Arbeitspapier
Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 12-042/4
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Forecasting Models; Simulation Methods
General Aggregative Models: Forecasting and Simulation: Models and Applications
- Thema
-
Kalman filter
Mixed frequency
Nowcasting
Principal components
State space model
Unobserved Components Time Series Model
Wirtschaftsindikator
Prognoseverfahren
Zustandsraummodell
Monte-Carlo-Methode
Schätzung
USA
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Brauning, Falk
Koopman, Siem Jan
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2012
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Brauning, Falk
- Koopman, Siem Jan
- Tinbergen Institute
Entstanden
- 2012
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