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Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis

Author

Listed:
  • Falk Brauning

    (VU University Amsterdam)

  • Siem Jan Koopman

    (VU University Amsterdam)

Abstract

This discussion paper resulted in an article in the International Journal of Forecasting (2014). Volume 30, pages 572-584. We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a dynamic factor model as the data generation process, we provide Monte Carlo evidence for the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a U.S. macroeconomic dataset. The unbalanced panel contain quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our dynamic factor state space analysis can lead to higher forecasting precisions when panel size and time series dimensions are moderate.

Suggested Citation

  • Falk Brauning & Siem Jan Koopman, 2012. "Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis," Tinbergen Institute Discussion Papers 12-042/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20120042
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter, 2014. "Nowcasting and forecasting economic growth in the euro area using principal components," DNB Working Papers 415, Netherlands Central Bank, Research Department.
    2. repec:wly:japmet:v:32:y:2017:i:2:p:296-317 is not listed on IDEAS
    3. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1158-5 is not listed on IDEAS
    4. Samuel Bates & Cheikh Tidiane Ndiaye, 2014. "Economic Growth from a Structural Unobserved Component Modeling: The Case of Senegal," Economics Bulletin, AccessEcon, vol. 34(2), pages 951-965.
    5. repec:eme:aecozz:s0731-905320150000035010 is not listed on IDEAS
    6. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    7. Bernd Schwaab & Siem Jan Koopman & André Lucas, 2017. "Global Credit Risk: World, Country and Industry Factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 296-317, March.
    8. repec:spr:laecrv:v:26:y:2017:i:1:d:10.1007_s40503-017-0044-7 is not listed on IDEAS
    9. Bragoli, Daniela, 2017. "Now-casting the Japanese economy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 390-402.
    10. Matteo Luciani & Madhavi Pundit & Arief Ramayandi & Giovanni Veronese, 2018. "Nowcasting Indonesia," Empirical Economics, Springer, vol. 55(2), pages 597-619, September.
    11. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Brave, Scott & Butters, R. Andrew, 2014. "Nowcasting Using the Chicago Fed National Activity Index," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 19-37.
    13. Wanger, Susanne & Weigand, Roland & Zapf, Ines, 2014. "Revision der IAB-Arbeitszeitrechnung 2014 : Grundlagen, methodische Weiterentwicklungen sowie ausgewählte Ergebnisse im Rahmen der Revision der Volkswirtschaftlichen Gesamtrechnungen," IAB-Forschungsbericht 201409, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    14. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    15. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
    16. repec:vrs:offsta:v:34:y:2018:i:1:p:265-301:n:12 is not listed on IDEAS
    17. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    18. 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 Publishing Ltd.
    19. Noordegraaf-Eelens, L.H.J. & Franses, Ph.H.B.F., 2014. "Do loss profiles on the mortgage market resonate with changes in macro economic prospects, business cycle movements or policy measures?," Econometric Institute Research Papers EI 2014-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. repec:spr:jbuscr:v:14:y:2018:i:1:d:10.1007_s41549-018-0027-z is not listed on IDEAS
    21. Weigand Roland & Wanger Susanne & Zapf Ines, 2018. "Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany," Journal of Official Statistics, Sciendo, vol. 34(1), pages 265-301, March.
    22. Borus Jungbacker & Siem Jan Koopman, 2008. "Likelihood-based Analysis for Dynamic Factor Models," Tinbergen Institute Discussion Papers 08-007/4, Tinbergen Institute, revised 20 Mar 2014.
    23. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.

    More about this item

    Keywords

    Kalman filter; Mixed frequency; Nowcasting; Principal components; State space model; Unobserved Components Time Series Model;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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