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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

Author

Listed:
  • 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 of Otrok and Whiteman (1998), the Bayesian state-space approach of Kim 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 from Cesa-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: Eric Hillebrand & Siem Jan Koopman (ed.), Dynamic Factor Models, volume 35, pages 361-400, Emerald Publishing Ltd.
  • Handle: RePEc:eme:aecozz:s0731-905320150000035009
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    1. 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.
    2. 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.
    3. Ambrogio Cesa‐Bianchi & Luis Felipe Cespedes & Alessandro Rebucci, 2015. "Global Liquidity, House Prices, and the Macroeconomy: Evidence from Advanced and Emerging Economies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 301-335, March.
    4. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    5. 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.
    6. 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.
    7. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    8. Mario Forni & Lucrezia Reichlin, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 453-473.
    9. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, 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. Ruiz Ortega, Esther & Rodríguez Caballero, Carlos Vladimir & Gonzalez Rivera, Gloria, 2021. "Expecting the unexpected: economic growth under stress," DES - Working Papers. Statistics and Econometrics. WS 32148, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. 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.
    5. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    6. 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.
    7. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    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. Amy Y. Guisinger & Michael T. Owyang & Daniel Soques, 2020. "Industrial Connectedness and Business Cycle Comovements," Working Papers 2020-052, Federal Reserve Bank of St. Louis.

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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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