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Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis

Falk Bräuning and Siem Jan Koopman ()

No 12-042/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: Kalman filter, Mixed frequency; Nowcasting, Principal components, State space model, Unobserved Components Time Series Model (search for similar items in EconPapers)
JEL-codes: C33 C53 E17 (search for similar items in EconPapers)
Date: 2012-04-20
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