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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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References

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