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Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement

In: Dynamic Factor Models

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  • Laura E. Jackson
  • M. Ayhan Kose
  • Christopher Otrok
  • Michael T. Owyang

Abstract

We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single-factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach ofOtrok and Whiteman (1998), the Bayesian state-space approach ofKim and Nelson (1998)and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets fromCesa-Bianchi, Cespedes, and Rebucci (2015)and interpret the empirical results in light of the Monte Carlo results.

Suggested Citation

  • Laura E. Jackson & M. Ayhan Kose & Christopher Otrok & Michael T. Owyang, 2016. "Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 361-400, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320150000035009
    DOI: 10.1108/S0731-905320150000035009
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    1. Danny Quah & Thomas J. Sargent, 1993. "A Dynamic Index Model for Large Cross Sections," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 285-310, National Bureau of Economic Research, Inc.
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    3. Hideaki Hirata & M. Ayhan Kose & Christopher Otrok & Marco E Terrones, 2013. "Global House Price Fluctuations: Synchronization and Determinants," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 9(1), pages 119-166.
    4. Otrok, Christopher & Whiteman, Charles H, 1998. "Bayesian Leading Indicators: Measuring and Predicting Economic Conditions in Iowa," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 997-1014, November.
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    9. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    10. Jungbacker, B. & Koopman, S.J. & van der Wel, M., 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1358-1368, August.
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    Cited by:

    1. Jackson, Laura E. & Owyang, Michael T. & Zubairy, Sarah, 2018. "Debt and stabilization policy: Evidence from a Euro Area FAVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 67-91.
    2. Shiyi Wang, 2019. "Capital Flow Volatility: The Effects of Financial Development and Global Financial Conditions," 2019 Papers pwa945, Job Market Papers.
    3. Petre Caraiani & Adrian Cantemir Călin, 2020. "Housing markets, monetary policy, and the international co‐movement of housing bubbles," Review of International Economics, Wiley Blackwell, vol. 28(2), pages 365-375, May.
    4. Shambaugh, Jay C. & Zhou, Hang, 2024. "Interest rates across the world: Global, regional, and idiosyncratic factors," Journal of Banking & Finance, Elsevier, vol. 163(C).
    5. Raphael A. Auer & Andrei A. Levchenko & Philip Sauré, 2019. "International Inflation Spillovers through Input Linkages," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 507-521, July.
    6. Beetsma, Roel & Cimadomo, Jacopo & van Spronsen, Josha, 2024. "One scheme fits all: A central fiscal capacity for the EMU targeting eurozone, national and regional shocks," European Economic Review, Elsevier, vol. 165(C).
    7. Krzysztof Beck & Piotr Stanek, 2019. "Globalization or Regionalization of Stock Markets? the Case of Central and Eastern European Countries," Eastern European Economics, Taylor & Francis Journals, vol. 57(4), pages 317-330, July.
    8. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    9. Klarl, Torben, 2018. "Housing is local: Applying a dynamic unobserved factor model for the Dutch housing market," Economics Letters, Elsevier, vol. 170(C), pages 79-84.
    10. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    11. Gonzalez Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2021. "Expecting the unexpected: economic growth under stress," DES - Working Papers. Statistics and Econometrics. WS 32148, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Xufeng Jiang & Zelu Jia & Lefei Li & Tianhong Zhao, 2022. "Understanding Housing Prices Using Geographic Big Data: A Case Study in Shenzhen," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    13. Harrison, James M., 2023. "Exploring 200 years of U.S. commodity market integration: A structural time series model approach," Explorations in Economic History, Elsevier, vol. 88(C).
    14. Krzysztof Beck & Karen Jackson, 2024. "International trade fluctuations: Global versus regional factors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(1), pages 331-358, February.
    15. Beck, Krzysztof, 2021. "Why business cycles diverge? Structural evidence from the European Union," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    16. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.
    17. Guisinger, Amy Y. & Owyang, Michael T. & Soques, Daniel, 2024. "Industrial Connectedness and Business Cycle Comovements," Econometrics and Statistics, Elsevier, vol. 29(C), pages 132-149.
    18. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.

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    More about this item

    Keywords

    Principal components; Kalman filter; data augmentation; business cycles; C3; C18; C32; E32;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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