R. Hall & Atsushi Inoue, 2005. "The Large Sample Behaviour of the Generalized Method of Moments Estimator in Misspecified Models," Econometrics 0505002, University Library of Munich, Germany.

    Cited by:

    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    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. Marmer, Vadim & Otsu, Taisuke, 2008. "Optimal Comparison of Misspecified Moment Restriction Models under a Chosen Measure of Fit," Microeconomics.ca working papers vadim_marmer-2008-13, Vancouver School of Economics, revised 25 Jul 2011.
    4. Ai, Chunrong & Chen, Xiaohong, 2007. "Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables," Journal of Econometrics, Elsevier, vol. 141(1), pages 5-43, November.
    5. Michael Jansson & Demian Pouzo, 2019. "Towards a general large sample theory for regularized estimators," CeMMAP working papers CWP63/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Nikolay Gospodinov & Raymond Kan & Cesare Robotti, 2018. "Asymptotic variance approximations for invariant estimators in uncertain asset-pricing models," Econometric Reviews, Taylor & Francis Journals, vol. 37(7), pages 695-718, August.
    7. George Hall & John Rust, 2002. "Econometric Methods for Endogenously Sampled Time Series: The Case of Commodity Price Speculation in the Steel Market," Cowles Foundation Discussion Papers 1376, Cowles Foundation for Research in Economics, Yale University.
    8. Caner, Mehmet, 2014. "Near exogeneity and weak identification in generalized empirical likelihood estimators: Many moment asymptotics," Journal of Econometrics, Elsevier, vol. 182(2), pages 247-268.
    9. Jondeau, E. & Le Bihan, H., 2003. "ML vs GMM Estimates of Hybrid Macroeconomic Models (With an Application to the New Phillips Curve)," Working papers 103, Banque de France.
    10. Raymond Kan & Cesare Robotti, 2006. "Specification tests of asset pricing models using excess returns," FRB Atlanta Working Paper 2006-10, Federal Reserve Bank of Atlanta.
    11. Lewbel, Arthur & Choi, Jin Young & Zhou, Zhuzhu, 2023. "Over-identified Doubly Robust identification and estimation," Journal of Econometrics, Elsevier, vol. 235(1), pages 25-42.
    12. Gagliardini, Patrick & Trojani, Fabio & Urga, Giovanni, 2005. "Robust GMM tests for structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 139-182.
    13. Pierluigi Balduzzi & Cesare Robotti, 2005. "Asset-pricing models and economic risk premia: a decomposition," FRB Atlanta Working Paper 2005-13, Federal Reserve Bank of Atlanta.
    14. Michael Creel & Jiti Gao & Han Hong & Dennis Kristensen, 2016. "Bayesian Indirect Inference and the ABC of GMM," Monash Econometrics and Business Statistics Working Papers 1/16, Monash University, Department of Econometrics and Business Statistics.
    15. Lavergne, Pascal, 2015. "Assessing the Approximate Validity of Moment Restrictions," TSE Working Papers 15-562, Toulouse School of Economics (TSE), revised May 2020.
    16. Jean-Jacques Forneron & Liang Zhong, 2023. "Convexity Not Required: Estimation of Smooth Moment Condition Models," Papers 2304.14386, arXiv.org.
    17. Andrews, Isaiah, 2019. "On the structure of IV estimands," Journal of Econometrics, Elsevier, vol. 211(1), pages 294-307.
    18. Bertille Antoine & Prosper Dovonon, 2020. "Robust Estimation with Exponentially Tilted Hellinger Distance," Discussion Papers dp20-02, Department of Economics, Simon Fraser University.
    19. Huigang Chen & Mr. Alin T Mirestean & Mr. Charalambos G Tsangarides, 2011. "Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model," IMF Working Papers 2011/230, International Monetary Fund.
    20. Jungbin Hwang & Byunghoon Kang & Seojeong Lee, 2019. "A Doubly Corrected Robust Variance Estimator for Linear GMM," Discussion Papers 2019-08, School of Economics, The University of New South Wales.
    21. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2023. "Optimal minimax rates of specification testing with data-driven bandwidth," Econometric Reviews, Taylor & Francis Journals, vol. 42(6), pages 487-512, June.
    22. Otilia Boldea & Alastair R. Hall, 2013. "Testing structural stability in macroeconometric models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 9, pages 206-228, Edward Elgar Publishing.
    23. Tao Chen & Gautam Tripathi, 2013. "Testing conditional symmetry without smoothing," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 273-313, June.
    24. Patrick Gagliardini & Diego Ronchetti, 2020. "Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 333-394.
    25. Jiti Gao & Han Hong, 2014. "A Computational Implementation of GMM," Monash Econometrics and Business Statistics Working Papers 24/14, Monash University, Department of Econometrics and Business Statistics.
    26. Kirill S. Evdokimov & Michal Kolesár, 2018. "Inference in Instrumental Variable Regression Analysis with Heterogeneous Treatment Effects," Working Papers 2018-16, Princeton University. Economics Department..
    27. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    28. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    29. Fuhrer, Jeffrey C. & Rudebusch, Glenn D., 2004. "Estimating the Euler equation for output," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1133-1153, September.
    30. Timothy B. Armstrong & Michal Kolesár, 2020. "Sensitivity Analysis using Approximate Moment Condition Models," Working Papers 2020-28, Princeton University. Economics Department..
    31. Yaroslav Mukhin, 2018. "Sensitivity of Regular Estimators," Papers 1805.08883, arXiv.org.
    32. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    33. Alain Guay & Florian Pelgrin, 2004. "The U.S. New Keynesian Phillips Curve: An Empirical Assessment," Staff Working Papers 04-35, Bank of Canada.
    34. Sueishi, Naoya, 2013. "Identification problem of the exponential tilting estimator under misspecification," Economics Letters, Elsevier, vol. 118(3), pages 509-511.
    35. Raymond Kan & Cesare Robotti, 2007. "Model comparison using the Hansen-Jagannathan distance," FRB Atlanta Working Paper 2007-04, Federal Reserve Bank of Atlanta.
    36. Magnolfi, Lorenzo & Sullivan, Christopher, 2022. "A comparison of testing and estimation of firm conduct," Economics Letters, Elsevier, vol. 212(C).
    37. Raymond Kan & Cesare Robotti, 0. "Comment on: Pseudo-True SDFs in Conditional Asset Pricing Models," Journal of Financial Econometrics, Oxford University Press, vol. 18(4), pages 729-735.
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    40. Richard Ashley & Christopher Parmeter, 2015. "Sensitivity analysis for inference in 2SLS/GMM estimation with possibly flawed instruments," Empirical Economics, Springer, vol. 49(4), pages 1153-1171, December.
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    43. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers 55/13, Institute for Fiscal Studies.
    44. Gao, Xiaodan & Hnatkovska, Viktoria & Marmer, Vadim, 2012. "Limited Participation in International Business Cycle Models: A Formal Evaluation," Microeconomics.ca working papers vadim_marmer-2012-1, Vancouver School of Economics, revised 21 Dec 2013.
    45. Francesco Bravo, 2022. "Misspecified semiparametric model selection with weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 558-586, July.
    46. Morris A. Davis & Robert F. Martin, 2005. "Housing, house prices, and the equity premium puzzle," Finance and Economics Discussion Series 2005-13, Board of Governors of the Federal Reserve System (U.S.).
    47. Jan F. KIVIET & Jerzy NIEMCZYK, 2013. "On the limiting and empirical distributions of IV estimators when some of the instruments are actually endogenous," Economic Growth Centre Working Paper Series 1311, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    48. Francesca Molinari, 2020. "Microeconometrics with Partial Identi?cation," CeMMAP working papers CWP15/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    49. Valentin Verdier, 2020. "Average treatment effects for stayers with correlated random coefficient models of panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 917-939, November.
    50. Corradi, Valentina & Swanson, Norman R., 2005. "Bootstrap specification tests for diffusion processes," Journal of Econometrics, Elsevier, vol. 124(1), pages 117-148, January.
    51. Onishi, Rikuto & Otsu, Taisuke, 2021. "Sample sensitivity for two-step and continuous updating GMM estimators," Economics Letters, Elsevier, vol. 198(C).
    52. Mehmet Caner, 2005. "Near Exogeneity and Weak Identification in Generalized Empirical Likelihood Estimators: Fixed and Many Moment Asymptotics," Econometrics 0509018, University Library of Munich, Germany.
    53. Seojeong Lee, 2015. "A Consistent Variance Estimator for 2SLS When Instruments Identify Different LATEs," Discussion Papers 2015-01, School of Economics, The University of New South Wales.
    54. Bruce E. Hansen & Seojeong Lee, 2019. "Asymptotic Theory for Clustered Samples," Papers 1902.01497, arXiv.org.
    55. Pierre Chausse & Dinghai Xu, 2012. "GMM Estimation of a Stochastic Volatility Model with Realized Volatility: A Monte Carlo Study," Working Papers 1203, University of Waterloo, Department of Economics, revised May 2012.
    56. Alexander Tsyplakov, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models (in Russian)," Quantile, Quantile, issue 8, pages 69-122, July.
    57. Seojeong Lee, 2013. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators," Discussion Papers 2013-09, School of Economics, The University of New South Wales.
    58. Hnatkovska, Viktoria & Marmer, Vadim & Tang, Yao, 2012. "Comparison of misspecified calibrated models: The minimum distance approach," Journal of Econometrics, Elsevier, vol. 169(1), pages 131-138.
    59. Mihai Giurcanu & Brett Presnell, 2018. "Bootstrap inference for misspecified moment condition models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 605-630, June.
    60. Morris Davis & Robert F. Martin, 2005. "Housing, House Prices, and the Equity Premium Revisited," 2005 Meeting Papers 753, Society for Economic Dynamics.
    61. Daniel Berkowitz & Mehmet Caner & Ying Fang, 2013. "The Validity of Instruments Revisited," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    62. Dovonon, Prosper, 2008. "Large sample properties of the three-step euclidean likelihood estimators under model misspecification," MPRA Paper 40025, University Library of Munich, Germany, revised 16 May 2010.
    63. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2018. "Rate Optimal Specification Test When the Number of Instruments is Large," KIER Working Papers 986, Kyoto University, Institute of Economic Research.
    64. Ying Fang, 2013. "GMM with Weak Identification and Near Exogenneity," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    65. Xu Cheng & Zhipeng Liao & Ruoyao Shi, 2013. "Uniform Asymptotic Risk of Averaging GMM Estimator Robust to Misspecification, Second Version," PIER Working Paper Archive 15-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Mar 2015.
    66. Ida Wolden Bache & Øistein Røislanda & Kjersti Næss Torstensen, 2011. "Interest Rate Smoothing and "Calvo-Type" Interest Rate Rules: A Comment on Levine, McAdam, and Pearlman (2007)," International Journal of Central Banking, International Journal of Central Banking, vol. 7(3), pages 79-90, September.
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    68. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    69. Bruce E. Hansen & Seojeong Jay Lee, 2018. "Inference for Iterated GMM Under Misspecification and Clustering," Discussion Papers 2018-07, School of Economics, The University of New South Wales.
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  31. Kilian, Lutz & Inoue, Atsushi, 2004. "Bagging Time Series Models," CEPR Discussion Papers 4333, C.E.P.R. Discussion Papers.

    Cited by:

    1. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
    2. Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Research Working Paper RWP 09-10, Federal Reserve Bank of Kansas City.
    3. Eric Hillebrand & Marcelo Cunha Medeiros, 2007. "Forecasting realized volatility models:the benefits of bagging and nonlinear specifications," Textos para discussão 547, Department of Economics PUC-Rio (Brazil).
    4. Michael McAleer & Marcelo C. Medeiros, 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," CIRJE F-Series CIRJE-F-686, CIRJE, Faculty of Economics, University of Tokyo.
    5. Ron Alquist & Lutz Kilian & Robert Vigfusson, 2011. "Forecasting the Price of Oil," Staff Working Papers 11-15, Bank of Canada.
    6. 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.
    7. Francesco Audrino & Marcelo C. Medeiros, 2008. "Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process," University of St. Gallen Department of Economics working paper series 2008 2008-16, Department of Economics, University of St. Gallen.
    8. António Rua & Francisco Craveiro Dias, 2008. "Forecasting Using Targeted Diffusion Indexes," Working Papers w200807, Banco de Portugal, Economics and Research Department.
    9. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    10. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    11. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
    12. Francesco Audrino & Marcelo Cunha Medeiros, 2010. "Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging," Textos para discussão 570, Department of Economics PUC-Rio (Brazil).
    13. Francesco Audrino & Kameliya Filipova, 2009. "Yield Curve Predictability, Regimes, and Macroeconomic Information: A Data-Driven Approach," University of St. Gallen Department of Economics working paper series 2009 2009-10, Department of Economics, University of St. Gallen.
    14. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary University of London, School of Economics and Finance.
    15. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    16. Chao Liang & Yi Zhang & Yaojie Zhang, 2022. "Forecasting the volatility of the German stock market: New evidence," Applied Economics, Taylor & Francis Journals, vol. 54(9), pages 1055-1070, February.
    17. Eric Hillebrand & Marcelo Medeiros, 2010. "The Benefits of Bagging for Forecast Models of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 571-593.
    18. Liu, Na & Gao, Fumin, 2022. "The world uncertainty index and GDP growth rate," Finance Research Letters, Elsevier, vol. 49(C).
    19. Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
    20. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    21. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
    22. Wang, Jiqian & Huang, Yisu & Ma, Feng & Chevallier, Julien, 2020. "Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? New evidence," Energy Economics, Elsevier, vol. 91(C).

  32. Kilian, Lutz & Inoue, Atsushi, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers 3809, C.E.P.R. Discussion Papers.

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    1. Atsushi Inoue & Mototsugu Shintani, 2018. "Quasi‐Bayesian model selection," Quantitative Economics, Econometric Society, vol. 9(3), pages 1265-1297, November.
    2. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    3. Pierre-Olivier Gourinchas & Hélène Rey, 2007. "International Financial Adjustment," Journal of Political Economy, University of Chicago Press, vol. 115(4), pages 665-703, August.
    4. Atsushi Inoue & Lutz Kilian & Fatma Burcu Kiraz, 2009. "Do Actions Speak Louder Than Words? Household Expectations of Inflation Based on Micro Consumption Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(7), pages 1331-1363, October.
    5. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
    6. Edith Skriner, 2008. "Forecasting Global Flows," FIW Working Paper series 009, FIW.
    7. Hansen, Bruce E., 2010. "Averaging estimators for autoregressions with a near unit root," Journal of Econometrics, Elsevier, vol. 158(1), pages 142-155, September.
    8. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 214, European Central Bank.
    9. 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].
    10. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
    11. 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.
    12. Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Research Working Paper RWP 09-10, Federal Reserve Bank of Kansas City.
    13. Frédérique Bec & Mélika Ben Salem, 2013. "Inventory investment and the business cycle: the usual suspect," Post-Print halshs-00846501, HAL.
    14. Barbara Rossi, 2005. "Are Exchange Rates Really Random Walks? Some Evidence Robust to Parameter Instability," International Finance 0503006, University Library of Munich, Germany.
    15. Thiago Carlomagno Carlo & Emerson Fernandes Marçal, 2016. "Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon," Applied Economics, Taylor & Francis Journals, vol. 48(50), pages 4846-4860, October.
    16. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    17. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    18. Chu, Pyung Kun & Hoff, Kristian & Molnár, Peter & Olsvik, Magnus, 2022. "Crude oil: Does the futures price predict the spot price?," Research in International Business and Finance, Elsevier, vol. 60(C).
    19. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    20. Susanne M. Schennach & Daniel Wilhelm, 2014. "A simple parametric model selection test," CeMMAP working papers 10/14, Institute for Fiscal Studies.
    21. Kirstin Hubrich & Kenneth D. West, 2010. "Forecast evaluation of small nested model sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
    22. Della Corte, Pasquale & Sarno, Lucio & Valente, Giorgio, 2010. "A century of equity premium predictability and the consumption-wealth ratio: An international perspective," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 313-331, June.
    23. Helmut Herwartz, 2011. "Specific-to-general predictor selection in approximate autoregressions—Monte Carlo evidence and a large scale performance assessment with real data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 147-168, June.
    24. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    25. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.
    26. Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, June.
    27. Skriner, Edith, 2007. "Forecasting Global Flows," Economics Series 214, Institute for Advanced Studies.
    28. 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.
    29. 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.
    30. Amor Aniss Benmoussa & Reinhard Ellwanger & Stephen Snudden, 2020. "The New Benchmark for Forecasts of the Real Price of Crude Oil," Staff Working Papers 20-39, Bank of Canada.
    31. Ron Alquist & Lutz Kilian & Robert Vigfusson, 2011. "Forecasting the Price of Oil," Staff Working Papers 11-15, Bank of Canada.
    32. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    33. Christiane Baumeister & Lutz Kilian, 2013. "What Central Bankers Need to Know about Forecasting Oil Prices," Staff Working Papers 13-15, Bank of Canada.
    34. 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.
    35. 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.
    36. Aatola, Piia & Ollikka, Kimmo & Ollikainen, Markku, 2012. "Informational Efficiency of the EU ETS market – a study of price predictability and profitable trading," Working Papers 28, VATT Institute for Economic Research.
    37. Giacomini, Raffaella & Rossi, Barbara, 2008. "Forecast Comparisons in Unstable Environments," Working Papers 08-04, Duke University, Department of Economics.
    38. Kathryn Dominguez & Freyan Panthaki, 2005. "What Defines "News" in Foreign Exchange Markets?," NBER Working Papers 11769, National Bureau of Economic Research, Inc.
    39. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    40. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    41. 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.
    42. 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.
    43. 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.
    44. Panos K. Pouliasis & Nikos C. Papapostolou, 2018. "Volatility and correlation timing: The role of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(11), pages 1407-1439, November.
    45. Pär Österholm, 2010. "Improving Unemployment Rate Forecasts Using Survey Data," Finnish Economic Papers, Finnish Economic Association, vol. 23(1), pages 16-26, Spring.
    46. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," ifo Working Paper Series 57, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    47. Heikki Kauppi, 2008. "Yield-Curve Based Probit Models for Forecasting U.S. Recessions: Stability and Dynamics," Discussion Papers 31, Aboa Centre for Economics.
    48. Mehmood, Sultan, 2013. "Terrorism and the macroeconomy: Evidence from Pakistan," MPRA Paper 44546, University Library of Munich, Germany.
    49. Ching-Kang Ing, 2005. "Accumulated Prediction Errors, Information Criteria And Optimal Forecasting For Autoregressive Time Series," Econometrics 0503020, University Library of Munich, Germany.
    50. Mario Meichle & Angelo Ranaldo & Attilio Zanetti, 2011. "Do financial variables help predict the state of the business cycle in small open economies? Evidence from Switzerland," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(4), pages 435-453, December.
    51. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.
    52. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    53. Kuang-Liang Chang & Nan-Kuang Chen & Charles Ka Yui Leung, 2016. "Losing Track of the Asset Markets: the Case of Housing and Stock," International Real Estate Review, Global Social Science Institute, vol. 19(4), pages 435-492.
    54. Lutz Kilian & Atsushi Inoue, 2004. "Bagging Time Series Models," Econometric Society 2004 North American Summer Meetings 110, Econometric Society.
    55. 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.
    56. Lutz Kilian & Simone Manganelli, 2007. "Quantifying the Risk of Deflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2-3), pages 561-590, March.
    57. Aatola, Piia, 2013. "Putting a Price on Carbon – Econometric Essays on the European Union Emissions Trading Scheme and its Impacts," Research Reports P62, VATT Institute for Economic Research.
    58. Frédérique Bec & Matteo Mogliani, 2013. "Nowcasting French GDP in Real-Time from Survey Opinions : Information or Forecast Combinations ?," Working Papers 2013-21, Center for Research in Economics and Statistics.
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    75. Peter Vlaar & Ard den Reijer, 2004. "Forecasting inflation: An art as well as a science!," Computing in Economics and Finance 2004 148, Society for Computational Economics.
    76. Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
    77. Lance Bachmeier & Qi Li & Dandan Liu, 2008. "Should Oil Prices Receive So Much Attention? An Evaluation Of The Predictive Power Of Oil Prices For The U.S. Economy," Economic Inquiry, Western Economic Association International, vol. 46(4), pages 528-539, October.
    78. 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.
    79. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    80. Costantini, Mauro & Kunst, Robert M., 2011. "On the Usefulness of the Diebold-Mariano Test in the Selection of Prediction Models," Economics Series 276, Institute for Advanced Studies.
    81. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2012. "Predicting BRICS Stock Returns Using ARFIMA Models," Working Papers 201235, University of Pretoria, Department of Economics.
    82. 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.
    83. Matteo Fragetta & Giovanni Melina, 2013. "Identification of monetary policy in SVAR models: a data-oriented perspective," Empirical Economics, Springer, vol. 45(2), pages 831-844, October.
    84. Adusei Jumah & Robert M. Kunst, 2008. "Seasonal prediction of European cereal prices: good forecasts using bad models?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 391-406.
    85. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    86. van den Berg, Jeroen & Candelon, Bertrand & Urbain, Jean-Pierre, 2008. "A cautious note on the use of panel models to predict financial crises," Economics Letters, Elsevier, vol. 101(1), pages 80-83, October.
    87. Giovanni Caggiano & Pietro Calice & Leone Leonida, 2013. "Working Paper 190 - Early Warning Systems and Systemic Banking Crises in Low Income Countries: A Multinomial Logit Approach," Working Paper Series 993, African Development Bank.
    88. Corina SAMAN, 2015. "Out-Of-Sample Forecasting Performance Of A Robust Neural Exchange Rate Model Of Ron/Usd," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 93-106, March.
    89. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
    90. Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
    91. Rotger, G.P. & Franses, Ph.H.B.F., 2006. "Forecasting high-frequency electricity demand with a diffusion index model," Econometric Institute Research Papers EI 2006-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    92. Nan Cai & Zongwu Cai & Ying Fang & Qiuhua Xu, 2015. "Forecasting major Asian exchange rates using a new semiparametric STAR model," Empirical Economics, Springer, vol. 48(1), pages 407-426, February.
    93. Ghandar, Adam & Michalewicz, Zbigniew & Zurbruegg, Ralf, 2016. "The relationship between model complexity and forecasting performance for computer intelligence optimization in finance," International Journal of Forecasting, Elsevier, vol. 32(3), pages 598-613.
    94. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    95. Zhu, Xiaoneng, 2015. "Out-of-sample bond risk premium predictions: A global common factor," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 155-173.
    96. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.

  33. Rossi, Barbara & Inoue, Atsushi, 2003. "Recursive Predictability Tests for Real-Time Data," Working Papers 03-24, Duke University, Department of Economics.

    Cited by:

    1. 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.
    2. Luca FANELLI & Giulio PALOMBA, 2007. "Simulation-Based Tests of Forward-Looking Models Under VAR Learning Dynamics," Working Papers 298, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. 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.
    4. Kenneth Kasa, 2007. "Learning and Model Validation," 2007 Meeting Papers 548, Society for Economic Dynamics.
    5. Todd E. Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Papers (Old Series) 1120, Federal Reserve Bank of Cleveland.
    6. Dean Croushore, 2008. "Frontiers of real-time data analysis," Working Papers 08-4, Federal Reserve Bank of Philadelphia.
    7. 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.
    8. Todd E. Clark & Michael W. McCracken, 2008. "Improving forecast accuracy by combining recursive and rolling forecasts," Working Papers 2008-028, Federal Reserve Bank of St. Louis.
    9. 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.
    10. Fanelli, Luca, 2007. "Evaluating the New Keynesian Phillips Curve under VAR-based learning," MPRA Paper 1616, University Library of Munich, Germany.
    11. Stanislav Anatolyev, 2006. "Nonparametric retrospection and monitoring of predictability of financial returns," Working Papers w0071, New Economic School (NES).
    12. 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.
    13. Paulo M.M. Rodrigues & Matei Demetrescu, 2019. "Testing for Episodic Predictability in Stock Returns," Working Papers w201906, Banco de Portugal, Economics and Research Department.
    14. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    15. Stanislav Anatolyev & Grigory Kosenok, 2011. "Sequential Testing with Uniformly Distributed Size," Working Papers w0123, New Economic School (NES).
    16. 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.
    17. Mihaela NICOLAU & Giulio PALOMBA & Ilaria TRAINI, 2013. "Are Futures Prices Influenced by Spot;Prices or Vice-versa? An Analysis of Crude;Oil, Natural Gas and Gold Markets," Working Papers 394, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

  34. Kilian, Lutz & Inoue, Atsushi, 2002. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," CEPR Discussion Papers 3671, C.E.P.R. Discussion Papers.

    Cited by:

    1. Imad Moosa, 2013. "Why is it so difficult to outperform the random walk in exchange rate forecasting?," Applied Economics, Taylor & Francis Journals, vol. 45(23), pages 3340-3346, August.
    2. Dai, Zhifeng & Zhang, Xiaotong & Li, Tingyu, 2023. "Forecasting stock return volatility in data-rich environment: A new powerful predictor," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    3. Schrimpf, Andreas, 2008. "International Stock Return Predictability Under Model Uncertainty," ZEW Discussion Papers 08-048, ZEW - Leibniz Centre for European Economic Research.
    4. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production with Automated Procedures," Econometric Society 2004 Latin American Meetings 177, Econometric Society.
    5. Inoue, Atsushi & Kilian, Lutz, 2003. "On the selection of forecasting models," Working Paper Series 214, European Central Bank.
    6. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    7. Romulo A. Chumacero, 2004. "Forecasting Chilean Industrial Production and Sales with Automated Procedures," Computing in Economics and Finance 2004 112, Society for Computational Economics.
    8. Krishnan, C.N.V. & Ritchken, Peter H. & Thomson, James B., 2010. "Predicting credit spreads," Journal of Financial Intermediation, Elsevier, vol. 19(4), pages 529-563, October.
    9. Pablo Pincheira, 2013. "A Simple Out-of-Sample Test for the Martingale Difference Hypothesis," Working Papers Central Bank of Chile 698, Central Bank of Chile.
    10. Kenneth Rogoff & Barbara Rossi & Yu-chin Chen, 2008. "Can Exchange Rates Forecast Commodity Prices?," 2008 Meeting Papers 540, Society for Economic Dynamics.
    11. Pham, Quynh Thi Thuy & Rudolf, Markus, 2021. "Gold, platinum, and industry stock returns," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 252-266.
    12. Junsoo Lee & John A. List & Mark Strazicich, 2005. "Nonrenewable Resource Prices: Deterministic or Stochastic Trends?," NBER Working Papers 11487, National Bureau of Economic Research, Inc.
    13. Dunbar, Kwamie, 2021. "Pricing the hedging factor in the cross-section of stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    14. Shiu-Sheng Chen, 2016. "Commodity prices and related equity prices," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 949-967, August.
    15. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Drivers of Realized Volatility for Emerging Countries with a Focus on South Africa: Fundamentals versus Sentiment," Mathematics, MDPI, vol. 11(6), pages 1-26, March.
    16. Pablo Pincheira & Jorge Selaive, 2011. "External imbalance, valuation adjustments and real Exchange rate: evidence of predictability in an emerging economy," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 26(1), pages 107-125, Junio.
    17. Marmer, Vadim, 2008. "Nonlinearity, nonstationarity, and spurious forecasts," Journal of Econometrics, Elsevier, vol. 142(1), pages 1-27, January.
    18. Pincheira, Pablo, 2017. "A Power Booster Factor for Out-of-Sample Tests of Predictability," MPRA Paper 77027, University Library of Munich, Germany.
    19. Gloria Gonzalez-Rivera & Joao Henrique Mazzeu & Esther Ruiz & Helena Veiga, 2017. "A Bootstrap Approach for Generalized Autocontour Testing. Implications for VIX Forecast Densities," Working Papers 201709, University of California at Riverside, Department of Economics.
    20. Ryan Greenaway-McGrevy & Nelson C. Mark & Donggyu Sul & Jyh-Lin Wu, 2012. "Exchange Rates as Exchange Rate Common Factors," Working Papers 212012, Hong Kong Institute for Monetary Research.
    21. Kyriazakou, Eleni & Panagiotidis, Theodore, 2017. "Causality analysis of the Canadian city house price indices: A cross-sample validation approach," The Journal of Economic Asymmetries, Elsevier, vol. 16(C), pages 42-52.
    22. Robert J. Hodrick & Tuomas Tomunen, 2018. "Taking the Cochrane-Piazzesi Term Structure Model Out of Sample: More Data, Additional Currencies, and FX Implications," NBER Working Papers 25092, National Bureau of Economic Research, Inc.
    23. Ming-Chih Lee & Chien-Liang Chiu & Wan-Hsiu Cheng, 2007. "Enhancing Forecast Accuracy By Using Long Estimation Periods," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 1(2), pages 1-9.
    24. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    25. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    26. Shi, Qi & Li, Bin, 2022. "Further evidence on financial information and economic activity forecasts in the United States," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    27. Michael W. McCracken & Todd E. Clark, 2003. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Computing in Economics and Finance 2003 183, Society for Computational Economics.
    28. Lucas W. Davis & Lutz Kilian, 2011. "The Allocative Cost of Price Ceilings in the U.S. Residential Market for Natural Gas," Journal of Political Economy, University of Chicago Press, vol. 119(2), pages 212-241.
    29. Chunming Yuan, 2008. "The Exchange Rate and Macroeconomic Determinants: Time-Varying Transitional Dynamics," UMBC Economics Department Working Papers 09-114, UMBC Department of Economics, revised 01 Nov 2009.
    30. Kirstin Hubrich & Kenneth D. West, 2010. "Forecast evaluation of small nested model sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 574-594.
    31. Ian Martin & Stefan Nagel, 2019. "Market Efficiency in the Age of Big Data," CESifo Working Paper Series 8015, CESifo.
    32. Francesco Ravazzolo & Philip Rothman, 2013. "Oil and U.S. GDP: A Real-Time Out-of-Sample Examination," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(2-3), pages 449-463, March.
    33. Della Corte, Pasquale & Sarno, Lucio & Valente, Giorgio, 2010. "A century of equity premium predictability and the consumption-wealth ratio: An international perspective," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 313-331, June.
    34. Fullerton, Thomas M. & Kelley, Brian W., 2008. "El Paso Housing Sector Econometric Forecast Accuracy," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(1), pages 385-402, April.
    35. Cotter, John & Eyiah-Donkor, Emmanuel & Potì, Valerio, 2023. "Commodity futures return predictability and intertemporal asset pricing," Journal of Commodity Markets, Elsevier, vol. 31(C).
    36. Pasquale Della Corte & Lucio Sarno & Daniel L. Thornton, 2007. "The expectation hypothesis of the term structure of very short-term rates: statistical tests and economic value," Working Papers 2006-061, Federal Reserve Bank of St. Louis.
    37. Ron Alquist & Gregory Bauer & Antonio Diez de los Rios, 2014. "What Does the Convenience Yield Curve Tell Us about the Crude Oil Market?," Staff Working Papers 14-42, Bank of Canada.
    38. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    39. Ana María Abarca G. & Felipe Alarcón G. & Pablo Pincheira B. & Jorge Selaive C., 2007. "Nominal Exchange Rate in Chile: Predictions based on technical analysis," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 10(2), pages 57-80, August.
    40. Sumudu W. Watugala, 2015. "Economic Uncertainty and Commodity Futures Volatility," Working Papers 15-14, Office of Financial Research, US Department of the Treasury.
    41. Manzan, Sebastiano & Westerhoff, Frank H., 2007. "Heterogeneous expectations, exchange rate dynamics and predictability," Journal of Economic Behavior & Organization, Elsevier, vol. 64(1), pages 111-128, September.
    42. Sousa, Ricardo M., 2010. "Consumption, (dis)aggregate wealth, and asset returns," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 606-622, September.
    43. 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.
    44. Cheung, Yin-Wong & Chinn, Menzie & Garcia Pascual, Antonio, 2003. "Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?," Santa Cruz Center for International Economics, Working Paper Series qt5fc508pt, Center for International Economics, UC Santa Cruz.
    45. Mahir Binici & Yin-Wong Cheung, 2011. "Exchange Rate Dynamics under Alternative Optimal Interest Rate Rules," Working Papers 1116, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    46. Markus Hertrich, 2022. "Foreign exchange interventions under a minimum exchange rate regime and the Swiss franc," Review of International Economics, Wiley Blackwell, vol. 30(2), pages 450-489, May.
    47. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
    48. João Sousa & Ricardo M. Sousa, 2011. "Asset Returns Under Model Uncertainty: Eveidence from the euro area, the U.K and the U.S," NIPE Working Papers 21/2011, NIPE - Universidade do Minho.
    49. David Haab & Dr. Thomas Nitschka, 2017. "Predicting returns on asset markets of a small, open economy and the influence of global risks," Working Papers 2017-14, Swiss National Bank.
    50. Virginie Coudert & Mathieu Gex, 2007. "Does Risk Aversion Drive Financial Crises? Testing the Predictive Power of Empirical Indicators," Working Papers 2007-02, CEPII research center.
    51. Kilian, Lutz & Vigfusson, Robert J., 2012. "Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries," CEPR Discussion Papers 8980, C.E.P.R. Discussion Papers.
    52. 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.
    53. Kim, Myung Suk, 2018. "Impacts of supply and demand factors on declining oil prices," Energy, Elsevier, vol. 155(C), pages 1059-1065.
    54. Todd E. Clark & Michael W. McCracken, 2010. "Reality checks and nested forecast model comparisons," Working Papers 2010-032, Federal Reserve Bank of St. Louis.
    55. Lu, Fei & Ma, Feng, 2023. "Cross-sectional uncertainty and stock market volatility: New evidence," Finance Research Letters, Elsevier, vol. 57(C).
    56. Gupta, Rangan & Modise, Mampho P., 2013. "Macroeconomic Variables and South African Stock Return Predictability," Economic Modelling, Elsevier, vol. 30(C), pages 612-622.
    57. Jaqueson K. Galimberti, 2020. "Forecasting GDP Growth from Outer Space," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 697-722, August.
    58. Campbell, John & Thompson, Samuel P., 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Scholarly Articles 2622619, Harvard University Department of Economics.
    59. Sousa, Ricardo M. & Vivian, Andrew & Wohar, Mark E., 2016. "Predicting asset returns in the BRICS: The role of macroeconomic and fundamental predictors," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 122-143.
    60. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    61. Edel Barnes & Gael Hardie‐Brown, 2006. "The Diversification Puzzle: Revisiting the Value Impact of Diversification for UK Firms," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(9‐10), pages 1508-1534, November.
    62. Ron Alquist & Lutz Kilian & Robert Vigfusson, 2011. "Forecasting the Price of Oil," Staff Working Papers 11-15, Bank of Canada.
    63. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    64. Enilov, Martin & Mensi, Walid & Stankov, Petar, 2023. "Does safe haven exist? Tail risks of commodity markets during COVID-19 pandemic," Journal of Commodity Markets, Elsevier, vol. 29(C).
    65. Funk, Christoph, 2018. "Forecasting the real price of oil - Time-variation and forecast combination," Energy Economics, Elsevier, vol. 76(C), pages 288-302.
    66. Zhu, Yinchu & Timmermann, Allan, 2022. "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, vol. 231(2), pages 329-347.
    67. 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.
    68. Rangan Gupta & Mampho P. Modise, 2010. "South African Stock Return Predictability in the Context of Data Mining: The Role of Financial Variables and International Stock Returns," Working Papers 201027, University of Pretoria, Department of Economics.
    69. Arcidiacono, Sally Giuseppe & Corrente, Salvatore & Greco, Salvatore, 2021. "Robust stochastic sorting with interacting criteria hierarchically structured," European Journal of Operational Research, Elsevier, vol. 292(2), pages 735-754.
    70. Jiali Fang & Ben Jacobsen & Yafeng Qin, 2014. "Predictability of the simple technical trading rules: An out‐of‐sample test," Review of Financial Economics, John Wiley & Sons, vol. 23(1), pages 30-45, January.
    71. José Afonso Faias & Tiago Castel-Branco, 2018. "Out-Of-Sample Stock Return Prediction Using Higher-Order Moments," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 1-27, September.
    72. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Wese Simen, Chardin, 2017. "International Tail Risk and World Fear," Hannover Economic Papers (HEP) dp-620, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    73. Byrne, Joseph P & Korobilis, Dimitris & Ribeiro, Pinho J, 2014. "On the Sources of Uncertainty in Exchange Rate Predictability," MPRA Paper 58956, University Library of Munich, Germany.
    74. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    75. Thomas Fullerton & Roberto Tinajero & Martha Barraza de Anda, 2006. "Short-Term Water Consumption Patterns in Ciudad Juárez, Mexico," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 34(4), pages 467-479, December.
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    81. Julliard, Christian, 2007. "Labor income risk and asset returns," LSE Research Online Documents on Economics 4811, London School of Economics and Political Science, LSE Library.
    82. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
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    88. Mr. Guy M Meredith, 2003. "Medium-Term Exchange Rate Forecasting: What Can We Expect?," IMF Working Papers 2003/021, International Monetary Fund.
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    Cited by:

    1. 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.
    2. Paulo Parente & Richard J. Smith, 2019. "Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," CeMMAP working papers CWP60/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Javed Iqbal & Robert Brooks & Don U.A. Galagedera, 2008. "Multivariate tests of asset pricing: Simulation evidence from an emerging market," Monash Econometrics and Business Statistics Working Papers 2/08, Monash University, Department of Econometrics and Business Statistics.
    4. Timothy Conley & Silvia Gonçalves & Christian Hansen, 2018. "Inference with Dependent Data in Accounting and Finance Applications," Journal of Accounting Research, Wiley Blackwell, vol. 56(4), pages 1139-1203, September.
    5. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
    6. 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.
    7. Jason Allen & Allan Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Staff Working Papers 08-18, Bank of Canada.
    8. Hill, Jonathan B. & Prokhorov, Artem, 2016. "GEL estimation for heavy-tailed GARCH models with robust empirical likelihood inference," Journal of Econometrics, Elsevier, vol. 190(1), pages 18-45.
    9. Davide La Vecchia & Alban Moor & O. Scaillet, 2020. "A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data," Swiss Finance Institute Research Paper Series 20-01, Swiss Finance Institute.
    10. Corradi, Valentina & Swanson, Norman R., 2007. "Evaluation of dynamic stochastic general equilibrium models based on distributional comparison of simulated and historical data," Journal of Econometrics, Elsevier, vol. 136(2), pages 699-723, February.
    11. 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.
    12. Zisimos Koustas & Jean-Francois Lamarche, 2009. "Instrumental variable estimation of a nonlinear Taylor rule," Working Papers 0909, Brock University, Department of Economics, revised Jul 2010.
    13. 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.
    14. Dovonon, Prosper & Gonçalves, Sílvia, 2017. "Bootstrapping the GMM overidentification test under first-order underidentification," Journal of Econometrics, Elsevier, vol. 201(1), pages 43-71.
    15. Valentina Corradi & Norman Swanson, 2004. "Predective Density and Conditional Confidence Interval Accuracy Tests," Departmental Working Papers 200423, Rutgers University, Department of Economics.
    16. Wei-Ming Lee & Chung-Ming Kuan, 2006. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 06-A009, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    17. Ghysels, Eric & Pereira, João Pedro, 2008. "Liquidity and conditional portfolio choice: A nonparametric investigation," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 679-699, September.
    18. Eric Ghysels & João Pereira, 2003. "On Portfolio Choice, Liquidity, and Short Selling: A Nonparametric Investigation," CIRANO Working Papers 2003s-27, CIRANO.
    19. Lutz Kilian & Atsushi Inoue, 2004. "Bagging Time Series Models," Econometric Society 2004 North American Summer Meetings 110, Econometric Society.
    20. Laurini, Márcio Poletti & Sanvicente, Antônio Zoratto & Monteiro, Rogério da Costa, 2011. "Generalized Tests of Investment Fund Performance," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    21. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification," Departmental Working Papers 200311, Rutgers University, Department of Economics.
    22. Al-Zoubi, Haitham A., 2019. "Bond and option prices with permanent shocks," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 272-290.
    23. Michael Creel & Dennis Kristensen, "undated". "Indirect Likelihood Inference," Working Papers 558, Barcelona School of Economics.
    24. Corradi, Valentina & Swanson, Norman R., 2005. "Bootstrap specification tests for diffusion processes," Journal of Econometrics, Elsevier, vol. 124(1), pages 117-148, January.
    25. Kilian, Lutz & Inoue, Atsushi, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers.
    26. Oliver Blaskowitz & Helmut Herwartz, 2008. "Testing directional forecast value in the presence of serial correlation," SFB 649 Discussion Papers SFB649DP2008-073, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    27. Härdle, Wolfgang & Horowitz, Joel L. & Kreiss, Jens-Peter, 2001. "Bootstrap methods for time series," SFB 373 Discussion Papers 2001,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    28. Seojeong Lee, 2013. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators," Discussion Papers 2013-09, School of Economics, The University of New South Wales.
    29. Francesco Bravo & Federico Crudu, 2012. "Efficient bootstrap with weakly dependent processes," Discussion Papers 12/08, Department of Economics, University of York.
    30. Yixiao Sun & Peter C.B. Phillips, 2008. "Optimal Bandwidth Choice for Interval Estimation in GMM Regression," Cowles Foundation Discussion Papers 1661, Cowles Foundation for Research in Economics, Yale University.
    31. Firmin Doko Tchatoka & Robert Garrard & Virginie Masson, 2017. "Testing for Stochastic Dominance in Social Networks," School of Economics and Public Policy Working Papers 2017-02, University of Adelaide, School of Economics and Public Policy.
    32. Rossini, Jacopo & Canale, Antonio, 2019. "Quantifying prediction uncertainty for functional-and-scalar to functional autoregressive models under shape constraints," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 221-231.
    33. Politis, D N, 2009. "Higher-Order Accurate, Positive Semi-definite Estimation of Large-Sample Covariance and Spectral Density Matrices," University of California at San Diego, Economics Working Paper Series qt66w826hz, Department of Economics, UC San Diego.
    34. Amilcar Velez, 2023. "The Local Projection Residual Bootstrap for AR(1) Models," Papers 2309.01889, arXiv.org, revised Feb 2024.
    35. Seojeong Lee, 2018. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Empirical Likelihood Estimators," Papers 1806.00953, arXiv.org, revised Jun 2018.
    36. Paulo M.D.C. Parente & Richard J. Smith, 2018. "Generalised Empirical Likelihood Kernel Block Bootstrapping," Working Papers REM 2018/55, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    37. Diana N. Weymark & Mototsugu Shintani, 2004. "Measuring Inflation Pressure and Monetary Policy Response: A General Approach Applied to US Data 1966 - 2001," Vanderbilt University Department of Economics Working Papers 0424, Vanderbilt University Department of Economics.
    38. 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.
    39. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    40. Diana N. Weymark & Mototsugu Shintani, 2006. "Quantifying Inflation Pressure and Monetary Policy Response in the United States," Levine's Bibliography 321307000000000321, UCLA Department of Economics.
    41. Turnbull, Christopher & Sun, Sizhong & Anwar, Sajid, 2016. "Trade liberalisation, inward FDI and productivity within Australia’s manufacturing sector," Economic Analysis and Policy, Elsevier, vol. 50(C), pages 41-51.

  36. Peter Christoffersen & Jinyong Hahn & Atsushi Inoue, 2001. "Testing and Comparing Value-at-Risk Measures," CIRANO Working Papers 2001s-03, CIRANO.

    Cited by:

    1. Marmer, Vadim & Otsu, Taisuke, 2008. "Optimal Comparison of Misspecified Moment Restriction Models under a Chosen Measure of Fit," Microeconomics.ca working papers vadim_marmer-2008-13, Vancouver School of Economics, revised 25 Jul 2011.
    2. 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.
    3. Giacomini, Raffaella & Komunjer, Ivana, 2002. "Evaluation and Combination of Conditional Quantile Forecasts," University of California at San Diego, Economics Working Paper Series qt4n99t4wz, Department of Economics, UC San Diego.
    4. 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.
    5. Kimera Naradh & Retius Chifurira & Knowledge Chinhamu, 2022. "Analysis of stock exchange risk and currency in South African Financial Markets using stable parameter estimation," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 11(1), pages 120-131, January.
    6. Wong, Woon K., 2010. "Backtesting value-at-risk based on tail losses," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 526-538, June.
    7. Kerkhof, F.L.J. & Melenberg, B. & Schumacher, J.M., 2003. "Testing Expected Shortfall Models for Derivative Positions," Discussion Paper 2003-24, Tilburg University, Center for Economic Research.
    8. David E. Allen & Mohammad A. Ashraf & Michael McAleer & Robert J. Powell & Abhay K. Singh, 2013. "Financial Dependence Analysis: Applications of Vine Copulae," Tinbergen Institute Discussion Papers 13-022/III, Tinbergen Institute.
    9. Ngozi G. Emenogu & Monday Osagie Adenomon & Nwaze Obini Nweze, 2020. "On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    10. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Risk Measurement and Risk Modelling using Applications of Vine Copulas," Tinbergen Institute Discussion Papers 14-054/III, Tinbergen Institute.
    11. 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.
    12. Wessam M. T. Abouarghoub & Iris Biefang-Frisancho Mariscal, 2011. "Measuring level of risk exposure in tanker Shipping freight markets," International Journal of Business and Social Research, LAR Center Press, vol. 1(1), pages 20-44, December.
    13. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," CIRJE F-Series CIRJE-F-430, CIRJE, Faculty of Economics, University of Tokyo.
    14. Abdul Hakim, 2009. "Forcasting portofolio value-at-risk for international stocks, bonds, and foreign exchange emerging market evidence," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 1(1), pages 13-26, April.
    15. Hasna Fadhila & Nora Amelda Rizal, 2013. "Analysis of Risk using Value at Risk (VaR) After Crisis in 2008 Study in Stocks of Bank Mandiri, Bank BRI and Bank BNI in 2009-2011," Information Management and Business Review, AMH International, vol. 5(8), pages 394-400.
    16. Roberta Fiori & Simonetta Iannotti, 2006. "Scenario Based Principal Component Value-at-Risk: an Application to Italian Banks' Interest Rate Risk Exposure," Temi di discussione (Economic working papers) 602, Bank of Italy, Economic Research and International Relations Area.
    17. Chen, Xiaohong & Hong, Han & Shum, Matthew, 2007. "Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models," Journal of Econometrics, Elsevier, vol. 141(1), pages 109-140, November.
    18. Zhijie Xiao, 2009. "Quantile Cointegrating Regression," Boston College Working Papers in Economics 708, Boston College Department of Economics.
    19. Chesney, Marc & Reshetar, Ganna & Karaman, Mustafa, 2011. "The impact of terrorism on financial markets: An empirical study," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 253-267, February.
    20. Sueishi, Naoya, 2013. "Identification problem of the exponential tilting estimator under misspecification," Economics Letters, Elsevier, vol. 118(3), pages 509-511.
    21. Valentina Corradi & Norman Swanson, 2004. "Predective Density and Conditional Confidence Interval Accuracy Tests," Departmental Working Papers 200423, Rutgers University, Department of Economics.
    22. Minglian Lin & Indranil SenGupta & William Wilson, 2023. "Estimation of VaR with jump process: application in corn and soybean markets," Papers 2311.00832, arXiv.org, revised Dec 2023.
    23. Wagner Piazza Gaglianone & Luiz Renato Lima & Oliver Linton & Daniel R. Smith, 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 150-160, January.
    24. 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.
    25. Peter Christoffersen & Denis Pelletier, 2003. "Backtesting Value-at-Risk: A Duration-Based Approach," CIRANO Working Papers 2003s-05, CIRANO.
    26. 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.
    27. Jakub Micha'nk'ow & {L}ukasz Kwiatkowski & Janusz Morajda, 2023. "Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecasting," Papers 2310.01063, arXiv.org.
    28. Vijverberg, Chu-Ping C. & Vijverberg, Wim P.M. & Taşpınar, Süleyman, 2016. "Linking Tukey’s legacy to financial risk measurement," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 595-615.
    29. Wessam Abouarghoub & Iris Biefang-Frisancho Mariscal, 2013. "Measuring the level of risk exposure in tanker shipping freight markets," Working Papers 20131313, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    30. Metiu, N., 2011. "Financial contagion in developed sovereign bond markets," Research Memorandum 004, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    31. Krzysztof Echaust & Małgorzata Just, 2021. "Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic," Energies, MDPI, vol. 14(14), pages 1-21, July.
    32. Schmidt, Ulrich, 2003. "The axiomatic basis of risk-value models," European Journal of Operational Research, Elsevier, vol. 145(1), pages 216-220, February.
    33. 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.
    34. Elena Andreou & Eric Ghysels, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," CIRANO Working Papers 2004s-25, CIRANO.
    35. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    36. 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.
    37. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
    38. Li, Leon, 2017. "Testing and comparing the performance of dynamic variance and correlation models in value-at-risk estimation," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 116-135.
    39. Nikola Radivojevic & Milena Cvjetkovic & Saša Stepanov, 2016. "The new hybrid value at risk approach based on the extreme value theory," Estudios de Economia, University of Chile, Department of Economics, vol. 43(1 Year 20), pages 29-52, June.
    40. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    41. Kerkhof, F.L.J. & Melenberg, B., 2002. "Backtesting for Risk-Based Regulatory Capital," Other publications TiSEM 2363cf81-9720-41f2-913c-f, Tilburg University, School of Economics and Management.
    42. L. Kourouma & Denis Dupré & G. Sanfilippo & O. Taramasco, 2011. "Extreme Value at Risk and Expected Shortfall during Financial Crisis," Post-Print halshs-00658495, HAL.
    43. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    44. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
    45. Fabozzi Frank J. & Stoyanov Stoyan V. & Rachev Svetlozar T., 2013. "Computational aspects of portfolio risk estimation in volatile markets: a survey," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 103-120, February.
    46. 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 University London.
    47. Carol Alexander & Jose Maria Sarabia, 2010. "Endogenizing Model Risk to Quantile Estimates," ICMA Centre Discussion Papers in Finance icma-dp2010-07, Henley Business School, University of Reading.
    48. 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.
    49. 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.
    50. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    51. Kerkhof, Jeroen & Melenberg, Bertrand, 2004. "Backtesting for risk-based regulatory capital," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 1845-1865, August.
    52. 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.
    53. Pinto, Cristian F. & Acuña, Andres A., 2011. "Consistencia de la evaluación de desempeño de inversiones financieras: Pruebas de dominación estocástica versus índices media-varianza [Consistency in the evaluation of financial investment perform," MPRA Paper 31301, University Library of Munich, Germany.
    54. David Feldman & Xin Xu, 2018. "Equilibrium-based volatility models of the market portfolio rate of return (peacock tails or stotting gazelles)," Annals of Operations Research, Springer, vol. 262(2), pages 493-518, March.
    55. Kraft, Holger & Schmidt, Alexander, 2013. "Systemic risk in the financial sector: What can se learn from option markets?," SAFE Working Paper Series 25, Leibniz Institute for Financial Research SAFE.
    56. Hamidreza Arian & Hossein Poorvasei & Azin Sharifi & Shiva Zamani, 2020. "The Uncertain Shape of Grey Swans: Extreme Value Theory with Uncertain Threshold," Papers 2011.06693, arXiv.org.
    57. Tomáš Jeøábek, 2020. "The Efficiency of GARCH Models in Realizing Value at Risk Estimates," ACTA VSFS, University of Finance and Administration, vol. 14(1), pages 32-50.
    58. Lehar, Alfred & Scheicher, Martin & Schittenkopf, Christian, 2002. "GARCH vs. stochastic volatility: Option pricing and risk management," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 323-345, March.
    59. Siva Kiran GUPTHA. K & Prabhakar RAO. R, 2019. "GARCH based VaR estimation: An empirical evidence from BRICS stock markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(621), W), pages 201-218, Winter.
    60. Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023. "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, vol. 5(1), pages 29-56, March.
    61. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    62. Metiu, Norbert, 2012. "Sovereign risk contagion in the Eurozone," Economics Letters, Elsevier, vol. 117(1), pages 35-38.
    63. Köksal, Bülent & Orhan, Mehmet, 2012. "Market risk of developed and developing countries during the global financial crisis," MPRA Paper 37523, University Library of Munich, Germany.
    64. Mirjana Miletić & Siniša Miletić, 2016. "Performance of VaR in Developed and CEE Countries during the Global Financial Crisis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 54-75, March.
    65. Kilic, Ekrem, 2006. "Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio," MPRA Paper 5610, University Library of Munich, Germany.
    66. 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.
    67. Isengildina-Massa, Olga & Sharp, Julia L., 2013. "Interval Forecast Comparison," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150791, Agricultural and Applied Economics Association.
    68. Cerović Julija & Lipovina-Božović Milena & Vujošević Saša, 2015. "A Comparative Analysis of Value at Risk Measurement on Emerging Stock Markets: Case of Montenegro," Business Systems Research, Sciendo, vol. 6(1), pages 36-55, March.

  37. Francis X. Diebold & Atsushi Inoue, 2000. "Long Memory and Regime Switching," NBER Technical Working Papers 0264, National Bureau of Economic Research, Inc.

    Cited by:

    1. Eric Hillebrand & Gunther Schnabl & Yasemin Ulu, 2006. "Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility," CESifo Working Paper Series 1766, CESifo.
    2. Chen, Xiaohong & Hansen, Lars Peter & Carrasco, Marine, 2008. "Nonlinearity and Temporal Dependence," Working Papers 48, Yale University, Department of Economics.
    3. J. Cuñado & L. Gil-Alana & F. Gracia, 2009. "US stock market volatility persistence: evidence before and after the burst of the IT bubble," Review of Quantitative Finance and Accounting, Springer, vol. 33(3), pages 233-252, October.
    4. Abi Morshed, Alaa & Andreou, E. & Boldea, Otilia, 2016. "Structural Break Tests Robust to Regression Misspecification," Discussion Paper 2016-019, Tilburg University, Center for Economic Research.
    5. Chin Wen Cheong, 2010. "Estimating the Hurst parameter in financial time series via heuristic approaches," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 201-214.
    6. Gürtler, Marc & Rauh, Ronald, 2012. "Challenging traditional risk models by a non-stationary approach with nonparametric heteroscedasticity," Working Papers IF41V1, Technische Universität Braunschweig, Institute of Finance.
    7. Morten Ø. Nielsen & Per Houmann Frederiksen, 2005. "Finite Sample Comparison Of Parametric, Semiparametric, And Wavelet Estimators Of Fractional Integration," Working Paper 1189, Economics Department, Queen's University.
    8. Haldrup, Niels & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2010. "A vector autoregressive model for electricity prices subject to long memory and regime switching," Energy Economics, Elsevier, vol. 32(5), pages 1044-1058, September.
    9. Geoffrey Ngene & Charles Lambert & Ali Darrat, 2015. "Testing Long Memory in the Presence of Structural Breaks: An Application to Regional and National Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 50(4), pages 465-483, May.
    10. Monge, Manuel & Gil-Alana, Luis A. & Pérez de Gracia, Fernando, 2017. "U.S. shale oil production and WTI prices behaviour," Energy, Elsevier, vol. 141(C), pages 12-19.
    11. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    12. Carlos Barros & Luis Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 103, CEsA - Centre for African and Development Studies.
    13. Luis A Gil-Alana & Christophe André & Rangan Gupta & Tsangyao Chang & Omid Ranjbar, 2015. "The Feldstein-Horioka Puzzle in South Africa: A Fractional Cointegration Approach," Working Papers 201501, University of Pretoria, Department of Economics.
    14. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    15. Ngene, Geoffrey & Tah, Kenneth A. & Darrat, Ali F., 2017. "Long memory or structural breaks: Some evidence for African stock markets," Review of Financial Economics, Elsevier, vol. 34(C), pages 61-73.
    16. 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.
    17. Maria Caporale, Guglielmo & A. Gil-Alana, Luis, 2011. "Multi-Factor Gegenbauer Processes and European Inflation Rates," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 26, pages 386-409.
    18. Dark Jonathan Graeme, 2010. "Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-50, March.
    19. Claudio Morana & Fabio Cesare Bagliano, 2007. "Inflation and monetary dynamics in the USA: a quantity-theory approach," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 229-244.
    20. Mstislav Elagin, 2008. "Locally adaptive estimation methods with application to univariate time series," Papers 0812.0449, arXiv.org.
    21. Mccloskey, Adam & Perron, Pierre, 2013. "Memory Parameter Estimation In The Presence Of Level Shifts And Deterministic Trends," Econometric Theory, Cambridge University Press, vol. 29(6), pages 1196-1237, December.
    22. Luis Alberiko Gil-Alaña & Olanrewaju L. Shittu & OlaOluwa S. Yaya, 2011. "Long memory, strcutural breaks and mean shifts in the inflation rates in Nigeria," NCID Working Papers 04/2011, Navarra Center for International Development, University of Navarra.
    23. Luis A. Gil-Alana & Juncal Cunado & Rangan Gupta, 2015. "Persistence, Mean-Reversion and Non-Linearities in Infant Mortality Rates," Working Papers 201574, University of Pretoria, Department of Economics.
    24. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.
    25. João Ricardo Faria & Juan Carlos Cuestas & Luis Gil-Alana & Estefania Mourelle, 2021. "Self-employment by gender in the EU: convergence and clusters," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(3), pages 717-741, August.
    26. Gürtler, Marc & Rauh, Ronald, 2009. "Shortcomings of a parametric VaR approach and nonparametric improvements based on a non-stationary return series model," Working Papers IF32V2, Technische Universität Braunschweig, Institute of Finance.
    27. Smith, Aaron, 2005. "Level Shifts and the Illusion of Long Memory in Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 321-335, July.
    28. Uwe Hassler & Paulo M.M. Rodrigues & Antonio Rubia, 2016. "Quantile Regression for Long Memory Testing: A Case of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 693-724.
    29. Claudio Morana & Giacomo Sbrana, 2017. "Temperature Anomalies, Radiative Forcing and ENSO," Working Papers 2017.09, Fondazione Eni Enrico Mattei.
    30. Guillaume Chevillon & Alain Hecq & Sébastien Laurent, 2018. "Generating Univariate Fractional Integration within a Large VAR(1)," AMSE Working Papers 1844, Aix-Marseille School of Economics, France.
    31. Chen, Ying & Han, Qian & Niu, Linlin, 2018. "Forecasting the Term Structure of Option Implied Volatility: The Power of an Adaptive Method," IRTG 1792 Discussion Papers 2018-046, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    32. Shaher Al-Gounmeein Remal & Ismail Mohd Tahir, 2021. "Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 29-54, March.
    33. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    34. Berna Kirkulak Uludag & Zorikto Lkhamazhapov, 2014. "Long memory and structural breaks in the returns and volatility of gold: evidence from Turkey," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3777-3787, November.
    35. 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.
    36. Christophe Andre & Luis A. Gil-Alana & Rangan Gupta, 2013. "Testing for Persistence in Housing Price-to-Income and Price-to-Rent Ratios in 16 OECD Countries," Working Papers 201321, University of Pretoria, Department of Economics.
    37. Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
    38. Silvestro Di Sanzo, 2007. "Forecasting Time Series with Long Memory and Level Shifts, A Bayesian Approach," Working Papers 2007_03, Department of Economics, University of Venice "Ca' Foscari".
    39. 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.
    40. Gbaguidi DAVID, 2011. "Expectations Impact On The Effectiveness Of The Inflation-Real Activity Trade-Off," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 2(2), pages 141-181.
    41. Luis Gil-Alana & Antonio Moreno, 2007. "Uncovering the U.S. Term Premium: An Alternative Route," Faculty Working Papers 12/07, School of Economics and Business Administration, University of Navarra.
    42. Lux, Thomas & Kaizoji, Taisei, 2006. "Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching," Economics Working Papers 2006-13, Christian-Albrechts-University of Kiel, Department of Economics.
    43. Robinson Kruse & Philipp Sibbertsen, 2010. "Long memory and changing persistence," CREATES Research Papers 2010-42, Department of Economics and Business Economics, Aarhus University.
    44. Andersen, Torben G. & Varneskov, Rasmus T., 2022. "Testing for parameter instability and structural change in persistent predictive regressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 361-386.
    45. Lanouar Charfeddine & Dominique Guegan, 2012. "Breaks or long memory behavior: An empirical investigation," PSE-Ecole d'économie de Paris (Postprint) hal-01314013, HAL.
    46. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    47. McMillan, David G. & Wohar, Mark E., 2010. "Persistence and time-varying coefficients," Economics Letters, Elsevier, vol. 108(1), pages 85-88, July.
    48. Yanlin Shi & Lingbing Feng & Tong Fu, 2020. "Markov Regime-Switching in-Mean Model with Tempered Stable Distribution," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1275-1299, April.
    49. Cuestas, Juan C. & Gil-Alana, Luís A., 2009. "Further evidence on the PPP analysis of the Australian dollar: Non-linearities, fractional integration and structural changes," Economic Modelling, Elsevier, vol. 26(6), pages 1184-1192, November.
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    184. Eric Hillebrand & Marcelo C. Medeiros, 2012. "Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models," CREATES Research Papers 2012-30, Department of Economics and Business Economics, Aarhus University.
    185. Achour Maha & Trabelsi Abdelwahed, 2011. "Markov Switching and State-Space Approaches for Investigating the Link between Egyptian Inflation Level and Uncertainty," Review of Middle East Economics and Finance, De Gruyter, vol. 6(3), pages 46-62, February.
    186. Guglielmo Maria Caporale & Juncal Cunado & Luis A. Gil-Alana, 2007. "Deterministic versus Stochastic Seasonal Fractional Integration and Structural Breaks," CESifo Working Paper Series 1989, CESifo.
    187. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2006. "Purchasing Power Parity: The Irish Experience Re-visited," Trinity Economics Papers tep200615, Trinity College Dublin, Department of Economics.
    188. Eric Hillebrand, 2003. "Overlaying Time Scales and Persistence Estimation in GARCH(1,1) Models," Econometrics 0301003, University Library of Munich, Germany.
    189. Juan Carlos Cuestas & Luis A. Gil-Alana & Paulo José Regis, 2015. "The Sustainability of European External Debt: What have We Learned?," Review of International Economics, Wiley Blackwell, vol. 23(3), pages 445-468, August.
    190. Fillol, Jerome, 2007. "Estimating long memory: Scaling function vs Andrews and Guggenberger GPH," Economics Letters, Elsevier, vol. 95(2), pages 309-314, May.
    191. 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).
    192. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    193. Kuswanto, Heri & Sibbertsen, Philipp, 2009. "Testing for Long Memory Against ESTAR Nonlinearities," Hannover Economic Papers (HEP) dp-427, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    194. Chen, Shengming & Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "The Russia–Ukraine war and energy market volatility: A novel application of the volatility ratio in the context of natural gas," Resources Policy, Elsevier, vol. 85(PA).
    195. Barros, Carlos Pestana & Gil-Alana, Luis A. & Payne, James E., 2011. "An analysis of oil production by OPEC countries: Persistence, breaks, and outliers," Energy Policy, Elsevier, vol. 39(1), pages 442-453, January.
    196. Young Wook Han, 2010. "The Effects of US Macroeconomic Surprises on the Intraday Movements of Foreign Exchange Rates: Cases of USD-EUR and USD-JPY Exchange Rates," International Economic Journal, Taylor & Francis Journals, vol. 24(3), pages 375-396.
    197. Heejoon Han & Shen Zhang, 2012. "Non‐stationary non‐parametric volatility model," Econometrics Journal, Royal Economic Society, vol. 15(2), pages 204-225, June.
    198. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    199. Tommaso Proietti, 2014. "Exponential Smoothing, Long Memory and Volatility Prediction," CEIS Research Paper 319, Tor Vergata University, CEIS, revised 30 Jul 2014.
    200. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    201. Aloui, Chaker & Hamida, Hela ben, 2014. "Modelling and forecasting value at risk and expected shortfall for GCC stock markets: Do long memory, structural breaks, asymmetry, and fat-tails matter?," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 349-380.
    202. Dennis Alvaro & Ángel Guillén & Gabriel Rodríguez, 2017. "Modelling the volatility of commodities prices using a stochastic volatility model with random level shifts," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 153(1), pages 71-103, February.
    203. Gilles Dufrénot & Valérie Mignon & Théo Naccache, 2009. "The slow convergence of per capita income between the developing countries: “growth resistance” and sometimes “growth tragedy”," Discussion Papers 09/03, University of Nottingham, CREDIT.
    204. Abanto-Valle, Carlos A. & Rodríguez, Gabriel & Garrafa-Aragón, Hernán B., 2021. "Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 272-286.
    205. Luis A. Gil-Alana, 2015. "Linear and segmented trends in sea surface temperature data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1531-1546, July.
    206. Gil-Alana, Luis A., 2002. "Structural breaks and fractional integration in the US output and unemployment rate," Economics Letters, Elsevier, vol. 77(1), pages 79-84, September.
    207. Attfield, Cliff & Temple, Jonathan R.W., 2010. "Balanced growth and the great ratios: New evidence for the US and UK," Journal of Macroeconomics, Elsevier, vol. 32(4), pages 937-956, December.
    208. Alessandro Casini, 2021. "Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models," Papers 2103.02981, arXiv.org.
    209. Dietmar Bauer & Alex Maynard, 2010. "Persistence-robust Granger causality testing," Working Papers 1011, University of Guelph, Department of Economics and Finance.
    210. Laurent-Emmanuel Calvet & Adlai J. Fisher & Samuel B. Thompson, 2006. "Volatility Comovement: a multifrequency approach," Post-Print hal-00459667, HAL.
    211. Dominique Guegan & Philippe de Peretti, 2011. "Tests of structural changes in conditional distributions with unknown changepoints," Post-Print halshs-00611932, HAL.
    212. Bond, Derek & Harrison, Michael J & Hession, Niall & O’Brien, Edward J., 2006. "Some Empirical Observations on the Forward Exchange Rate Anomaly," Research Technical Papers 3/RT/06, Central Bank of Ireland.
    213. Guglielmo Maria Caporale & Hector Carcel & Luis A. Gil-Alana, 2015. "Modelling African inflation rates: nonlinear deterministic terms and long-range dependence," Applied Economics Letters, Taylor & Francis Journals, vol. 22(5), pages 421-424, March.
    214. Beylunioğlu Fuat C. & Yazgan M. Ege & Stengos Thanasis, 2018. "Regime switching with structural breaks in output convergence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(3), pages 1-17, June.
    215. Ye Li & Pierre Perron & Jiawen Xu, 2017. "Modelling exchange rate volatility with random level shifts," Applied Economics, Taylor & Francis Journals, vol. 49(26), pages 2579-2589, June.
    216. Surgailis, Donatas & Teyssière, Gilles & Vaiciulis, Marijus, 2008. "The increment ratio statistic," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 510-541, March.
    217. Chevillon, G. & Hecq, A.W. & Laurent, S.F.J.A., 2015. "Long memory through marginalization of large systems and hidden cross-section dependence," Research Memorandum 014, Maastricht University, Graduate School of Business and Economics (GSBE).
    218. Javier Haulde & Morten Ørregaard Nielsen, 2022. "Fractional integration and cointegration," CREATES Research Papers 2022-02, Department of Economics and Business Economics, Aarhus University.
    219. Juncal Cunado & Luis Alberiko Gil-Alana & Fernando Perez de Gracia, 2008. "New Evidence on US Current Account Sustainability," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 7(1), pages 1-21, April.
    220. Davidson James E. H. & Peel David A & Byers J. David, 2006. "Support for Governments and Leaders: Fractional Cointegration Analysis of Poll Evidence from the UK, 1960-2004," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(1), pages 1-23, March.
    221. Mulligan, Robert F., 2004. "Fractal analysis of highly volatile markets: an application to technology equities," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(1), pages 155-179, February.
    222. Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, vol. 117(2), pages 207-244, December.
    223. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    224. Veiga, Helena, 2006. "Are feedback factors important in modelling financial data?," DES - Working Papers. Statistics and Econometrics. WS ws060101, Universidad Carlos III de Madrid. Departamento de Estadística.
    225. Manmohan S. Kumar & Tatsuyoshi Okimoto, 2007. "Dynamics of Persistence in International Inflation Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1457-1479, September.
    226. Luis A. Gil-Alana & Fernando Perez de Gracia & Rangan Gupta, 2015. "Modeling Persistence of Carbon Emission Allowance Prices," Working Papers 201515, University of Pretoria, Department of Economics.
    227. Ivelina Pavlova & Jang Hyung Cho & A.M. Parhizgari & William G. Hardin, 2014. "Long memory in REIT volatility and changes in the unconditional mean: a modified FIGARCH approach," Journal of Property Research, Taylor & Francis Journals, vol. 31(4), pages 315-332, December.
    228. Iacone, Fabrizio & Robinson, Peter M., 2004. "Cointegration in fractional systems with deterministic trends," LSE Research Online Documents on Economics 2232, London School of Economics and Political Science, LSE Library.
    229. Luis A. Gil-Alana & Rangan Gupta & Olanrewaju I. Shittu & OlaOluwa S. Yaya, 2016. "Market Efficiency of Baltic Stock Markets: A Fractional Integration Approach," Working Papers 201617, University of Pretoria, Department of Economics.
    230. Katarzyna Lasak & Carlos Velasco, 2014. "Fractional Cointegration Rank Estimation," Tinbergen Institute Discussion Papers 14-021/III, Tinbergen Institute.
    231. Mulligan, Robert F. & Lombardo, Gary A., 2004. "Maritime businesses: volatile stock prices and market valuation inefficiencies," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(2), pages 321-336, May.
    232. Lutz, Benjamin Johannes & Pigorsch, Uta & Rotfuß, Waldemar, 2013. "Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals," ZEW Discussion Papers 13-001, ZEW - Leibniz Centre for European Economic Research.
    233. Susanne M. Schennach, 2013. "Long memory via networking," CeMMAP working papers CWP13/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    234. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    235. Luis A. Gil-Alana & Sakiru Adebola Solarin & Rangan Gupta, 2021. "Productivity and GDP: International Evidence of Persistence and Trends Over 130 Years of Data," Working Papers 202170, University of Pretoria, Department of Economics.
    236. Koubaa, Yosra & Slim, Skander, 2019. "The relationship between trading activity and stock market volatility: Does the volume threshold matter?," Economic Modelling, Elsevier, vol. 82(C), pages 168-184.
    237. Adam Goliński & João Madeira & Dooruj Rambaccussing, 2015. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model of Stock Prices," Dundee Discussion Papers in Economics 284, Economic Studies, University of Dundee.
    238. Marcel Scharth & Marcelo Cunha Medeiros, 2006. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," Textos para discussão 532, Department of Economics PUC-Rio (Brazil).
    239. Fabrizio Iacone, 2009. "A Semiparametric Analysis of the Term Structure of the US Interest Rates," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(4), pages 475-490, August.
    240. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.
    241. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2009. "Long Memory in US Real Output per Capita," Discussion Papers of DIW Berlin 891, DIW Berlin, German Institute for Economic Research.
    242. Golinski, Adam & Madeira, Joao & Rambaccussing, Dooruj, 2014. "Fractional Integration of the Price-Dividend Ratio in a Present-Value Model," MPRA Paper 58554, University Library of Munich, Germany.
    243. Arnaud Dufays & Maciej Augustyniak & Luc Bauwens, 2016. "A new approach to volatility modeling: the High-Dimensional Markov model," Cahiers de recherche 1609, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    244. Zhang, Yue-Jun & Zhang, Han, 2023. "Volatility forecasting of crude oil futures market: Which structural change-based HAR models have better performance?," International Review of Financial Analysis, Elsevier, vol. 85(C).
    245. Robinson Kruse & Christoph Wegener, 2019. "Explosive behaviour and long memory with an application to European bond yield spreads," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 139-153, February.
    246. Cătălin Stărică & Clive Granger, 2005. "Nonstationarities in Stock Returns," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 503-522, August.
    247. Christophe Andre & Mehmet Balcilar & Tsangyao Chang & Luis Alberiko Gil-Alana & Rangan Gupta, 2018. "Current account sustainability in G7 and BRICS: Evidence from a long-memory model with structural breaks," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 27(6), pages 638-654, August.
    248. Les Oxley & Chris Price & William Rea & Marco Reale, 2008. "A New Procedure to Test for H Self-Similarity," Working Papers in Economics 08/16, University of Canterbury, Department of Economics and Finance.
    249. Stoyan V. Stoyanov & Yong Shin Kim & Svetlozar T. Rachev & Frank J. Fabozzi, 2017. "Option pricing for Informed Traders," Papers 1711.09445, arXiv.org.
    250. Ioanna-Yvonni Tsaknaki & Fabrizio Lillo & Piero Mazzarisi, 2023. "Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods," Papers 2307.02375, arXiv.org.
    251. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).
    252. Marinko Skare & Luis A. Gil-Alana & Gloria Claudio-Quiroga & Romina Pržiklas Družeta, 2021. "Income inequality in China 1952–2017: persistence and main determinants," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 863-888, December.
    253. Solarin, Sakiru Adebola & Gil-Alana, Luis A. & Lafuente, Carmen, 2020. "An investigation of long range reliance on shale oil and shale gas production in the U.S. market," Energy, Elsevier, vol. 195(C).
    254. Dolado, Juan J & Rachinger, Heiko & Velasco, Carlos, 2020. "LM tests for joint breaks in the dynamics and level of a long-memory time series," CEPR Discussion Papers 15435, C.E.P.R. Discussion Papers.
    255. Vygintas Gontis, 2023. "Discrete $q$-exponential limit order cancellation time distribution," Papers 2306.00093, arXiv.org, revised Oct 2023.
    256. Richard T. Baille & Claudio Morana, 2009. "Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach," ICER Working Papers - Applied Mathematics Series 06-2009, ICER - International Centre for Economic Research.
    257. Georgios P. Kouretas & Mark E. Wohar, 2012. "The dynamics of inflation: a study of a large number of countries," Applied Economics, Taylor & Francis Journals, vol. 44(16), pages 2001-2026, June.
    258. Diebold, F.X. & Kilian, L. & Nerlove, Marc, 2006. "Time Series Analysis," Working Papers 28556, University of Maryland, Department of Agricultural and Resource Economics.
    259. Pierre Perron & Wendong Shi, 2014. "Temporal Aggregation, Bandwidth Selection and Long Memory for Volatility Models," Boston University - Department of Economics - Working Papers Series wp2014-009, Boston University - Department of Economics.
    260. Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
    261. Marco R Barassi & Dayong Zhang, 2009. "Fractional Integration and Cointegration: Testing the Term Structure of Interest Rates," Discussion Papers 09-17, Department of Economics, University of Birmingham.
    262. Leipus, Remigijus & Viano, Marie-Claude, 2003. "Long memory and stochastic trend," Statistics & Probability Letters, Elsevier, vol. 61(2), pages 177-190, January.
    263. Sakiru Adebola Solarin & Luis A. Gil-Alana & Maria Jesus Gonzalez-Blanch, 2021. "Fractional persistence in income poverty in Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 563-581, June.
    264. Luis A. Gil-Alana & Rolando Pelaez, 2008. "The Persistence of Earnings per Share," Faculty Working Papers 08/08, School of Economics and Business Administration, University of Navarra.
    265. Goodness C. Aye & Luis A. Gil-Alana & Rangan Gupta & Mark Wohar, 2016. "The Efficiency of the Art Market: Evidence from Variance Ratio Tests, Linear and Nonlinear Fractional Integration Approaches," Working Papers 201610, University of Pretoria, Department of Economics.
    266. Frank S. Nielsen, 2008. "Local polynomial Whittle estimation covering non-stationary fractional processes," CREATES Research Papers 2008-28, Department of Economics and Business Economics, Aarhus University.
    267. Haldrup, Niels & Vera Valdés, J. Eduardo, 2017. "Long memory, fractional integration, and cross-sectional aggregation," Journal of Econometrics, Elsevier, vol. 199(1), pages 1-11.
    268. Gilles Dufrénot & Valérie Mignon & Théo Naccache, 2012. "Testing Catching-Up Between The Developing Countries: “Growth Resistance” And Sometimes “Growth Tragedy”," Bulletin of Economic Research, Wiley Blackwell, vol. 64(4), pages 470-508, October.
    269. Le, Van & Zurbruegg, Ralf, 2010. "The role of trading volume in volatility forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 533-555, December.
    270. Philipp Sibbertsen, 2004. "Long memory versus structural breaks: An overview," Statistical Papers, Springer, vol. 45(4), pages 465-515, October.
    271. Quinton Morris & Gary Van Vuuren & Paul Styger, 2009. "Further Evidence Of Long Memory In The South African Stock Market," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 81-101, March.
    272. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2018. "Persistence and Cyclical Dynamics of US and UK House Prices: Evidence from Over 150 Years of Data," Working Papers 201838, University of Pretoria, Department of Economics.
    273. Li, Zhicheng & Chen, Xinyun & Xing, Haipeng, 2023. "A multifactor regime-switching model for inter-trade durations in the high-frequency limit order market," Economic Modelling, Elsevier, vol. 118(C).
    274. David G. McMillan, 2010. "Level‐shifts and non‐linearity in US financial ratios," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 9(2), pages 189-207, May.
    275. Willert, Juliane, 2010. "Mean Shift detection under long-range dependencies with ART," Hannover Economic Papers (HEP) dp-437, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    276. Busch, Marie & Sibbertsen, Philipp, 2018. "An Overview of Modified Semiparametric Memory Estimation Methods," Hannover Economic Papers (HEP) dp-628, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    277. Ying Chen & Bo Li, 2011. "Forecasting Yield Curves in an Adaptive Framework," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 3(4), pages 237-259, December.
    278. Zeynel Abidin Ozdemir & Mehmet Balcilar & Aysit Tansel, 2012. "Are Labor Force Participation Rates Really Non-Stationary? Evidence from Three OECD Countries," ERC Working Papers 1206, ERC - Economic Research Center, Middle East Technical University, revised Aug 2012.
    279. Ghysels, Eric & Sohn, Bumjean, 2009. "Which power variation predicts volatility well?," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 686-700, September.
    280. Cizek, P. & Haerdle, W. & Spokoiny, V., 2007. "Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models," Discussion Paper 2007-35, Tilburg University, Center for Economic Research.
    281. Ben-zhang Yang & Xinjiang He & Nan-jing Huang, 2019. "Equilibrium price and optimal insider trading strategy under stochastic liquidity with long memory," Papers 1901.00345, arXiv.org, revised Jan 2019.
    282. Barros, Carlos P. & Gil-Alana, Luis A. & Wanke, Peter, 2016. "Energy production in Brazil: Empirical facts based on persistence, seasonality and breaks," Energy Economics, Elsevier, vol. 54(C), pages 88-95.
    283. C.S. Bos & S.J. Koopman & M. Ooms, 2007. "Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks," Tinbergen Institute Discussion Papers 07-099/4, Tinbergen Institute.
    284. Kai Wenger & Christian Leschinski & Philipp Sibbertsen, 2019. "Change-in-mean tests in long-memory time series: a review of recent developments," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(2), pages 237-256, June.
    285. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "How do OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process," Energy Economics, Elsevier, vol. 42(C), pages 343-354.
    286. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Kiel Working Papers 1426, Kiel Institute for the World Economy (IfW Kiel).
    287. Wenger, Kai & Leschinski, Christian & Sibbertsen, Philipp, 2018. "A simple test on structural change in long-memory time series," Economics Letters, Elsevier, vol. 163(C), pages 90-94.
    288. Lux, Thomas, 2008. "Stochastic behavioral asset pricing models and the stylized facts," Economics Working Papers 2008-08, Christian-Albrechts-University of Kiel, Department of Economics.
    289. Nazlioglu, Saban & Gupta, Rangan & Bouri, Elie, 2020. "Movements in international bond markets: The role of oil prices," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 47-58.
    290. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2012. "Persistence and Cycles in the US Federal Funds Rate," CESifo Working Paper Series 4035, CESifo.
    291. Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.
    292. Baillie, Richard T. & Morana, Claudio, 2012. "Adaptive ARFIMA models with applications to inflation," Economic Modelling, Elsevier, vol. 29(6), pages 2451-2459.
    293. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    294. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    295. Sanjeeta Shirodkar & Guntur Anjana Raju, 2021. "Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 230-239.
    296. Elena Andreou & Eric Ghysels, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," CIRANO Working Papers 2004s-25, CIRANO.
    297. Gil-Alana, Luis Alberiko & Trani, Tommaso, 2019. "An examination of trade-weighted real exchange rates based on fractional integration," International Economics, Elsevier, vol. 158(C), pages 64-76.
    298. Luis A. Gil-Alana & Trilochan Tripathy, 2016. "Long Range Dependence in the Indian Stock Market: Evidence of Fractional Integration, Non-Linearities and Breaks," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 199-215, December.
    299. Luis A. Gil-Alana & Guglielmo M. Caporale, 2008. "Modelling the US, the UK and Japanese unemployment rates. Fractional integrationand structural breaks," Faculty Working Papers 11/08, School of Economics and Business Administration, University of Navarra.
    300. Bisaglia, Luisa & Gerolimetto, Margherita, 2008. "Forecasting long memory time series when occasional breaks occur," Economics Letters, Elsevier, vol. 98(3), pages 253-258, March.
    301. Richard T. Baillie & George Kapetanios, 2005. "Testing for Neglected Nonlinearity in Long Memory Models," Working Papers 528, Queen Mary University of London, School of Economics and Finance.
    302. Alfarano, Simone & Lux, Thomas, 2006. "A minimal noise trader model with realistic time series properties," Economics Working Papers 2006-11, Christian-Albrechts-University of Kiel, Department of Economics.
    303. Juan Hoyo & Guillermo Llorente & Carlos Rivero, 2020. "A Testing Procedure for Constant Parameters in Stochastic Volatility Models," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 163-186, June.
    304. Mboya, Mwasi & Sibbertsen, Philipp, 2022. "Optimal Forecasts in the Presence of Discrete Structural Breaks under Long Memory," Hannover Economic Papers (HEP) dp-705, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    305. Huthaifa Alqaralleh & Alaa Adden Abuhommous & Ahmad Alsaraireh, 2020. "Modelling and Forecasting the Volatility of Cryptocurrencies: A Comparison of Nonlinear GARCH-Type Models," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(4), pages 346-356, July.
    306. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," NBER Working Papers 9839, National Bureau of Economic Research, Inc.
    307. Jonathan Dark & Xin Gao & Thijs van der Heijden & Federico Nardari, 2022. "Forecasting variance swap payoffs," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2135-2164, December.
    308. Juan Carlos Cuestas & Luis A. Gil-Alana & Karl Taylor, 2016. "Inflation convergence in Central and Eastern Europe vs. the Eurozone: Non-linearities and long memory," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(5), pages 519-538, November.
    309. Krämer, Walter, 2008. "Long memory with Markov-Switching GARCH," Economics Letters, Elsevier, vol. 99(2), pages 390-392, May.
    310. Luis A. Gil‐Alana, 2004. "A joint test of fractional integration and structural breaks at a known period of time," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 691-700, September.
    311. Yalama, Abdullah & Celik, Sibel, 2013. "Real or spurious long memory characteristics of volatility: Empirical evidence from an emerging market," Economic Modelling, Elsevier, vol. 30(C), pages 67-72.
    312. Guglielmo Maria Caporale & Luis Alberiko Gil-Alana & Robert Mudida, 2015. "Testing the Marshall–Lerner Condition in Kenya," South African Journal of Economics, Economic Society of South Africa, vol. 83(2), pages 253-268, June.
    313. Cunado, J. & Gil-Alana, L. A. & Perez de Gracia, F., 2004. "Is the US fiscal deficit sustainable?: A fractionally integrated approach," Journal of Economics and Business, Elsevier, vol. 56(6), pages 501-526.
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    4. Wendy Nyakabawo & Stephen M. Miller & Mehmet Balcilar & Sonali Das & Rangan Gupta, 2013. "Temporal Causality between House Prices and Output in the U. S.: A Bootstrap Rolling-Window Approach," Working Papers 201329, University of Pretoria, Department of Economics.
    5. DUFOUR, Jean-Marie, 2005. "Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics," Cahiers de recherche 2005-03, Universite de Montreal, Departement de sciences economiques.
    6. Claude Lopez & Christian J. Murray & David H. Papell, 2004. "State of the Art Unit Root Tests and Purchasing Power Parity," University of Cincinnati, Economics Working Papers Series 2004-04, University of Cincinnati, Department of Economics.
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    8. Kim, Jae H., 2003. "Forecasting autoregressive time series with bias-corrected parameter estimators," International Journal of Forecasting, Elsevier, vol. 19(3), pages 493-502.
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    10. 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.
    11. Rossi, Barbara, 2005. "Confidence Intervals for Half-Life Deviations From Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 432-442, October.
    12. Claude Lopez & Christian J. Murray & David H. Papell, 2008. "Median-Unbiased Estimation in DF-GLS Regressions and the PPP Puzzle," University of Cincinnati, Economics Working Papers Series 2008-05, University of Cincinnati, Department of Economics, revised 2008.
    13. Cavaliere, Giuseppe & Rahbek, Anders & Taylor, A.M. Robert, 2010. "Testing for co-integration in vector autoregressions with non-stationary volatility," Journal of Econometrics, Elsevier, vol. 158(1), pages 7-24, September.
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    17. Baumeister, Christiane & Kilian, Lutz, 2013. "Do oil price increases cause higher food prices?," CFS Working Paper Series 2013/10, Center for Financial Studies (CFS).
    18. Prodan, Ruxandra, 2008. "Potential Pitfalls in Determining Multiple Structural Changes With an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 50-65, January.
    19. Shin, Dong Wan & Hwang, Eunju, 2013. "Stationary bootstrapping for cointegrating regressions," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 474-480.
    20. Omtzigt Pieter & Fachin Stefano, 2002. "Bootstrapping and Bartlett corrections in the cointegrated VAR model," Economics and Quantitative Methods qf0212, Department of Economics, University of Insubria.
    21. Cavaliere, Giuseppe & Rahbek, Anders & Taylor, A.M. Robert, 2010. "Cointegration Rank Testing Under Conditional Heteroskedasticity," Econometric Theory, Cambridge University Press, vol. 26(6), pages 1719-1760, December.
    22. Kruse, Yves Robinson & Kaufmann, Hendrik, 2015. "Bias-corrected estimation in mildly explosive autoregressions," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112897, Verein für Socialpolitik / German Economic Association.
    23. Kruse, Robinson & Kaufmann, Hendrik & Wegener, Christoph, 2018. "Bias-corrected estimation for speculative bubbles in stock prices," Economic Modelling, Elsevier, vol. 73(C), pages 354-364.
    24. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    25. Burridge, Peter & Robert Taylor, A. M., 2004. "Bootstrapping the HEGY seasonal unit root tests," Journal of Econometrics, Elsevier, vol. 123(1), pages 67-87, November.
    26. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.
    27. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    28. Mehmet Balcilar & Zeynel Ozdemir, 2013. "The export-output growth nexus in Japan: a bootstrap rolling window approach," Empirical Economics, Springer, vol. 44(2), pages 639-660, April.
    29. Fuertes, Ana-Maria, 2008. "Sieve bootstrap t-tests on long-run average parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3354-3370, March.
    30. Francis X. Diebold & Lutz Kilian, 1997. "Measuring Predictability: Theory and Macroeconomic Applications," NBER Technical Working Papers 0213, National Bureau of Economic Research, Inc.
    31. Schusser, Sandra & Jaraite, Jurate, 2016. "Explaining the Interplay of Three Markets: Green Certificates, Carbon Emissions and Electricity," CERE Working Papers 2016:10, CERE - the Center for Environmental and Resource Economics.
    32. Quentin Giai Gianetto & Hamdi Raïssi, 2015. "Testing Instantaneous Causality in Presence of Nonconstant Unconditional Covariance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 46-53, January.
    33. Helmut Luetkepohl, 2011. "Vector Autoregressive Models," Economics Working Papers ECO2011/30, European University Institute.
    34. Park, Joon, 2003. "A Bootstrap Theory for Weakly Integrated Processes," Working Papers 2003-16, Rice University, Department of Economics.
    35. Yuriy Gorodnichenko, 2005. "Reduced-Rank Identification of Structural Shocks in VARs," Macroeconomics 0512011, University Library of Munich, Germany.
    36. Choi, In, 2005. "Inconsistency of bootstrap for nonstationary, vector autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 75(1), pages 39-48, November.
    37. Jui-Chung Yang & Ke-Li Xu, 2013. "Estimation and Inference under Weak Identi cation and Persistence: An Application on Forecast-Based Monetary Policy Reaction Function," 2013 Papers pya307, Job Market Papers.
    38. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    39. Pesavento, Elena, 2000. "Analytical Evaluation of the Power of Tests for the Absence of Cointegration," University of California at San Diego, Economics Working Paper Series qt4cq4773c, Department of Economics, UC San Diego.
    40. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    41. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    42. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    43. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    44. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    45. Ozdemir, Zeynel Abidin & Cakan, Esin, 2010. "The persistence in real exchange rate: Evidence from East Asian countries," Economic Modelling, Elsevier, vol. 27(5), pages 891-895, September.
    46. Sevan Gulesserian & Mohitosh Kejriwal, 2014. "On the power of bootstrap tests for stationarity: a Monte Carlo comparison," Empirical Economics, Springer, vol. 46(3), pages 973-998, May.
    47. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
    48. Yuriy Gorodnichenko & Linda Tesar, 2005. "A Re-Examination of the Border Effect," NBER Working Papers 11706, National Bureau of Economic Research, Inc.
    49. Diego Romero-Avila & Carlos Usabiaga, 2007. "Unit root tests and persistence of unemployment: Spain vs. the United States," Applied Economics Letters, Taylor & Francis Journals, vol. 14(6), pages 457-461.
    50. Diego Romero‐Ávila & Carlos Usabiaga, 2007. "Unit Root Tests, Persistence, and the Unemployment Rate of the U.S. States," Southern Economic Journal, John Wiley & Sons, vol. 73(3), pages 698-716, January.
    51. Elena Pesavento, Barbara Rossi, 2006. "Impulse Response Confidence Intervals for Persistent Data: What Have We Learned?," Economics Working Papers ECO2006/19, European University Institute.
    52. Stanislav Anatolyev, 2007. "The basics of bootstrapping (in Russian)," Quantile, Quantile, issue 3, pages 1-12, September.
    53. Amilcar Velez, 2023. "The Local Projection Residual Bootstrap for AR(1) Models," Papers 2309.01889, arXiv.org, revised Feb 2024.
    54. van Giersbergen, Noud P. A., 2003. "A note on bootstrapping unit root tests in the presence of a non-zero drift," Economics Letters, Elsevier, vol. 78(2), pages 259-265, February.
    55. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Arslanturk, Yalcin, 2010. "Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window," Energy Economics, Elsevier, vol. 32(6), pages 1398-1410, November.
    56. Carlos Usabiaga & Diego Romero-Ávila, 2012. "New Disaggregate Evidence on Spanish Inflation Persistence," EcoMod2012 3800, EcoMod.
    57. Diego Romero-Ávila & Carlos Usabiaga, 2012. "Disaggregate evidence on Spanish inflation persistence," Applied Economics, Taylor & Francis Journals, vol. 44(23), pages 3029-3046, August.
    58. Murray, Christian J. & Papell, David H., 2002. "The purchasing power parity persistence paradigm," Journal of International Economics, Elsevier, vol. 56(1), pages 1-19, January.
    59. Lieb, Lenard & Smeekes, Stephan, 2017. "Inference for Impulse Responses under Model Uncertainty," Research Memorandum 022, Maastricht University, Graduate School of Business and Economics (GSBE).
    60. Christis Katsouris, 2023. "Bootstrapping Nonstationary Autoregressive Processes with Predictive Regression Models," Papers 2307.14463, arXiv.org.
    61. Lei Pan & Vinod Mishra, 2019. "International Portfolio Diversification Possibilities: Could BRICS become a Destination for G7 Invesments," Monash Economics Working Papers 11-18, Monash University, Department of Economics.
    62. Giuseppe Cavaliere & Anders Rahbek & A. M. Robert Taylor, 2009. "Co-integration rank tests under conditional heteroskedasticity," Discussion Papers 09/02, University of Nottingham, Granger Centre for Time Series Econometrics.
    63. Jan J J Groen & Clare Lombardelli, 2004. "Real exchange rates and the relative prices of non-traded and traded goods: an empirical analysis," Bank of England working papers 223, Bank of England.
    64. Gafarov, Bulat & Meier, Matthias & Montiel Olea, José Luis, 2018. "Delta-method inference for a class of set-identified SVARs," Journal of Econometrics, Elsevier, vol. 203(2), pages 316-327.

  39. Peter Christoffersen & Jinyong Hahn & Atsushi Inoue, 1999. "Testing, Comparing, and Combining Value at Risk Measures," Center for Financial Institutions Working Papers 99-44, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Cited by:

    1. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
    2. Chen, Xiaohong & Hong, Han & Shum, Matthew, 2007. "Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models," Journal of Econometrics, Elsevier, vol. 141(1), pages 109-140, November.
    3. Victor Chernozhukov & Iván Fernández-Val, 2011. "Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(2), pages 559-589.
    4. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    5. Shcherba, Alexandr, 2011. "Comparison of VaR estimation methods for different forecasting samples for Russian stocks," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 24(4), pages 58-70.
    6. Dany Rogers Silva & Karem Cristina de Sousa Ribeiro & Hsia Hua Sheng, 2011. "Trade credit profitability measurement: application in a wholesalerdistributor case," Brazilian Business Review, Fucape Business School, vol. 8(2), pages 22-41, April.
    7. Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2014. "Are news important to predict large losses?," Papers 1410.6898, arXiv.org, revised Oct 2014.
    8. Kilic, Ekrem, 2006. "Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio," MPRA Paper 5610, University Library of Munich, Germany.

  40. Francis X. Diebold & Andrew Hickman & Atsushi Inoue & Til Schuermann, 1997. "Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think," Center for Financial Institutions Working Papers 97-34, Wharton School Center for Financial Institutions, University of Pennsylvania.

    Cited by:

    1. Odening, Martin & Hinrichs, Jan, 2003. "Die Quantifizierung von Marktrisiken in der Tierproduktion mittels Value-at-Risk und Extreme-Value-Theory," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 52(02), pages 1-11.
    2. Wolff, Christian & Lehnert, Thorsten, 2001. "Modelling Scale-Consistent VaR with the Truncated Lévy Flight," CEPR Discussion Papers 2711, C.E.P.R. Discussion Papers.
    3. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "VaR performance during the subprime and sovereign debt crises: An application to emerging markets," Emerging Markets Review, Elsevier, vol. 20(C), pages 23-41.
    4. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," PIER Working Paper Archive 05-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    6. 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.
    7. Saadi, Samir & Rahman, Abdul, 2008. "Evidence of non-stationary bias in scaling by square root of time: Implications for Value-at-Risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(3), pages 272-289, July.
    8. Odening, Martin & Hinrichs, Jan, 2002. "Assessment Of Market Risk In Hog Production Using Value-At-Risk And Extreme Value Theory," 2002 Annual meeting, July 28-31, Long Beach, CA 19907, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Mark R. Manfredo & Raymond M. Leuthold, 1998. "Agricultural Applications of Value-at-Risk Analysis: A Perspective," Finance 9805002, University Library of Munich, Germany.
    10. Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," NBER Working Papers 6844, National Bureau of Economic Research, Inc.
    11. Th'eophile Griveau-Billion & Ben Calderhead, 2019. "A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour," Papers 1904.08153, arXiv.org, revised Jan 2020.
    12. Kam Fong Chan & Christopher Gan & Patricia A. McGraw, 2003. "A Hedging Strategy for New Zealand’s Exporters in Transaction Exposure to Currency Risk," Multinational Finance Journal, Multinational Finance Journal, vol. 7(1-2), pages 25-54, March-Jun.
    13. Wang, Jying-Nan & Du, Jiangze & Hsu, Yuan-Teng, 2018. "Measuring long-term tail risk: Evaluating the performance of the square-root-of-time rule," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 120-138.
    14. Peter F. Christoffersen & Francis X. Diebold & Til Schuermann, 1998. "Horizon Problems and Extreme Events in Financial Risk Management," Center for Financial Institutions Working Papers 98-16, Wharton School Center for Financial Institutions, University of Pennsylvania.
    15. Kavussanos, Manolis G. & Dimitrakopoulos, Dimitris N., 2011. "Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 258-268.
    16. Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman, 2019. "Bayesian Risk Forecasting for Long Horizons," Tinbergen Institute Discussion Papers 19-018/III, Tinbergen Institute.
    17. J. Q. Smith & António Santos, 2003. "Second Order Filter Distribution Approximations for Financial Time Series with Extreme Outlier," GEMF Working Papers 2003-03, GEMF, Faculty of Economics, University of Coimbra.
    18. Danielsson, Jon & Zigrand, Jean-Pierre, 2006. "On time-scaling of risk and the square-root-of-time rule," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2701-2713, October.
    19. Amy S. K. Wong, 2006. "Basel II and the Risk Management of Basket Options with Time-Varying Correlations," International Journal of Central Banking, International Journal of Central Banking, vol. 2(4), December.
    20. Wang, Jying-Nan & Yeh, Jin-Huei & Cheng, Nick Ying-Pin, 2011. "How accurate is the square-root-of-time rule in scaling tail risk: A global study," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1158-1169, May.
    21. Simon Fritzsch & Maike Timphus & Gregor Weiss, 2021. "Marginals Versus Copulas: Which Account For More Model Risk In Multivariate Risk Forecasting?," Papers 2109.10946, arXiv.org.

  41. Atsushi Inoue, "undated". "Testing Change in Time Series," Computing in Economics and Finance 1997 7, Society for Computational Economics.

    Cited by:

    1. Rossi, Barbara & Sekhposyan, Tatevik, 2013. "Conditional predictive density evaluation in the presence of instabilities," Journal of Econometrics, Elsevier, vol. 177(2), pages 199-212.
    2. Bücher, Axel & Kojadinovic, Ivan & Rohmer, Tom & Segers, Johan, 2014. "Detecting changes in cross-sectional dependence in multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 111-128.
    3. 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.
    4. Barbara Rossi & Tatevik Sekhposyan, 2014. "Alternative tests for correct specification of conditional predictive densities," Economics Working Papers 1416, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2017.
    5. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    6. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    7. 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.
    8. Leonie Selk & Natalie Neumeyer, 2013. "Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 770-788, December.
    9. Chen, Bin & Hong, Yongmiao, 2012. "Testing For The Markov Property In Time Series," Econometric Theory, Cambridge University Press, vol. 28(1), pages 130-178, February.
    10. Qu, Zhongjun, 2008. "Testing for structural change in regression quantiles," Journal of Econometrics, Elsevier, vol. 146(1), pages 170-184, September.
    11. Jonas Dovern & Geoff Kenny, 2020. "Anchoring Inflation Expectations in Unconventional Times: Micro Evidence for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 16(5), pages 309-347, October.
    12. Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
    13. 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.
    14. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification," Departmental Working Papers 200311, Rutgers University, Department of Economics.
    15. Elena Andreou & Eric Ghysels, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," CIRANO Working Papers 2004s-25, CIRANO.
    16. Corradi, Valentina & Swanson, Norman R., 2005. "Bootstrap specification tests for diffusion processes," Journal of Econometrics, Elsevier, vol. 124(1), pages 117-148, January.
    17. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    18. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
    19. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
    20. Holmes, Mark & Kojadinovic, Ivan & Quessy, Jean-François, 2013. "Nonparametric tests for change-point detection à la Gombay and Horváth," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 16-32.
    21. Rohmer, Tom, 2016. "Some results on change-point detection in cross-sectional dependence of multivariate data with changes in marginal distributions," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 45-54.
    22. Fabio Busetti, 2012. "On detecting end-of-sample instabilities," Temi di discussione (Economic working papers) 881, Bank of Italy, Economic Research and International Relations Area.
    23. Su, Liangjun & Xiao, Zhijie, 2008. "Testing for parameter stability in quantile regression models," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2768-2775, November.
    24. Bucher, Axel, 2013. "A note on weak convergence of the sequential multivariate empirical process under strong mixing," LIDAM Discussion Papers ISBA 2013028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    25. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 723-736.
    26. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2012. "A new fluctuation test for constant variances with applications to finance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1111-1127, November.
    27. Jean-François Quessy, 2019. "Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series," Statistical Papers, Springer, vol. 60(3), pages 717-746, June.

Articles

  1. Inoue, Atsushi & Kilian, Lutz, 2022. "Joint Bayesian inference about impulse responses in VAR models," Journal of Econometrics, Elsevier, vol. 231(2), pages 457-476.
    See citations under working paper version above.
  2. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2021. "Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 307-324, January.
    See citations under working paper version above.
  3. Atsushi Inoue & Lu Jin & Denis Pelletier, 2021. "Local-Linear Estimation of Time-Varying-Parameter GARCH Models and Associated Risk Measures [Modelling Volatility by Variance Decomposition]," Journal of Financial Econometrics, Oxford University Press, vol. 19(1), pages 202-234.

    Cited by:

    1. Cai, Zongwu & Juhl, Ted, 2023. "The distribution of rolling regression estimators," Journal of Econometrics, Elsevier, vol. 235(2), pages 1447-1463.
    2. 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.
    3. Armin Pourkhanali & Jonathan Keith & Xibin Zhang, 2021. "Conditional Heteroscedasticity Models with Time-Varying Parameters: Estimation and Asymptotics," Monash Econometrics and Business Statistics Working Papers 15/21, Monash University, Department of Econometrics and Business Statistics.

  4. Atsushi Inoue & Barbara Rossi, 2021. "A new approach to measuring economic policy shocks, with an application to conventional and unconventional monetary policy," Quantitative Economics, Econometric Society, vol. 12(4), pages 1085-1138, November.
    See citations under working paper version above.
  5. Inoue, Atsushi & Kuo, Chun-Hung & Rossi, Barbara, 2020. "Identifying the sources of model misspecification," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 1-18.
    See citations under working paper version above.
  6. Inoue, Atsushi & Kilian, Lutz, 2020. "The uniform validity of impulse response inference in autoregressions," Journal of Econometrics, Elsevier, vol. 215(2), pages 450-472.
    See citations under working paper version above.
  7. Inoue, Atsushi & Rossi, Barbara, 2019. "The effects of conventional and unconventional monetary policy on exchange rates," Journal of International Economics, Elsevier, vol. 118(C), pages 419-447.
    See citations under working paper version above.
  8. Atsushi Inoue & Mototsugu Shintani, 2018. "Quasi‐Bayesian model selection," Quantitative Economics, Econometric Society, vol. 9(3), pages 1265-1297, November.
    See citations under working paper version above.
  9. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    See citations under working paper version above.
  10. 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.
    See citations under working paper version above.
  11. Inoue, Atsushi & Kilian, Lutz, 2016. "Joint confidence sets for structural impulse responses," Journal of Econometrics, Elsevier, vol. 192(2), pages 421-432.
    See citations under working paper version above.
  12. Yasuo Hirose & Atsushi Inoue, 2016. "The Zero Lower Bound and Parameter Bias in an Estimated DSGE Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 630-651, June.
    See citations under working paper version above.
  13. Emily Anderson & Atsushi Inoue & Barbara Rossi, 2016. "Heterogeneous Consumers and Fiscal Policy Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(8), pages 1877-1888, December.
    See citations under working paper version above.
  14. Han, Xu & Inoue, Atsushi, 2015. "Tests For Parameter Instability In Dynamic Factor Models," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1117-1152, October.
    See citations under working paper version above.
  15. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    See citations under working paper version above.
  16. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    See citations under working paper version above.
  17. Atsushi Inoue, 2012. "Mean-Plus-Noise Factor Models: An Empirical Exploration," The Japanese Economic Review, Japanese Economic Association, vol. 63(3), pages 289-309, September.

    Cited by:

    1. Eo, Yunjong & Kim, Chang-Jin, 2012. "Markov-Switching Models with Evolving Regime-Specific Parameters: Are Post-War Booms or Recessions All Alike?," Working Papers 2012-04, University of Sydney, School of Economics.

  18. Hall, Alastair R. & Inoue, Atsushi & Nason, James M. & Rossi, Barbara, 2012. "Information criteria for impulse response function matching estimation of DSGE models," Journal of Econometrics, Elsevier, vol. 170(2), pages 499-518.
    See citations under working paper version above.
  19. Atsushi Inoue & Barbara Rossi, 2011. "Identifying the Sources of Instabilities in Macroeconomic Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1186-1204, November.

    Cited by:

    1. 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).
    2. In-Koo Cho & Kenneth Kasa, 2016. "Gresham’S Law Of Model Averaging," Discussion Papers dp16-06, Department of Economics, Simon Fraser University.
    3. 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.
    4. Tommaso Ferraresi & Andrea Roventini & Willi Semmler, 2016. "Macroeconomic Regimes, Technological Shocks and Employment Dynamics," Sciences Po publications 2016-19, Sciences Po.
    5. Gürkaynak, Refet S. & Kantur, Zeynep & Tas, M. Anil & Yildirim, Secil, 2015. "Monetary policy in Turkey after Central Bank independence," CFS Working Paper Series 520, Center for Financial Studies (CFS).
    6. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    7. Jürgen Jerger & Oke Röhe, 2012. "Testing for Parameter Stability in DSGE Models. The Cases of France, Germany, Italy, and Spain," Working Papers 118, Bavarian Graduate Program in Economics (BGPE).
    8. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2020. "Uncertainty and Monetary Policy during Extreme Events," Economics Working Papers 2020-11, Department of Economics and Business Economics, Aarhus University.
    9. Erdenebat Bataa & Marwan Izzeldin & Denise Osborn, 2015. "Changes in the global oil market," Working Papers 75761696, Lancaster University Management School, Economics Department.
    10. Gulan, Adam, 2018. "Paradise lost? A brief history of DSGE macroeconomics," Bank of Finland Research Discussion Papers 22/2018, Bank of Finland.
    11. Martínez-García Enrique, 2018. "Modeling time-variation over the business cycle (1960–2017): an international perspective," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(5), pages 1-25, December.
    12. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    13. Jerger, Jürgen & Röhe, Oke, 2009. "Testing for Parameter Stability in DSGE Models. The Cases of France, Germany and Spain," University of Regensburg Working Papers in Business, Economics and Management Information Systems 453, University of Regensburg, Department of Economics.
    14. Marcellino, Massimiliano & Galvão, Ana Beatriz, 2010. "Endogenous Monetary Policy Regimes and the Great Moderation," CEPR Discussion Papers 7827, C.E.P.R. Discussion Papers.
    15. Mariano Kulish & Adrian Pagan, 2014. "Estimation and Solution of Models with Expectations and Structural Changes," CAMA Working Papers 2014-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    16. Seonghoon Cho & Koen Inghelbrecht & Geert Bekaert & Antonio Moreno & Lieven Baele, 2011. "Macroeconomic Regimes," 2011 Meeting Papers 817, Society for Economic Dynamics.
    17. Andrew C. Chang & Phillip Li, 2015. "Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say \"Usually Not\"," Finance and Economics Discussion Series 2015-83, Board of Governors of the Federal Reserve System (U.S.).
    18. Givens, Gregory & Salemi, Michael, 2012. "Inferring monetary policy objectives with a partially observed state," MPRA Paper 39353, University Library of Munich, Germany.
    19. Paul Hubert & Harun Mirza, 2014. "Inflation expectation dynamics: the role of past present and forward looking information," Working Papers hal-03473828, HAL.
    20. 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.
    21. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.
    22. M. Hashem Pesaran & Ron P. Smith, 2018. "Tests of Policy Interventions in DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(3), pages 457-484, June.
    23. Francesca Marino, 2016. "The Italian productivity slowdown in a Real Business Cycle perspective," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 63(2), pages 171-193, June.
    24. Miguel Casares & Jesús Vázquez, 2018. "The Swings Of U.S. Inflation And The Gibson Paradox," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 799-820, April.
    25. 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.
    26. Galvao Ana Beatriz & Marcellino Massimiliano, 2014. "The effects of the monetary policy stance on the transmission mechanism," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 1-20, May.
    27. Imane El Ouadghiri & Remzi Uctum, 2020. "Macroeconomic expectations and time varying heterogeneity: Evidence from individual survey data," Post-Print hal-03319091, HAL.
    28. Efrem Castelnuovo & Giovanni Pellegrino, 2018. "Uncertainty-dependent Effects of Monetary Policy Shocks: A New Keynesian Interpretation," Melbourne Institute Working Paper Series wp2018n02, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    29. Aguirre, Idoia & Vázquez, Jesús, 2020. "Learning, parameter variability, and swings in US macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 66(C).
    30. De Lipsis Vincenzo, 2021. "Dating Structural Changes in UK Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 21(2), pages 509-539, June.
    31. John W. Keating & Victor J. Valcarcel, 2012. "What's so Great about the Great Moderation? A Multi-Country Investigation of Time-Varying Volatilities of Output Growth and Inflation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201204, University of Kansas, Department of Economics.
    32. 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.
    33. Liu, Yuelin & Morley, James, 2014. "Structural evolution of the postwar U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 42(C), pages 50-68.
    34. 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.
    35. Mboya, Mwasi & Sibbertsen, Philipp, 2022. "Optimal Forecasts in the Presence of Discrete Structural Breaks under Long Memory," Hannover Economic Papers (HEP) dp-705, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    36. Paul Hubert & Harun Mirza, 2019. "The role of forward- and backward-looking information for inflation expectations formation," Post-Print hal-03403616, HAL.
    37. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
    38. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
    39. Bayar, Omer, 2018. "Weak instruments and estimated monetary policy rules," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 308-317.
    40. Ana gomez-Loscos & M. Dolores Gadea (Universidad de Zaragoza) & Gabriel Perez-Quiros (Bank of Spain), 2015. "Great Moderation and Great Recession. From plain sailing to stormy seas?," EcoMod2015 8267, EcoMod.
    41. Andrew C. Chang & Phillip Li, 2018. "Measurement Error In Macroeconomic Data And Economics Research: Data Revisions, Gross Domestic Product, And Gross Domestic Income," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1846-1869, July.
    42. Harun Mirza & Lidia Storjohann, 2014. "Making Weak Instrument Sets Stronger: Factor‐Based Estimation of Inflation Dynamics and a Monetary Policy Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 643-664, June.
    43. Yuelin Liu & James Morley, 2013. "Structural Evolution of the Postwar U.S. Economy," Discussion Papers 2013-15, School of Economics, The University of New South Wales.
    44. Mirza, Harun & Storjohann, Lidia, 2011. "Making a Weak Instrument Set Stronger: Factor-Based Estimation of the Taylor Rule," Bonn Econ Discussion Papers 13/2011, University of Bonn, Bonn Graduate School of Economics (BGSE).
    45. Rossi, Barbara & Inoue, Atsushi & Jin, Lu, 2014. "Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters," CEPR Discussion Papers 10168, C.E.P.R. Discussion Papers.
    46. Castelnuovo, Efrem, 2013. "Monetary policy shocks and financial conditions: A Monte Carlo experiment," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 282-303.
    47. 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.
    48. 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.
    49. Keating, John W. & Valcarcel, Victor J., 2017. "What's so great about the Great Moderation?," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 115-142.
    50. 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).

  20. Inoue, Atsushi & Rossi, Barbara, 2011. "Testing for weak identification in possibly nonlinear models," Journal of Econometrics, Elsevier, vol. 161(2), pages 246-261, April.
    See citations under working paper version above.
  21. Atsushi Inoue & Gary Solon, 2010. "Two-Sample Instrumental Variables Estimators," The Review of Economics and Statistics, MIT Press, vol. 92(3), pages 557-561, August.
    See citations under working paper version above.
  22. Atsushi Inoue & Lutz Kilian & Fatma Burcu Kiraz, 2009. "Do Actions Speak Louder Than Words? Household Expectations of Inflation Based on Micro Consumption Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(7), pages 1331-1363, October.
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  23. Inoue, Atsushi, 2008. "Efficient estimation and inference in linear pseudo-panel data models," Journal of Econometrics, Elsevier, vol. 142(1), pages 449-466, January.

    Cited by:

    1. Hai-Anh Dang & Peter Lanjouw, 2022. "Measuring Poverty Dynamics with Synthetic Panels Based on Repeated Cross-Sections," Working Papers 632, ECINEQ, Society for the Study of Economic Inequality.
    2. Emrehan Aktug & Tolga Umut Kuzubas & Orhan Torul, 2018. "Heterogeneity in Labor Income Profiles: Evidence from Turkey," Working Papers 2018/10, Bogazici University, Department of Economics.
    3. Gerard Ferrer-Esteban & Mauro Mediavilla, 2017. "The more educated, the more engaged? An analysis of social capital and education," Working Papers 2017/13, Institut d'Economia de Barcelona (IEB).
    4. Guarini, Giulio & Laureti, Tiziana & Garofalo, Giuseppe, 2018. "Territorial and individual educational inequality: A Capability Approach analysis for Italy," Economic Modelling, Elsevier, vol. 71(C), pages 247-262.
    5. Aart Kraay & Roy Weide, 2022. "Measuring intragenerational mobility using aggregate data," Journal of Economic Growth, Springer, vol. 27(2), pages 273-314, June.
    6. Emrehan Aktug & Tolga Umut Kuzubas & Orhan Torul, 2017. "An Investigation of Labor Income Profiles in Turkey," Working Papers 2017/04, Bogazici University, Department of Economics.
    7. Morley K. Gunderson & Byron Y. Lee & Hui Wang, 2024. "Worker Congresses in China: Do they matter?," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 63(1), pages 43-58, January.
    8. Dang,Hai-Anh H. & Lanjouw,Peter F., 2013. "Measuring poverty dynamics with synthetic panels based on cross-sections," Policy Research Working Paper Series 6504, The World Bank.
    9. Artūras Juodis, 2018. "Pseudo Panel Data Models With Cohort Interactive Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 47-61, January.
    10. Nadja Dwenger & Viktor Steiner, 2014. "Financial leverage and corporate taxation: evidence from German corporate tax return data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 21(1), pages 1-28, February.
    11. D'Amato, Alessio & Giaccherini, Matilde & Zoli, Mariangela, 2019. "The Role of Information Sources and Providers in Shaping Green Behaviors. Evidence from Europe," Ecological Economics, Elsevier, vol. 164(C), pages 1-1.
    12. Beatriz Muriel & Horacio Vera, 2015. "The Effects of Economic Growth on Earnings in Bolivia," Development Research Working Paper Series 08/2015, Institute for Advanced Development Studies.

  24. Inoue, Atsushi & Kilian, Lutz, 2008. "How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 511-522, June.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    3. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    4. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    5. Manuel Lukas & Eric Hillebrand, 2014. "Bagging Weak Predictors," CREATES Research Papers 2014-01, Department of Economics and Business Economics, Aarhus University.
    6. Antonello Loddo & Shawn Ni & Dongchu Sun, 2011. "Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 342-355, July.
    7. Giacomini, Raffaella & Ragusa, Giuseppe, 2011. "Incorporating theoretical restrictions into forecasting by projection methods," CEPR Discussion Papers 8604, C.E.P.R. Discussion Papers.
    8. Alessandro Giovannelli & Tommaso Proietti, 2014. "On the Selection of Common Factors for Macroeconomic Forecasting," CREATES Research Papers 2014-46, Department of Economics and Business Economics, Aarhus University.
    9. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    10. Lyu, Zhichong & Ma, Feng & Zhang, Jixiang, 2023. "Oil futures volatility prediction: Bagging or combination?," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 457-467.
    11. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    12. Gang Cheng & Sicong Wang & Yuhong Yang, 2015. "Forecast Combination under Heavy-Tailed Errors," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    13. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
    14. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    15. 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.
    16. 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.
    17. Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021. "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series 2021/04, Queen's University Belfast, Queen's Business School.
    18. Michael McAleer & Marcelo C. Medeiros, 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," CIRJE F-Series CIRJE-F-686, CIRJE, Faculty of Economics, University of Tokyo.
    19. 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.
    20. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    21. Ivașcu Codruț, 2023. "Can Machine Learning Models Predict Inflation?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1748-1756, July.
    22. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    23. 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.
    24. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    25. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    26. Zhu, Yinchu & Timmermann, Allan, 2022. "Conditional rotation between forecasting models," Journal of Econometrics, Elsevier, vol. 231(2), pages 329-347.
    27. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
    28. Boot, Tom & Nibbering, Didier, 2019. "Forecasting using random subspace methods," Journal of Econometrics, Elsevier, vol. 209(2), pages 391-406.
    29. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    30. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    31. Daniel Borup & Erik Christian Montes Schütte, 2022. "In Search of a Job: Forecasting Employment Growth Using Google Trends," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 186-200, January.
    32. KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," LIDAM Discussion Papers CORE 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    33. David Rapach & Jack Strauss, 2010. "Bagging or Combining (or Both)? An Analysis Based on Forecasting U.S. Employment Growth," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 511-533.
    34. Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
    35. Apergis Nicholas, 2021. "Forecasting US overseas travelling with univariate and multivariate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 963-976, September.
    36. Andreas Karatahansopoulos & Georgios Sermpinis & Jason Laws & Christian Dunis, 2014. "Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 596-610, December.
    37. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    38. 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.
    39. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    40. Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2016. "Forecasting macroeconomic variables in data-rich environments," Economics Letters, Elsevier, vol. 138(C), pages 50-52.
    41. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    42. Christina Anderl & Guglielmo Maria Caporale, 2023. "Forecasting inflation with a zero lower bound or negative interest rates: Evidence from point and density forecasts," Manchester School, University of Manchester, vol. 91(3), pages 171-232, June.
    43. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    44. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    45. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    46. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
    47. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
    48. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
    49. Jon D. Samuels & Rodrigo Sekkel, 2013. "Forecasting with Many Models: Model Confidence Sets and Forecast Combination," Staff Working Papers 13-11, Bank of Canada.
    50. Hongwei Zhang & Qiang He & Ben Jacobsen & Fuwei Jiang, 2020. "Forecasting stock returns with model uncertainty and parameter instability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 629-644, August.
    51. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," University of California at San Diego, Economics Working Paper Series qt1st3n7z7, Department of Economics, UC San Diego.
    52. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
    53. Francesco Audrino & Marcelo Cunha Medeiros, 2010. "Modeling and Forecasting Short-term Interest Rates: The Benefits of Smooth Regimes, Macroeconomic Variables, and Bagging," Textos para discussão 570, Department of Economics PUC-Rio (Brazil).
    54. Xu Cheng & Bruce E. Hansen, 2012. "Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach," PIER Working Paper Archive 12-046, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    55. Erik Christian Montes Schütte, 2018. "In Search of a Job: Forecasting Employment Growth in the US using Google Trends," CREATES Research Papers 2018-25, Department of Economics and Business Economics, Aarhus University.
    56. Krüger Fabian & Pohlmeier Winfried & Mokinski Frieder, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 63-81, February.
    57. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    58. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    59. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    60. Yuyi Zhang & Ruimin Ma & Jing Liu & Xiuxiu Liu & Ovanes Petrosian & Kirill Krinkin, 2021. "Comparison and Explanation of Forecasting Algorithms for Energy Time Series," Mathematics, MDPI, vol. 9(21), pages 1-12, November.
    61. 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.
    62. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    63. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    64. Ribeiro, Pinho J., 2017. "Selecting exchange rate fundamentals by bootstrap," International Journal of Forecasting, Elsevier, vol. 33(4), pages 894-914.
    65. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    66. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    67. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz, 2018. "Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing," International Journal of Forecasting, Elsevier, vol. 34(4), pages 748-761.
    68. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    69. In Choi & Seong Jin Hwang, 2012. "Forecasting Korean inflation," Working Papers 1202, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    70. Dufays, A. & Rombouts, V., 2015. "Sparse Change-Point Time Series Models," LIDAM Discussion Papers CORE 2015032, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    71. Yang, Ke & Tian, Fengping & Chen, Langnan & Li, Steven, 2017. "Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 276-291.
    72. Gilberto Boaretto & Marcelo C. Medeiros, 2023. "Forecasting inflation using disaggregates and machine learning," Papers 2308.11173, arXiv.org.
    73. 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.
    74. Timmermann, Allan & Zhu, Yinchu, 2021. "Conditional Rotation Between Forecasting Models," CEPR Discussion Papers 15917, C.E.P.R. Discussion Papers.
    75. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2016. "Can commodity returns forecast Canadian sector stock returns?," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 172-188.
    76. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    77. Rapach, David E. & Strauss, Jack K., 2012. "Forecasting US state-level employment growth: An amalgamation approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 315-327.
    78. Kadir Özen & Dilem Yıldırım, 2021. "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers 2101, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    79. Luca Brugnolini & Giuseppe Ragusa, 2022. "Euro Area Deflationary Pressure Index," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 883-900, October.
    80. 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.
    81. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Georgios Sermpinis & Charalampos Stasinakis & Konstantinos Theofilatos & Andreas Karathanasopoul, 2014. "Inflation and Unemployment Forecasting with Genetic Support Vector Regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 471-487, September.
    82. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    83. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    84. Wang, Lu & Wu, Rui & Ma, WeiChun & Xu, Weiju, 2023. "Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information," International Review of Financial Analysis, Elsevier, vol. 89(C).
    85. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
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  25. Atsushi Inoue & Barbara Rossi, 2008. "Monitoring and Forecasting Currency Crises," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2-3), pages 523-534, March.
    See citations under working paper version above.
  26. Hall, Alastair R. & Inoue, Atsushi & Jana, Kalidas & Shin, Changmock, 2007. "Information in generalized method of moments estimation and entropy-based moment selection," Journal of Econometrics, Elsevier, vol. 138(2), pages 488-512, June.

    Cited by:

    1. Scheufele, Rolf, 2008. "Evaluating the German (New Keynesian) Phillips Curve," IWH Discussion Papers 10/2008, Halle Institute for Economic Research (IWH).
    2. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    3. De Lipsis, Vincenzo, 2021. "Is time preference different across incomes and countries?," Economics Letters, Elsevier, vol. 201(C).
    4. Alastair R. Hall & Atsushi Inoue & James M Nason & Barbara Rossi, 2009. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Centre for Growth and Business Cycle Research Discussion Paper Series 127, Economics, The University of Manchester.
    5. Lutz Kilian & Simone Manganelli, 2008. "The Central Banker as a Risk Manager: Estimating the Federal Reserve's Preferences under Greenspan," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1103-1129, September.
    6. Poghosyan, Karen & Boldea, Otilia, 2013. "Structural versus matching estimation: Transmission mechanisms in Armenia," Economic Modelling, Elsevier, vol. 30(C), pages 136-148.
    7. Jinyuan Chang & Zhentao Shi & Jia Zhang, 2021. "Culling the herd of moments with penalized empirical likelihood," Papers 2108.03382, arXiv.org, revised May 2022.
    8. Bolko, Anine E. & Christensen, Kim & Pakkanen, Mikko S. & Veliyev, Bezirgen, 2023. "A GMM approach to estimate the roughness of stochastic volatility," Journal of Econometrics, Elsevier, vol. 235(2), pages 745-778.
    9. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
    10. Meijer, Erik & Spierdijk, Laura & Wansbeek, Tom, 2017. "Consistent estimation of linear panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 169-180.
    11. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    12. Lina Zhang & David T. Frazier & D. S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Papers 2009.02642, arXiv.org, revised Sep 2022.
    13. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    14. Tae-Hwy Lee & Tao Wang, 2023. "Estimation and Testing of Forecast Rationality with Many Moments," Working Papers 202307, University of California at Riverside, Department of Economics.
    15. Cizek, P. & Aquaro, M., 2015. "Robust Estimation and Moment Selection in Dynamic Fixed-effects Panel Data Models," Other publications TiSEM 39d0f613-007f-4d21-b1e2-b, Tilburg University, School of Economics and Management.
    16. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    17. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
    18. Masahiko Shibamoto, 2016. "Empirical Assessment of the Impact of Monetary Policy Communication on the Financial Market," Discussion Paper Series DP2016-19, Research Institute for Economics & Business Administration, Kobe University.
    19. 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.
    20. Caner, Mehmet & Fan, Qingliang, 2015. "Hybrid generalized empirical likelihood estimators: Instrument selection with adaptive lasso," Journal of Econometrics, Elsevier, vol. 187(1), pages 256-274.
    21. Prosper Dovonon & Firmin Doko Tchatoka & Michael Aguessy, 2019. "Relevant moment selection under mixed identification strength," School of Economics and Public Policy Working Papers 2019-04, University of Adelaide, School of Economics and Public Policy.
    22. Tobias Rühl, 2015. "Taylor rules revisited: ECB and Bundesbank in comparison," Empirical Economics, Springer, vol. 48(3), pages 951-967, May.
    23. Eryuruk, Gunce & Hall, Alastair R. & Jana, Kalidas, 2009. "A comparative study of three data-based methods of instrument selection," Economics Letters, Elsevier, vol. 105(3), pages 280-283, December.
    24. Martyn Andrews & Obbey Elamin & Alastair R. Hall & Kostas Kyriakoulis & Matthew Sutton, 2017. "Inference in the presence of redundant moment conditions and the impact of government health expenditure on health outcomes in England," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 23-41, March.
    25. Tchatoka, Firmin Doko, 2015. "Subset Hypotheses Testing And Instrument Exclusion In The Linear Iv Regression," Econometric Theory, Cambridge University Press, vol. 31(6), pages 1192-1228, December.
    26. Rolando Einar Paz Rodriguez, 2019. "La función de emparejamiento agregada del mercado laboral chileno," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 34(1), pages 85-110, April.
    27. Karamysheva, Madina & Skrobotov, Anton, 2022. "Do we reject restrictions identifying fiscal shocks? identification based on non-Gaussian innovations," Journal of Economic Dynamics and Control, Elsevier, vol. 138(C).
    28. Ng Serena & Bai Jushan, 2009. "Selecting Instrumental Variables in a Data Rich Environment," Journal of Time Series Econometrics, De Gruyter, vol. 1(1), pages 1-34, April.
    29. Prosper Donovon & Alastair R. Hall, 2015. "GMM and Indirect Inference: An appraisal of their connections and new results on their properties under second order identification," Economics Discussion Paper Series 1505, Economics, The University of Manchester.
    30. Matteo Barigozzi & Roxana Halbleib & David Veredas, 2012. "Which model to match?," Working Papers 1229, Banco de España.
    31. Jondeau, Eric & Le Bihan, Hervé, 2008. "Examining bias in estimators of linear rational expectations models under misspecification," Journal of Econometrics, Elsevier, vol. 143(2), pages 375-395, April.
    32. Yiying Cheng & Yaozhong Hu & Hongwei Long, 2020. "Generalized moment estimators for $$\alpha $$α-stable Ornstein–Uhlenbeck motions from discrete observations," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 53-81, April.
    33. Yanli Ma & Jieyu Zhu & Gaofeng Gu & Ke Chen, 2020. "Freight Transportation and Economic Growth for Zones: Sustainability and Development Strategy in China," Sustainability, MDPI, vol. 12(24), pages 1-17, December.
    34. Eryuruk, Gunce & Hall, Alastair R. & Jana, Kalidas, 2009. "Contemporaneous and long run canonical correlations in the linear IV model: Implications for instrument selection," Economics Letters, Elsevier, vol. 105(1), pages 83-85, October.
    35. Chen, Weihao & Cizek, Pavel, 2023. "Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models," Other publications TiSEM 9bf2c16c-522f-4223-8037-c, Tilburg University, School of Economics and Management.
    36. Okui, Ryo, 2009. "The optimal choice of moments in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 151(1), pages 1-16, July.
    37. Martins, Luis F. & Gabriel, Vasco J., 2014. "Linear instrumental variables model averaging estimation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 709-724.
    38. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    39. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.

  27. Inoue, Atsushi & Solon, Gary, 2006. "A Portmanteau Test For Serially Correlated Errors In Fixed Effects Models," Econometric Theory, Cambridge University Press, vol. 22(5), pages 835-851, October.
    See citations under working paper version above.
  28. Atsushi Inoue, 2006. "A bootstrap approach to moment selection," Econometrics Journal, Royal Economic Society, vol. 9(1), pages 48-75, March.

    Cited by:

    1. Marcelo J. Moreira & Jack R. Porter & Gustavo A. Suarez, 2004. "Bootstrap and Higher-Order Expansion Validity When Instruments May Be Weak," Harvard Institute of Economic Research Working Papers 2048, Harvard - Institute of Economic Research.
    2. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    3. Tae-Hwy Lee & Tao Wang, 2023. "Estimation and Testing of Forecast Rationality with Many Moments," Working Papers 202307, University of California at Riverside, Department of Economics.
    4. Kuersteiner, Guido M., 2012. "Kernel-weighted GMM estimators for linear time series models," Journal of Econometrics, Elsevier, vol. 170(2), pages 399-421.
    5. Moreira, Marcelo J. & Porter, Jack R. & Suarez, Gustavo A., 2009. "Bootstrap validity for the score test when instruments may be weak," Journal of Econometrics, Elsevier, vol. 149(1), pages 52-64, April.
    6. Canay, Ivan A., 2010. "Simultaneous selection and weighting of moments in GMM using a trapezoidal kernel," Journal of Econometrics, Elsevier, vol. 156(2), pages 284-303, June.
    7. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  29. Inoue, Atsushi & Shintani, Mototsugu, 2006. "Bootstrapping GMM estimators for time series," Journal of Econometrics, Elsevier, vol. 133(2), pages 531-555, August.
    See citations under working paper version above.
  30. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.
    See citations under working paper version above.
  31. Atsushi Inoue & Tomislav Vukina, 2006. "Testing for the principal’s monopsony power in agency contracts," Empirical Economics, Springer, vol. 31(3), pages 717-734, September.

    Cited by:

    1. Timothy A. Wise & Sarah E. Trist, "undated". "Buyer Power in U.S. Hog Markets: A Critical Review of the Literature," GDAE Working Papers 10-04, GDAE, Tufts University.

  32. Atsushi Inoue & Lutz Kilian, 2005. "In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?," Econometric Reviews, Taylor & Francis Journals, vol. 23(4), pages 371-402.
    See citations under working paper version above.
  33. Inoue, Atsushi & Rossi, Barbara, 2005. "Recursive Predictability Tests for Real-Time Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 336-345, July.
    See citations under working paper version above.
  34. Hall, Alastair R. & Inoue, Atsushi & Peixe, Fernanda P.M., 2003. "Covariance Matrix Estimation And The Limiting Behavior Of The Overidentifying Restrictions Test In The Presence Of Neglected Structural Instability," Econometric Theory, Cambridge University Press, vol. 19(6), pages 962-983, December.

    Cited by:

    1. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2012. "Inference regarding multiple structural changes in linear models with endogenous regressors," Journal of Econometrics, Elsevier, vol. 170(2), pages 281-302.
    2. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    3. Shin-Kun Peng & Takatoshi Tabuchi, 2005. "Spatial Competition in Variety and Number of Stores," CIRJE F-Series CIRJE-F-360, CIRJE, Faculty of Economics, University of Tokyo.
    4. Wei-Ming Lee & Chung-Ming Kuan, 2006. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 06-A009, Institute of Economics, Academia Sinica, Taipei, Taiwan.

  35. Inoue, Atsushi & Kilian, Lutz, 2003. "The Continuity Of The Limit Distribution In The Parameter Of Interest Is Not Essential For The Validity Of The Bootstrap," Econometric Theory, Cambridge University Press, vol. 19(6), pages 944-961, December.

    Cited by:

    1. Jean-Marie Dufour & Tarek Jouini, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," CIRANO Working Papers 2005s-26, CIRANO.
    2. DUFOUR, Jean-Marie, 2005. "Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics," Cahiers de recherche 2005-03, Universite de Montreal, Departement de sciences economiques.
    3. Grabowski, Daniel & Staszewska-Bystrova, Anna, 2018. "Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181590, Verein für Socialpolitik / German Economic Association.
    4. George Kapetanios, 2004. "A Bootstrap Invariance Principle for Highly Nonstationary Long Memory Processes," Working Papers 507, Queen Mary University of London, School of Economics and Finance.
    5. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    6. George Kapetanios, 2004. "Testing for Exogeneity in Nonlinear Threshold Models," Working Papers 515, Queen Mary University of London, School of Economics and Finance.
    7. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    8. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.

  36. Hall, Alastair R. & Inoue, Atsushi, 2003. "The large sample behaviour of the generalized method of moments estimator in misspecified models," Journal of Econometrics, Elsevier, vol. 114(2), pages 361-394, June.
    See citations under working paper version above.
  37. Atsushi Inoue & Lutz Kilian, 2002. "Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR (∞) Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 309-332, May.

    Cited by:

    1. Trenkler, Carsten & Weber, Enzo, 2012. "Identifying the Shocks behind Business Cycle Asynchrony in Euroland," University of Regensburg Working Papers in Business, Economics and Management Information Systems 466, University of Regensburg, Department of Economics.
    2. Jeremy Berkowitz & Ionel Birgean & Lutz Kilian, 1999. "On the finite-sample accuracy of nonparametric resampling algorithms for economic time series," Finance and Economics Discussion Series 1999-04, Board of Governors of the Federal Reserve System (U.S.).
    3. Jean-Marie Dufour & Tarek Jouini, 2005. "Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing," CIRANO Working Papers 2005s-26, CIRANO.
    4. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    5. Lamb, John D. & Tee, Kai-Hong, 2012. "Resampling DEA estimates of investment fund performance," European Journal of Operational Research, Elsevier, vol. 223(3), pages 834-841.
    6. Brüggemann, Ralf & Jentsch, Carsten & Trenkler, Carsten, 2014. "Inference in VARs with Conditional Heteroskedasticity of Unknown Form," Working Papers 14-21, University of Mannheim, Department of Economics.
    7. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    8. Kyritsis, Evangelos & Serletis, Apostolos, 2017. "The Zero Lower Bound and Market Spillovers: Evidence from the G7 and Norway," Discussion Papers 2017/7, Norwegian School of Economics, Department of Business and Management Science.
    9. Fuertes, Ana-Maria, 2008. "Sieve bootstrap t-tests on long-run average parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3354-3370, March.
    10. Francis X. Diebold & Lutz Kilian, 1997. "Measuring Predictability: Theory and Macroeconomic Applications," NBER Technical Working Papers 0213, National Bureau of Economic Research, Inc.
    11. Helmut Luetkepohl, 2011. "Vector Autoregressive Models," Economics Working Papers ECO2011/30, European University Institute.
    12. Enrique Martínez García, 2016. "Finite-Order VAR Representation of Linear Rational Expectations Models: With Some Lessons for Monetary Policy," Globalization Institute Working Papers 285, Federal Reserve Bank of Dallas.
    13. Luca Sala, 2004. "The Fiscal Theory of the Price Level: Identifying Restrictions and Empirical Evidence," Working Papers 257, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    14. Atsushi Inoue & Lutz Kilian, 2019. "The Uniform Validity of Impulse Response Inference in Autoregressions," Working Papers 1908, Federal Reserve Bank of Dallas.
    15. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
    16. Ziadat, Salem Adel & McMillan, David G. & Herbst, Patrick, 2022. "Oil shocks and equity returns during bull and bear markets: The case of oil importing and exporting nations," Resources Policy, Elsevier, vol. 75(C).
    17. Richard T. Baillie & George Kapetanios & Fotis Papailias, 2017. "Inference for impulse response coefficients from multivariate fractionally integrated processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 60-84, March.
    18. Phillips, Kerk L. & Spencer, David E., 2010. "Bootstrapping Structural VARs: Avoiding a Potential Bias in Confidence Intervals for Impulse Response Functions," MPRA Paper 23503, University Library of Munich, Germany.
    19. Enrique Martínez García, 2020. "A Matter of Perspective: Mapping Linear Rational Expectations Models into Finite-Order VAR Form," Globalization Institute Working Papers 389, Federal Reserve Bank of Dallas.
    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. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    22. Dufour, Jean-Marie & Jouini, Tarek, 2006. "Finite-sample simulation-based inference in VAR models with application to Granger causality testing," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 229-254.
    23. Chaudourne, Jeremy & Fève, Patrick & Guay, Alain, 2014. "Understanding the effect of technology shocks in SVARs with long-run restrictions," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 154-172.
    24. Elena Pesavento, Barbara Rossi, 2006. "Impulse Response Confidence Intervals for Persistent Data: What Have We Learned?," Economics Working Papers ECO2006/19, European University Institute.
    25. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.
    26. Karras, Georgios & Lee, Jin Man & Stokes, Houston, 2005. "Sources of exchange-rate volatility: Impulses or propagation?," International Review of Economics & Finance, Elsevier, vol. 14(2), pages 213-226.
    27. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
    28. Cruz, Christopher John, 2022. "Reduced macroeconomic volatility after adoption of inflation targeting: Impulses or propagation?," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 759-770.
    29. Karras, Georgios & Lee, Jin Man & Stokes, Houston, 2006. "Why are postwar cycles smoother? Impulses or propagation?," Journal of Economics and Business, Elsevier, vol. 58(5-6), pages 392-406.
    30. Roberto Duncan & Enrique Martínez García, 2015. "Forecasting local inflation in Open Economies: What Can a NOEM Model Do?," Globalization Institute Working Papers 235, Federal Reserve Bank of Dallas, revised 21 Dec 2022.

  38. Inoue, Atsushi, 2002. "Identifying the sign of the slope of a monotonic function via OLS," Economics Letters, Elsevier, vol. 75(3), pages 419-424, May.

    Cited by:

    1. Vukina, Tomislav & Zheng, Xiaoyong & Marra, Michele & Levy, Armando, 2008. "Do farmers value the environment? Evidence from a conservation reserve program auction," International Journal of Industrial Organization, Elsevier, vol. 26(6), pages 1323-1332, November.

  39. Jinyong Hahn & Atsushi Inoue, 2002. "A Monte Carlo Comparison Of Various Asymptotic Approximations To The Distribution Of Instrumental Variables Estimators," Econometric Reviews, Taylor & Francis Journals, vol. 21(3), pages 309-336.

    Cited by:

    1. Paul A. Bekker & Jan van der Ploeg, 2000. "Instrumental Variable Estimation Based on Grouped Data," Econometric Society World Congress 2000 Contributed Papers 1862, Econometric Society.
    2. John Chao & Norman Swanson, 2004. "Consistent Estimation with a Large Number of Weak Instruments," Departmental Working Papers 200421, Rutgers University, Department of Economics.
    3. Jerry Hausman & Whitney K. Newey & Tiemen M. Woutersen & John Chao & Norman Swanson, 2007. "Instrumental variable estimation with heteroskedasticity and many instruments," CeMMAP working papers CWP22/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
    5. Bekker, Paul A. & Crudu, Federico, 2015. "Jackknife instrumental variable estimation with heteroskedasticity," Journal of Econometrics, Elsevier, vol. 185(2), pages 332-342.
    6. Park, Albert & Brandt, Loren & Giles, John, 2003. "Competition under credit rationing: theory and evidence from rural China," Journal of Development Economics, Elsevier, vol. 71(2), pages 463-495, August.
    7. Neil Kellard & Denise Osborn & Jerry Coakley & Alastair R. Hall & Denise R. Osborn & Nikolaos Sakkas, 2015. "Structural Break Inference Using Information Criteria in Models Estimated by Two-Stage Least Squares," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 741-762, September.
    8. Abutaliev, Albert & Anatolyev, Stanislav, 2013. "Asymptotic variance under many instruments: Numerical computations," Economics Letters, Elsevier, vol. 118(2), pages 272-274.
    9. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2012. "Inference regarding multiple structural changes in linear models with endogenous regressors," Journal of Econometrics, Elsevier, vol. 170(2), pages 281-302.
    10. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    11. Christl, Michael & Köppl Turyna, Monika & Kucsera, Denes, 2015. "Employment effects of minimum wages in Europe revisited," MPRA Paper 65761, University Library of Munich, Germany.
    12. John Chao & Norman R. Swanson, 2003. "Alternative Approximations of the Bias and MSE of the IV Estimator under Weak Identification with an Application to Bias Correction," Cowles Foundation Discussion Papers 1418, Cowles Foundation for Research in Economics, Yale University.
    13. Christian Hansen & Jerry Hausman & Whitney K. Newey, 2006. "Estimation with many instrumental variables," CeMMAP working papers CWP19/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Murray Michael P., 2017. "Linear Model IV Estimation When Instruments Are Many or Weak," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-22, January.
    15. Richard Startz & Charles Nelson & Eric Zivot, 1999. "Improved Inference for the Instrumental Variable Estimator," Working Papers 0039, University of Washington, Department of Economics.
    16. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2008. "Asymptotic Distribution Theory for Break Point Estimators in Models Estimated via 2SLS," MPRA Paper 9472, University Library of Munich, Germany.
    17. Otilia Boldea & Alastair Hall & Sanggohn Han, 2010. "Uncertainty, Entrepreneurship and the Organisation of Corruption," Centre for Growth and Business Cycle Research Discussion Paper Series 134, Economics, The University of Manchester.
    18. Joura, Essam & Xiao, Qin & Ullah, Subhan, 2021. "The impact of Say-on-Pay votes on firms' strategic policies: Insights from the Anglo-Saxon economy," International Review of Financial Analysis, Elsevier, vol. 73(C).
    19. Jan F. KIVIET & Jerzy NIEMCZYK, 2013. "On the limiting and empirical distributions of IV estimators when some of the instruments are actually endogenous," Economic Growth Centre Working Paper Series 1311, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    20. Bekker, Paul A. & Crudu, Federico, 2012. "Symmetric Jackknife Instrumental Variable Estimation," MPRA Paper 37853, University Library of Munich, Germany.
    21. Kweh, Qian Long & Tebourbi, Imen & Lo, Huai-Chun & Huang, Cheng-Tsu, 2022. "CEO compensation and firm performance: Evidence from financially constrained firms," Research in International Business and Finance, Elsevier, vol. 61(C).
    22. Christl Michael & Köppl-Turyna Monika & Kucsera Dénes, 2018. "Revisiting the Employment Effects of Minimum Wages in Europe," German Economic Review, De Gruyter, vol. 19(4), pages 426-465, December.
    23. Hall, Alastair R. & Han, Sanggohn & Boldea, Otilia, 2008. "Inference regarding multiple structural changes in linear models estimated via two stage least squares," MPRA Paper 9251, University Library of Munich, Germany, revised 20 Jun 2008.
    24. Kazuhiko Hayakawa, 2006. "Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present," Hi-Stat Discussion Paper Series d05-130, Institute of Economic Research, Hitotsubashi University.
    25. Kiviet, Jan F. & Niemczyk, Jerzy, 2007. "The asymptotic and finite sample distributions of OLS and simple IV in simultaneous equations," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3296-3318, April.

  40. Atsushi Inoue & Lutz Kilian, 2002. "Bootstrapping Autoregressive Processes with Possible Unit Roots," Econometrica, Econometric Society, vol. 70(1), pages 377-391, January.
    See citations under working paper version above.
  41. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    See citations under working paper version above.
  42. Christoffersen, Peter & Hahn, Jinyong & Inoue, Atsushi, 2001. "Testing and comparing Value-at-Risk measures," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 325-342, July.
    See citations under working paper version above.
  43. Inoue, Atsushi, 2001. "Testing For Distributional Change In Time Series," Econometric Theory, Cambridge University Press, vol. 17(1), pages 156-187, February.

    Cited by:

    1. Rossi, Barbara & Sekhposyan, Tatevik, 2013. "Conditional predictive density evaluation in the presence of instabilities," Journal of Econometrics, Elsevier, vol. 177(2), pages 199-212.
    2. Bücher, Axel & Kojadinovic, Ivan & Rohmer, Tom & Segers, Johan, 2014. "Detecting changes in cross-sectional dependence in multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 111-128.
    3. 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.
    4. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    5. Barbara Rossi & Tatevik Sekhposyan, 2014. "Alternative tests for correct specification of conditional predictive densities," Economics Working Papers 1416, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2017.
    6. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    7. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    8. 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.
    9. O‐Chia Chuang & Xiaojun Song & Abderrahim Taamouti, 2022. "Testing for Asymmetric Comovements," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1153-1180, October.
    10. Leonie Selk & Natalie Neumeyer, 2013. "Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 770-788, December.
    11. Ulrich Hounyo, 2014. "The wild tapered block bootstrap," CREATES Research Papers 2014-32, Department of Economics and Business Economics, Aarhus University.
    12. Chen, Bin & Hong, Yongmiao, 2012. "Testing For The Markov Property In Time Series," Econometric Theory, Cambridge University Press, vol. 28(1), pages 130-178, February.
    13. 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).
    14. Qu, Zhongjun, 2008. "Testing for structural change in regression quantiles," Journal of Econometrics, Elsevier, vol. 146(1), pages 170-184, September.
    15. Bucchia, Béatrice & Wendler, Martin, 2017. "Change-point detection and bootstrap for Hilbert space valued random fields," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 344-368.
    16. Jonas Dovern & Geoff Kenny, 2020. "Anchoring Inflation Expectations in Unconventional Times: Micro Evidence for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 16(5), pages 309-347, October.
    17. Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
    18. 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.
    19. Valentina Corradi & Norman R. Swanson, 2003. "Bootstrap Conditional Distribution Tests In the Presence of Dynamic Misspecification," Departmental Working Papers 200311, Rutgers University, Department of Economics.
    20. Elena Andreou & Eric Ghysels, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," CIRANO Working Papers 2004s-25, CIRANO.
    21. Corradi, Valentina & Swanson, Norman R., 2005. "Bootstrap specification tests for diffusion processes," Journal of Econometrics, Elsevier, vol. 124(1), pages 117-148, January.
    22. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    23. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
    24. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
    25. Holmes, Mark & Kojadinovic, Ivan & Quessy, Jean-François, 2013. "Nonparametric tests for change-point detection à la Gombay and Horváth," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 16-32.
    26. B. Cooper Boniece & Lajos Horv'ath & Lorenzo Trapani, 2023. "On changepoint detection in functional data using empirical energy distance," Papers 2310.04853, arXiv.org.
    27. Rohmer, Tom, 2016. "Some results on change-point detection in cross-sectional dependence of multivariate data with changes in marginal distributions," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 45-54.
    28. Fabio Busetti, 2012. "On detecting end-of-sample instabilities," Temi di discussione (Economic working papers) 881, Bank of Italy, Economic Research and International Relations Area.
    29. Axel Bücher, 2015. "A Note on Weak Convergence of the Sequential Multivariate Empirical Process Under Strong Mixing," Journal of Theoretical Probability, Springer, vol. 28(3), pages 1028-1037, September.
    30. Su, Liangjun & Xiao, Zhijie, 2008. "Testing for parameter stability in quantile regression models," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2768-2775, November.
    31. Bucher, Axel, 2013. "A note on weak convergence of the sequential multivariate empirical process under strong mixing," LIDAM Discussion Papers ISBA 2013028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    32. Wied, Dominik & Dehling, Herold & van Kampen, Maarten & Vogel, Daniel, 2014. "A fluctuation test for constant Spearman’s rho with nuisance-free limit distribution," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 723-736.
    33. Dominik Wied & Matthias Arnold & Nicolai Bissantz & Daniel Ziggel, 2012. "A new fluctuation test for constant variances with applications to finance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1111-1127, November.
    34. Jean-François Quessy, 2019. "Consistent nonparametric tests for detecting gradual changes in the marginals and the copula of multivariate time series," Statistical Papers, Springer, vol. 60(3), pages 717-746, June.

  44. Inoue, Atsushi, 1999. "Tests of cointegrating rank with a trend-break," Journal of Econometrics, Elsevier, vol. 90(2), pages 215-237, June.

    Cited by:

    1. Perry, L. J. & Wilson, Patrick J., 2004. "Trends in work stoppages : a global perspective," ILO Working Papers 993742343402676, International Labour Organization.
    2. Sébastien Morin, 2004. "Ruptures structurelles sur les marchés action et obligataire américains : preuve empirique à partir de la méthode de Saikkönen," Economie & Prévision, La Documentation Française, vol. 166(5), pages 87-98.
    3. Patrick J. Wilson & Ralf Zurbruegg & Richard Gerlach, 2002. "Structural Breaks and Diversification: The Impact of the 1997 Asian Financial Crisis on the Integration of Asia Pacific Real Estate Markets," ERES eres2002_140, European Real Estate Society (ERES).
    4. Gabriel, Vasco J. & Psaradakis, Zacharias & Sola, Martin, 2002. "A simple method of testing for cointegration subject to multiple regime changes," Economics Letters, Elsevier, vol. 76(2), pages 213-221, July.
    5. Peter Reinhard Hansen, 2000. "Structural Changes in the Cointegrated Vector Autoregressive Model," Working Papers 2000-20, Brown University, Department of Economics.
    6. Harris, David & Leybourne, Stephen J. & Taylor, A.M. Robert, 2016. "Tests of the co-integration rank in VAR models in the presence of a possible break in trend at an unknown point," Journal of Econometrics, Elsevier, vol. 192(2), pages 451-467.
    7. Kose, Nezir & Emirmahmutoglu, Furkan & Aksoy, Sezgin, 2012. "The interest rate–inflation relationship under an inflation targeting regime: The case of Turkey," Journal of Asian Economics, Elsevier, vol. 23(4), pages 476-485.
    8. Kirstin Hubrich & Helmut Lutkepohl & Pentti Saikkonen, 2001. "A Review Of Systems Cointegration Tests," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 247-318.
    9. Skrobotov, Anton, 2021. "Structural breaks in cointegration models: Multivariate case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 83-106.
    10. Rodrigues, Paulo M.M. & Sibbertsen, Philipp & Voges, Michelle, 2019. "Testing for breaks in the cointegrating relationship: On the stability of government bond markets' equilibrium," Hannover Economic Papers (HEP) dp-656, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    11. Perron, Pierre & Zhu, Xiaokang, 2005. "Structural breaks with deterministic and stochastic trends," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 65-119.
    12. Lutkepohl, Helmut & Saikkonen, Pentti & Trenkler, Carsten, 2003. "Comparison of tests for the cointegrating rank of a VAR process with a structural shift," Journal of Econometrics, Elsevier, vol. 113(2), pages 201-229, April.
    13. Zurbruegg, R. & Allsopp, L., 2004. "Purchasing power parity and the impact of the East Asian currency crisis," Journal of Asian Economics, Elsevier, vol. 15(4), pages 739-758, August.
    14. Domenico Sartore & Lucia Trevisan & Michele Trova & Francesca Volo, 2002. "US dollar/Euro exchange rate: a monthly econometric model for forecasting," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 480-501.
    15. Khaled Chnaina & Farid Makhlouf, 2012. "Impact des Transferts de Fonds sur le Taux de Change Réel Effectif en Tunisie," Working papers of CATT hal-01885155, HAL.
    16. David E. Giles & Ryan T. Godwin, 2011. "Testing for Multivariate Cointegration in the Presence of Structural Breaks: p-Values and Critical Values," Econometrics Working Papers 1110, Department of Economics, University of Victoria.
    17. Chee Seng Cheong & Patrick J. Wilson & Ralf Zurbruegg, 2009. "An analysis of the long‐run impact of fixed income and equity market performance on Australian and UK securitised property markets," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 27(3), pages 259-276, April.
    18. Khaled Chnaina & Farid Makhlouf, 2015. "Impact des Transferts de Fonds sur le Taux de Change Réel Effectif en Tunisie," African Development Review, African Development Bank, vol. 27(2), pages 145-160, June.
    19. Cheong, Chee Seng & Gerlach, Richard & Stevenson, Simon & Wilson, Patrick J. & Zurbruegg, Ralf, 2009. "Equity and fixed income markets as drivers of securitised real estate," Review of Financial Economics, Elsevier, vol. 18(2), pages 103-111, April.
    20. Patrick Wilson & Michael White & Neil Dunse & Chee Cheong & Ralf Zurbruegg, 2011. "Modelling Price Movements in Housing Micro Markets," Urban Studies, Urban Studies Journal Limited, vol. 48(9), pages 1853-1874, July.
    21. Vasco J. Gabriel & Martin Sola & Zacharias Psaradakis, 2001. "A simple method for testing cointegration subject to regime changes," NIPE Working Papers 15/2001, NIPE - Universidade do Minho.
    22. Marco GALLEGATI, 2002. "Financial Constraints and the Balance Sheet Channel: a Re-Interpretation," Working Papers 161, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    23. Bent Nielsen, 2000. "Cointegration Analysis in the Presence of Structural Breaks in the Deterministic Trend," Econometric Society World Congress 2000 Contributed Papers 1494, Econometric Society.
    24. Norman-Lόpez, Ana & Pascoe, Sean & Thébaud, Olivier & Van Putten, Ingrid & Innes, James & Jennings, Sarah & Hobday, Alistair & Green, Bridget & Plaganyi, Eva, 2014. "Price integration in the Australian rock lobster industry: implications for management and climate change adaptation," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 58(1), January.
    25. Takamitsu Kurita & Mototsugu Shintani, 2023. "Johansen Test with Fourier-Type Smooth Nonlinear Trends in Cointegrating Relations," CIRJE F-Series CIRJE-F-1216, CIRJE, Faculty of Economics, University of Tokyo.
    26. Salah A. Nusair, 2008. "Purchasing Power Parity under Regime Shifts: An Application to Asian Countries," Asian Economic Journal, East Asian Economic Association, vol. 22(3), pages 241-266, September.
    27. Kurozumi, Eiji & 黒住, 英司 & Arai, Yoichi & 荒井, 洋一, 2005. "Efficient Estimation and Inference in Cointegrating Regressions with Structural Change," Discussion Papers 2004-09, Graduate School of Economics, Hitotsubashi University.
    28. Antonio E. Noriega & Daniel Ventosa-Santaularia, 2012. "The effect of structural breaks on the Engle-Granger test for cointegration," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 27(1), pages 99-132.
    29. Ozdemir, Zeynel Abidin & Cakan, Esin, 2010. "The persistence in real exchange rate: Evidence from East Asian countries," Economic Modelling, Elsevier, vol. 27(5), pages 891-895, September.
    30. Reza Anglingkusumo, 2005. "Stability of the Demand for Real Narrow Money in lndonesia," Tinbergen Institute Discussion Papers 05-051/4, Tinbergen Institute.
    31. Yoichi Arai & Eiji Kurozumi, 2007. "Testing for the Null Hypothesis of Cointegration with a Structural Break," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 705-739.
    32. Solarin, Sakiru Adebola & Ozturk, Ilhan, 2015. "On the causal dynamics between hydroelectricity consumption and economic growth in Latin America countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1857-1868.
    33. Trenkler, Carsten, 2002. "The effects of ignoring level shifts on systems cointegration tests," SFB 373 Discussion Papers 2002,68, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    34. Andrade, Philippe & Bruneau, Catherine & Gregoir, Stephane, 2005. "Testing for the cointegration rank when some cointegrating directions are changing," Journal of Econometrics, Elsevier, vol. 124(2), pages 269-310, February.
    35. Takamitsu Kurita & Bent Nielsen, 2019. "Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms," Econometrics, MDPI, vol. 7(4), pages 1-35, October.
    36. Kim Liow & Zhiwei Chen & Jingran Liu, 2011. "Multiple Regimes and Volatility Transmission in Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 295-328, April.
    37. Sébastien Morin, 2004. "Ruptures structurelles sur les marchés action et obligataire américains : preuve empirique à partir de la méthode de Saikkönen," Économie et Prévision, Programme National Persée, vol. 166(5), pages 87-98.
    38. Razvan Pascalau & Junsoo Lee & Saban Nazlioglu & Yan (Olivia) Lu, 2022. "Johansen‐type cointegration tests with a Fourier function," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 828-852, September.
    39. Eriko Hoshino & Caleb Gardner & Sarah Jennings & Klaas Hartmann, 2015. "Examining the Long-Run Relationship between the Prices of Imported Abalone in Japan," Marine Resource Economics, University of Chicago Press, vol. 30(2), pages 179-192.
    40. Salah A. Nusair & Naser I. Abumustafa, 2012. "Recursive Cointegration Analysis of Purchasing Power Parity: An Application to Asian Countries," The American Economist, Sage Publications, vol. 57(2), pages 196-209, November.
    41. Daiki Maki, 2013. "Detecting cointegration relationships under nonlinear models: Monte Carlo analysis and some applications," Empirical Economics, Springer, vol. 45(1), pages 605-625, August.
    42. Patrick J. Wilson & Ralf Zurbruegg, 2008. "Big City Difference? Another Look at Factors Driving House Prices," Journal of Property Research, Taylor & Francis Journals, vol. 25(2), pages 157-177, November.
    43. Peter Reinhard Hansen, 2000. "Structural Breaks in the Cointegrated Vector Autoregressive Model," Econometric Society World Congress 2000 Contributed Papers 1240, Econometric Society.
    44. Víctor-Hugo Alcalá Ríos & Manuel Gómez Zaldívar & Daniel Ventosa-Santaulà ria, 2011. "Paradoja Feldstein-Horioka: el caso de México (1950-2007)," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 26(2), pages 293-313.
    45. Yoichi Arai & Eiji Kurozumi, 2005. "Testing for the Null Hypothesis of Cointegration with Structural Breaks (Subsequently published in "Econometric Reviews", Volume 26, Issue 6 November 2007, pages 705 - 739. )," CARF F-Series CARF-F-022, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    46. Patrick Wilson & Simon Stevenson & Ralf Zurbruegg, 2007. "Foreign Property Shocks and the Impact on Domestic Securitized Real Estate Markets: An Unobserved Components Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 34(3), pages 407-424, April.
    47. Chen, Cathy W.S. & Gerlach, Richard & Cheng, Nick Y.P. & Yang, Y.L., 2009. "The impact of structural breaks on the integration of the ASEAN-5 stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2654-2664.
    48. Kim, Bong-Han & Kim, Hong-Kee & Oh, Keun-Yeob, 2009. "The purchasing power parity of Southeast Asian currencies: A time-varying coefficient approach," Economic Modelling, Elsevier, vol. 26(1), pages 96-106, January.
    49. Kosei Fukuda, 2011. "Cointegration rank switching model: an application to forecasting interest rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 509-522, August.
    50. Takamitsu Kurita & B. Nielsen, 2018. "Partial cointegrated vector autoregressive models with structural breaks in deterministic terms," Economics Papers 2018-W03, Economics Group, Nuffield College, University of Oxford.

  45. Koehler, Anne & Diebold, Francis X. & Giogianni, Lorenzo & Inoue, Atsushi, 1996. "Software review," International Journal of Forecasting, Elsevier, vol. 12(2), pages 309-315, June.

    Cited by:

    1. Julie Tam & Heather Kirkham, 2000. "Automatic Fiscal Stabilisers: Implications for New Zealand," Treasury Working Paper Series 01/10, New Zealand Treasury, revised 2001.

  46. Yabushita Shiro & Inoue Atsushi, 1993. "The Stability of the Japanese Banking System: A Historical Perspective," Journal of the Japanese and International Economies, Elsevier, vol. 7(4), pages 387-407, December.

    Cited by:

    1. Tetsuji Okazaki & Michiru Sawada, 2012. "Interbank networks in prewar Japan: structure and implications," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 21(2), pages 463-506, April.
    2. Vitols, Sigurt, 2001. "The origins of bank-based and market-based financial systems: Germany, Japan, and the United States," Discussion Papers, Research Unit: Economic Change and Employment FS I 01-302, WZB Berlin Social Science Center.
    3. Konishi, Masaru, 2002. "Bond underwriting by banks and conflicts of interest: Evidence from Japan during the pre-war period," Journal of Banking & Finance, Elsevier, vol. 26(4), pages 767-793, April.
    4. Okazaki, Tetsuji, 2007. "Micro-aspects of monetary policy: Lender of Last Resort and selection of banks in pre-war Japan," Explorations in Economic History, Elsevier, vol. 44(4), pages 657-679, October.
    5. Tetsuji Okazaki & Michiru Sawada & Kazuki Yokoyama, 2003. "Measuring the Extent and Implications of Director Interlocking in the Pre-war Japanese Banking Industry," CIRJE F-Series CIRJE-F-241, CIRJE, Faculty of Economics, University of Tokyo.
    6. Masami Imai & Tetsuji Okazaki & Michiru Sawada, 2019. "The Effects of Lender of Last Resort on Financial Intermediation during the Great Depression in Japan," Wesleyan Economics Working Papers 2019-002, Wesleyan University, Department of Economics.
    7. Yokoyama, Kazuki, 2007. "Too Big to Fail: the Panic of 1927," MPRA Paper 2768, University Library of Munich, Germany.
    8. Konishi, Masaru, 2005. "Bond underwriting syndicates organized by commercial banks: evidence from prewar Japan," Journal of the Japanese and International Economies, Elsevier, vol. 19(3), pages 303-321, September.
    9. Uesugi, Iichiro & Hiraga, Kazuki & Manabe, Masashi & Yoshino, Naoyuki, 2022. "Measuring concentration in the Japanese loan and deposit markets," Japan and the World Economy, Elsevier, vol. 63(C).
    10. Kasuya, Makoto, 2007. "Bond markets and banks in inter-war Japan," LSE Research Online Documents on Economics 6873, London School of Economics and Political Science, LSE Library.
    11. Makoto Kasuya, 2007. "Bond Markets and Banks in Inter-War Japan," STICERD - International Studies Paper Series 521, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

Chapters

  1. Atsushi Inoue & Barbara Rossi, 2018. "The Effects of Conventional and Unconventional Monetary Policy on Exchange Rates," NBER Chapters, in: NBER International Seminar on Macroeconomics 2018, pages 419-447, National Bureau of Economic Research, Inc.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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