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Lasse Bork

Personal Details

First Name:Lasse
Middle Name:
Last Name:Bork
Suffix:
RePEc Short-ID:pbo460
[This author has chosen not to make the email address public]
http://lassebork.dk
Aalborg University Department of Business and Management http://personprofil.aau.dk/profil/123645?lang=en Fibigerstraede 2 DK-9220 Aalborg East T: +45 9940 2707

Affiliation

Institut for Økonomi og Ledelse
Aalborg Universitet

Aalborg, Denmark
http://www.business.aau.dk/
RePEc:edi:ieaucdk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Lasse Bork & Stig V. Møller & Thomas Q. Pedersen, 2016. "A New Index of Housing Sentiment," CREATES Research Papers 2016-32, Department of Economics and Business Economics, Aarhus University.
  2. Lasse Bork & Stig V. Møller, 2012. "Housing price forecastability: A factor analysis," CREATES Research Papers 2012-27, Department of Economics and Business Economics, Aarhus University.
  3. Lasse Bork & Hans Dewachter & Romain Houssa, 2009. "Identification of Macroeconomic Factors in Large Panels," CREATES Research Papers 2009-43, Department of Economics and Business Economics, Aarhus University.
  4. Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, Department of Economics and Business Economics, Aarhus University.

Articles

  1. Bork, Lasse & Kaltwasser, Pablo Rovira & Sercu, Piet, 2022. "Aggregation bias in tests of the commodity currency hypothesis," Journal of Banking & Finance, Elsevier, vol. 135(C).
  2. Lasse Bork & Stig V. Møller & Thomas Q. Pedersen, 2020. "A New Index of Housing Sentiment," Management Science, INFORMS, vol. 66(4), pages 1563-1583, April.
  3. Lasse Bork & Stig V. Møller, 2018. "Housing Price Forecastability: A Factor Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 46(3), pages 582-611, September.
  4. Bork, Lasse & Møller, Stig V., 2015. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection," International Journal of Forecasting, Elsevier, vol. 31(1), pages 63-78.

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.

Working papers

  1. Lasse Bork & Stig V. Møller & Thomas Q. Pedersen, 2016. "A New Index of Housing Sentiment," CREATES Research Papers 2016-32, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Timmermann, Allan & Møller, Stig & Pedersen, Thomas & Schütte, Erik Christian Montes, 2021. "Search and Predictability of Prices in the Housing Market," CEPR Discussion Papers 15875, C.E.P.R. Discussion Papers.
    2. Rangan Gupta & Chi Keung Marco Lau & Vasilios Plakandaras & Wing-Keung Wong, 2018. "The Role of Housing Sentiment in Forecasting US Home Sales Growth: Evidence from a Bayesian Compressed Vector Autoregressive Model," Working Papers 201842, University of Pretoria, Department of Economics.
    3. Luiz Renato Lima & Lucas Lúcio Godeiro, 2023. "Equity‐premium prediction: Attention is all you need," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 105-122, January.
    4. Gupta, Rangan & Ma, Jun & Theodoridis, Konstantinos & Wohar, Mark E., 2023. "Is there a national housing market bubble brewing in the United States?," Macroeconomic Dynamics, Cambridge University Press, vol. 27(8), pages 2191-2228, December.
    5. Jinwoong Lee, 2024. "What factors drive house prices in the USA? Sign restricted VAR approach," Empirical Economics, Springer, vol. 66(6), pages 2533-2556, June.
    6. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2020. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," Working Papers 202077, University of Pretoria, Department of Economics.
    7. Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
    8. Bouras, Christos & Christou, Christina & Gupta, Rangan & Lesame, Keagile, 2023. "Forecasting state- and MSA-level housing returns of the US: The role of mortgage default risks," Research in International Business and Finance, Elsevier, vol. 65(C).
    9. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    10. Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
    11. Shao, Jin & Hong, Jingke & Wang, Xianzhu & Yan, Xiaochen, 2023. "The relationship between social media sentiment and house prices in China: Evidence from text mining and wavelet analysis," Finance Research Letters, Elsevier, vol. 57(C).
    12. Schmeling, Maik & Schrimpf, Andreas & Steffensen, Sigurd A.M., 2022. "Monetary policy expectation errors," Journal of Financial Economics, Elsevier, vol. 146(3), pages 841-858.
    13. Petre Caraiani & Rangan Gupta & Chi Keung Marco Lau & Hardik A. Marfatia, 2019. "Effects of Conventional and Unconventional Monetary Policy Shocks on Housing Prices in the United States: The Role of Sentiment," Working Papers 201953, University of Pretoria, Department of Economics.
    14. André, Christophe & Caraiani, Petre & Călin, Adrian Cantemir & Gupta, Rangan, 2022. "Can monetary policy lean against housing bubbles?," Economic Modelling, Elsevier, vol. 110(C).
    15. Erdinc Akyildirim & Ahmet Faruk Aysan & Oguzhan Cepni & Özge Serbest, 2024. "Sentiment matters: the effect of news-media on spillovers among cryptocurrency returns," The European Journal of Finance, Taylor & Francis Journals, vol. 30(14), pages 1577-1613, September.
    16. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy, 2019. "Time-varying impact of uncertainty shocks on the US housing market," Economics Letters, Elsevier, vol. 180(C), pages 15-20.
    17. Biktimirov, Ernest N. & Sokolyk, Tatyana & Ayanso, Anteneh, 2024. "What is behind housing sentiment?," Finance Research Letters, Elsevier, vol. 60(C).
    18. Biktimirov, Ernest N. & Sokolyk, Tatyana & Ayanso, Anteneh, 2024. "Unpacking the relation between media sentiment and house prices: A topic modeling approach," Journal of Housing Economics, Elsevier, vol. 66(C).
    19. Shulin Shen & Yiyi Zhao & Jindong Pang, 2024. "Local Housing Market Sentiments and Returns: Evidence from China," The Journal of Real Estate Finance and Economics, Springer, vol. 68(3), pages 488-522, April.
    20. Bauer, Gregory H., 2017. "International house price cycles, monetary policy and credit," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 88-114.
    21. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.
    22. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
    23. Christophe Andre & David Gabauer & Rangan Gupta, 2020. "Time-Varying Spillovers between Housing Sentiment and Housing Market in the United States," Working Papers 202091, University of Pretoria, Department of Economics.
    24. André, Christophe & Gabauer, David & Gupta, Rangan, 2021. "Time-varying spillovers between housing sentiment and housing market in the United States☆," Finance Research Letters, Elsevier, vol. 42(C).
    25. Bro, Jeppe & Eriksen, Jonas N., 2025. "Subjective expectations and house prices," Journal of Banking & Finance, Elsevier, vol. 172(C).
    26. Hardik A. Marfatia & Christophe Andre & Rangan Gupta, 2020. "Predicting Housing Market Sentiment: The Role of Financial, Macroeconomic and Real Estate Uncertainties," Working Papers 202061, University of Pretoria, Department of Economics.
    27. Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting Growth-at-Risk of the United States: Housing Price versus Housing Sentiment or Attention," Working Papers 202401, University of Pretoria, Department of Economics.
    28. Gong, Xue & Zhang, Weiguo & Wang, Junbo & Wang, Chao, 2022. "Investor sentiment and stock volatility: New evidence," International Review of Financial Analysis, Elsevier, vol. 80(C).

  2. Lasse Bork & Stig V. Møller, 2012. "Housing price forecastability: A factor analysis," CREATES Research Papers 2012-27, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Mark E. Wohar, 2020. "Mortgage Default Risks and High-Frequency Predictability of the U.S. Housing Market: A Reconsideration," Journal of Real Estate Portfolio Management, Taylor & Francis Journals, vol. 26(2), pages 111-117, December.
    2. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    3. Timmermann, Allan & Møller, Stig & Pedersen, Thomas & Schütte, Erik Christian Montes, 2021. "Search and Predictability of Prices in the Housing Market," CEPR Discussion Papers 15875, C.E.P.R. Discussion Papers.
    4. Kucharska-Stasiak Ewa, 2019. "Valuation Schools and the Evolution of the Income Approach. An Evaluation of Change Trends," Real Estate Management and Valuation, Sciendo, vol. 27(2), pages 66-76, June.
    5. Bork, Lasse & Møller, Stig V., 2015. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection," International Journal of Forecasting, Elsevier, vol. 31(1), pages 63-78.
    6. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    7. Theodore Panagiotidis & Panagiotis Printzis, 2016. "On the macroeconomic determinants of the housing market in Greece: a VECM approach," International Economics and Economic Policy, Springer, vol. 13(3), pages 387-409, July.
    8. Paul E. Carrillo & Erik Robert De Wit & William D. Larson, 2012. "Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the U.S. and the Netherlands," Working Papers 2012-11, The George Washington University, Institute for International Economic Policy.
    9. Christina Christou & Rangan Gupta & Christis Hassapis, 2016. "Does Economic Policy Uncertainty Forecast Real Housing Returns in a Panel of OECD Countries? A Bayesian Approach," Working Papers 201637, University of Pretoria, Department of Economics.
    10. Tripathi, Sabyasachi, 2019. "Macroeconomic Determinants of Housing Prices: A Cross Country Level Analysis," MPRA Paper 98089, University Library of Munich, Germany.
    11. Taufiq Choudhry, 2020. "Economic Policy Uncertainty and House Prices: Evidence from Geographical Regions of England and Wales," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(2), pages 504-529, June.
    12. Osman Dou{g}an & Raffaele Mattera & Philipp Otto & Suleyman Tac{s}p{i}nar, 2024. "A Dynamic Spatiotemporal and Network ARCH Model with Common Factors," Papers 2410.16526, arXiv.org.
    13. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    14. Paul E. Carrillo & Eric R. Wit & William Larson, 2015. "Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the United States and the Netherlands," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(3), pages 609-651, September.

  3. Lasse Bork & Hans Dewachter & Romain Houssa, 2009. "Identification of Macroeconomic Factors in Large Panels," CREATES Research Papers 2009-43, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Bajraj, Gent & Lorca, Jorge & Wlasiuk, Juan M., 2023. "On foreign drivers of emerging markets fluctuations," Economic Modelling, Elsevier, vol. 129(C).
    2. Fulvio Pegoraro & Siegel, A. F. & Tiozzo Pezzoli, L., 2014. "Specification Analysis of International Treasury Yield Curve Factors," Working papers 490, Banque de France.
    3. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    4. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    5. Antonello D'Agostino & Michele Modugno & Chiara Osbat, 2015. "A Global Trade Model for the Euro Area," Finance and Economics Discussion Series 2015-13, Board of Governors of the Federal Reserve System (U.S.).
    6. Jorge Fornero & Markus Kirchner & Carlos Molina, 2021. "Estimating Shadow Policy Rates in a Small Open Economy and the Role of Foreign Factors," Working Papers Central Bank of Chile 915, Central Bank of Chile.
    7. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    8. Franz Ramsauer & Aleksey Min & Michael Lingauer, 2019. "Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components," Econometrics, MDPI, vol. 7(3), pages 1-43, July.
    9. Piyachart Phiromswad & Takeshi Yagihashi, 2016. "Empirical identification of factor models," Empirical Economics, Springer, vol. 51(2), pages 621-658, September.
    10. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    11. Riccardo (Jack) Lucchetti & Ioannis A. Venetis, 2019. "Dynamic Factor Models in gretl. The DFM package," gretl working papers 7, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    12. Monica Defend & Aleksey Min & Lorenzo Portelli & Franz Ramsauer & Francesco Sandrini & Rudi Zagst, 2021. "Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data," Forecasting, MDPI, vol. 3(1), pages 1-35, February.

  4. Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Lasse BORK & Hans DEWACHTER & Romain HOUSSA, 2009. "Identification of macroeconomic factors in large panels," Working Papers of Department of Economics, Leuven ces09.18, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    2. Antonello D'Agostino & Michele Modugno & Chiara Osbat, 2015. "A Global Trade Model for the Euro Area," Finance and Economics Discussion Series 2015-13, Board of Governors of the Federal Reserve System (U.S.).
    3. Jorge Fornero & Markus Kirchner & Carlos Molina, 2021. "Estimating Shadow Policy Rates in a Small Open Economy and the Role of Foreign Factors," Working Papers Central Bank of Chile 915, Central Bank of Chile.
    4. Andrés Felipe Londono & Jorge Andrés Tamayo & Carlos Alberto Velásquez, 2012. "Dinámica de la política monetaria e inflación objetivo en Colombia: una aproximación FAVAR," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 30(68), pages 14-71, June.
    5. Eugen Ivanov & Aleksey Min & Franz Ramsauer, 2017. "Copula-Based Factor Models for Multivariate Asset Returns," Econometrics, MDPI, vol. 5(2), pages 1-24, May.
    6. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    7. Kemal Bagzibagli, 2012. "Monetary Transmission Mechanism and Time Variation in the Euro Area," Discussion Papers 12-12, Department of Economics, University of Birmingham.
    8. Franz Ramsauer & Aleksey Min & Michael Lingauer, 2019. "Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components," Econometrics, MDPI, vol. 7(3), pages 1-43, July.
    9. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    10. Riccardo (Jack) Lucchetti & Ioannis A. Venetis, 2019. "Dynamic Factor Models in gretl. The DFM package," gretl working papers 7, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    11. André Binette & Tony Chernis & Daniel de Munnik, 2017. "Global Real Activity for Canadian Exports: GRACE," Discussion Papers 17-2, Bank of Canada.
    12. Danilo Vassallo & Giacomo Bormetti & Fabrizio Lillo, 2019. "A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics," Papers 1910.01407, arXiv.org, revised Sep 2020.
    13. Hacioglu, Sinem & Tuzcuoglu, Kerem, 2016. "Interpreting the latent dynamic factors by threshold FAVAR model," Bank of England working papers 622, Bank of England.

Articles

  1. Bork, Lasse & Kaltwasser, Pablo Rovira & Sercu, Piet, 2022. "Aggregation bias in tests of the commodity currency hypothesis," Journal of Banking & Finance, Elsevier, vol. 135(C).

    Cited by:

    1. Kakran, Shubham & Bajaj, Parminder Kaur & Pandey, Dharen Kumar & Kumar, Ashish, 2025. "Interconnectedness and return spillover among APEC currency exchange rates: A time-frequency analysis," Research in International Business and Finance, Elsevier, vol. 73(PA).
    2. Markos Farag, Stephen Snudden, Greg Upton, 2024. "Can Futures Prices Predict the Real Price of Primary Commodities?," LCERPA Working Papers jc0145, Laurier Centre for Economic Research and Policy Analysis, revised 2024.
    3. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    4. Huang, Jionghao & Li, Hongqiao & Chen, Baifan & Liu, Mengai & An, Chaofan & Xia, Xiaohua, 2025. "Revisiting the currency-commodity nexus: New insights into the R2 decomposed connectedness and the role of global shocks," International Review of Economics & Finance, Elsevier, vol. 98(C).
    5. Albrecht, Peter & Kočenda, Evžen & de Oliveira, Alexandre Silva & Ceretta, Paulo Sergio & Drábek, Michal, 2025. "Event-driven changes in connectedness among commodities and commodity currencies: A quantile, network and probabilistic analysis," Research in International Business and Finance, Elsevier, vol. 75(C).
    6. Ellwanger, Reinhard & Snudden, Stephen, 2023. "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, vol. 154(C).
    7. Li, Kaixin & Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil returns with oil-related industry ESG indices," Journal of Commodity Markets, Elsevier, vol. 36(C).

  2. Lasse Bork & Stig V. Møller & Thomas Q. Pedersen, 2020. "A New Index of Housing Sentiment," Management Science, INFORMS, vol. 66(4), pages 1563-1583, April.
    See citations under working paper version above.
  3. Lasse Bork & Stig V. Møller, 2018. "Housing Price Forecastability: A Factor Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 46(3), pages 582-611, September.
    See citations under working paper version above.
  4. Bork, Lasse & Møller, Stig V., 2015. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection," International Journal of Forecasting, Elsevier, vol. 31(1), pages 63-78.

    Cited by:

    1. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Mark E. Wohar, 2020. "Mortgage Default Risks and High-Frequency Predictability of the U.S. Housing Market: A Reconsideration," Journal of Real Estate Portfolio Management, Taylor & Francis Journals, vol. 26(2), pages 111-117, December.
    2. Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2019. "High-Frequency Volatility Forecasting of US Housing Markets," Working Papers 201977, University of Pretoria, Department of Economics.
    3. Robert A. Hill & Paulo M. M. Rodrigues, 2022. "Forgetting approaches to improve forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1356-1371, November.
    4. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy & Wohar, Mark E., 2018. "Do house prices hedge inflation in the US? A quantile cointegration approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 15-26.
    5. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    6. Jan Prüser, 2019. "Adaptive learning from model space," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 29-38, January.
    7. Gupta, Rangan & Ma, Jun & Theodoridis, Konstantinos & Wohar, Mark E., 2023. "Is there a national housing market bubble brewing in the United States?," Macroeconomic Dynamics, Cambridge University Press, vol. 27(8), pages 2191-2228, December.
    8. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2020. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," Working Papers 202077, University of Pretoria, Department of Economics.
    9. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
    10. Bouras, Christos & Christou, Christina & Gupta, Rangan & Lesame, Keagile, 2023. "Forecasting state- and MSA-level housing returns of the US: The role of mortgage default risks," Research in International Business and Finance, Elsevier, vol. 65(C).
    11. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    12. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    13. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    14. Paulo M.M. Rodrigues & Rita Fradique Lourenço & Robert Hill, 2020. "House price forecasting and uncertainty: Examining Portugal and Spain," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    15. 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.
    16. Piotr Dybka & Bartosz Olesiński & Marek Rozkrut & Andrzej Torój, 2020. "Measuring the uncertainty of shadow economy estimates using Bayesian and frequentist model averaging," KAE Working Papers 2020-046, Warsaw School of Economics, Collegium of Economic Analysis.
    17. Dimitrios Sarris & Evangelos Spiliotis & Vassilios Assimakopoulos, 2020. "Exploiting resampling techniques for model selection in forecasting: an empirical evaluation using out-of-sample tests," Operational Research, Springer, vol. 20(2), pages 701-721, June.
    18. Cheng, Xian & Wu, Peng & Liao, Stephen Shaoyi & Wang, Xuelian, 2023. "An integrated model for crude oil forecasting: Causality assessment and technical efficiency," Energy Economics, Elsevier, vol. 117(C).
    19. Piotr Dybka & Bartosz Olesiński & Marek Rozkrut & Andrzej Torój, 2023. "Measuring the model uncertainty of shadow economy estimates," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(4), pages 1069-1106, August.
    20. Meng, Fanyi & Liu, Li, 2019. "Analyzing the economic sources of oil price volatility: An out-of-sample perspective," Energy, Elsevier, vol. 177(C), pages 476-486.
    21. Tim Meyer, 2019. "On the Directional Accuracy of United States Housing Starts Forecasts: Evidence from Survey Data," The Journal of Real Estate Finance and Economics, Springer, vol. 58(3), pages 457-488, April.
    22. Mirko S. Bozanic‐Leal & Marcel Goic & Charles Thraves, 2025. "Affinities and Complementarities of Methods and Information Sets in the Estimation of Prices in Real Estate Markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 356-375, March.
    23. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    24. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    25. Piotr Dybka, 2020. "One model or many? Exchange rates determinants and their predictive capabilities," KAE Working Papers 2020-053, Warsaw School of Economics, Collegium of Economic Analysis.
    26. Wang, TianTian & Zhang, Dayong & Clive Broadstock, David, 2019. "Financialization, fundamentals, and the time-varying determinants of US natural gas prices," Energy Economics, Elsevier, vol. 80(C), pages 707-719.
    27. Laurynas Narusevicius & Tomas Ramanauskas & Laura Gudauskaitė & Tomas Reichenbachas, 2019. "Lithuanian house price index: modelling and forecasting," Bank of Lithuania Occasional Paper Series 28, Bank of Lithuania.
    28. Lin, Boqiang & Su, Tong, 2021. "Do China's macro-financial factors determine the Shanghai crude oil futures market?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    29. Yusupova, Alisa & Pavlidis, Nicos G. & Pavlidis, Efthymios G., 2023. "Dynamic linear models with adaptive discounting," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1925-1944.
    30. Christina Christou & Rangan Gupta & Christis Hassapis, 2016. "Does Economic Policy Uncertainty Forecast Real Housing Returns in a Panel of OECD Countries? A Bayesian Approach," Working Papers 201637, University of Pretoria, Department of Economics.
    31. Wei, Yu & Cao, Yang, 2017. "Forecasting house prices using dynamic model averaging approach: Evidence from China," Economic Modelling, Elsevier, vol. 61(C), pages 147-155.
    32. Rangan Gupta & Chi Keung Marco Lau & Wendy Nyakabawo, 2018. "Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment," Working Papers 201866, University of Pretoria, Department of Economics.
    33. Sakar Hasan Hamza & Qingna Li, 2023. "The Dynamics of US Gasoline Demand and Its Prediction: An Extended Dynamic Model Averaging Approach," Energies, MDPI, vol. 16(12), pages 1-13, June.
    34. Abdullah Sultan Al Shammre & Benaissa Chidmi, 2023. "Oil Price Forecasting Using FRED Data: A Comparison between Some Alternative Models," Energies, MDPI, vol. 16(11), pages 1-24, May.
    35. Graefe, Andreas & Küchenhoff, Helmut & Stierle, Veronika & Riedl, Bernhard, 2015. "Limitations of Ensemble Bayesian Model Averaging for forecasting social science problems," International Journal of Forecasting, Elsevier, vol. 31(3), pages 943-951.
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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 6 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-MAC: Macroeconomics (5) 2009-03-22 2009-04-25 2009-10-10 2010-04-17 2016-11-20. Author is listed
  2. NEP-CBA: Central Banking (3) 2009-03-22 2009-04-25 2009-10-10
  3. NEP-URE: Urban and Real Estate Economics (2) 2012-06-13 2016-11-20
  4. NEP-BEC: Business Economics (1) 2009-10-10
  5. NEP-ECM: Econometrics (1) 2009-10-10
  6. NEP-ETS: Econometric Time Series (1) 2009-10-10
  7. NEP-FOR: Forecasting (1) 2012-06-13

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