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Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis

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Author Info

  • Falk Brauning

    (VU University Amsterdam)

  • Siem Jan Koopman

    (VU University Amsterdam)

Abstract

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.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 12-042/4.

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Date of creation: 20 Apr 2012
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Handle: RePEc:dgr:uvatin:20120042

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Web page: http://www.tinbergen.nl

Related research

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

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Cited by:
  1. 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.
  2. Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter, 2014. "Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components," Tinbergen Institute Discussion Papers, Tinbergen Institute 140113/III, Tinbergen Institute.
  3. Brave, Scott & Butters, R. Andrew, 2014. "Nowcasting Using the Chicago Fed National Activity Index," Economic Perspectives, Federal Reserve Bank of Chicago, Federal Reserve Bank of Chicago, issue Q I, pages 19-37.
  4. 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, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute EI 2014-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  5. 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, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany] 201409, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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