Abstract
Starting from the dynamic factor model for non-stationary data we derive the
factor-augmented error correction model (FECM) and its moving-average representation.The latter is used for the identication of structural shocks and their propagation mechanisms. We show how to implement classical identication schemes based on long-run restrictions in the case of large panels. The importance of the error-correction mechanism for impulse response analysis is analysed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a FAVAR model is positively related to the strength of the error-correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identied permanent real (productivity) and monetary policy shocks.
factor-augmented error correction model (FECM) and its moving-average representation.The latter is used for the identication of structural shocks and their propagation mechanisms. We show how to implement classical identication schemes based on long-run restrictions in the case of large panels. The importance of the error-correction mechanism for impulse response analysis is analysed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a FAVAR model is positively related to the strength of the error-correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identied permanent real (productivity) and monetary policy shocks.
Original language | English |
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Pages (from-to) | 1069-1086 |
Number of pages | 18 |
Journal | Journal of Applied Econometrics |
Volume | 32 |
Issue number | 6 |
Early online date | 3 May 2017 |
DOIs | |
Publication status | Published - 1 Sept 2017 |
Keywords
- Dynamic Factor Models
- Cointegration
- Structural Analysis
- Factor-augmented Error Correction Models
- FAVAR