Data-Rich DSGE and Dynamic Factor Models /
Dynamic factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics. The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common unobserved...
| Autor principal: | |
|---|---|
| Format: | Revista |
| Idioma: | English |
| Publicat: |
Washington, D.C. :
International Monetary Fund,
2011.
|
| Col·lecció: | IMF Working Papers; Working Paper ;
No. 2011/216 |
| Accés en línia: | Full text available on IMF |
| Sumari: | Dynamic factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics. The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common unobserved factors. Similarly, the dynamics in DSGE models are often governed by a handful of state variables and exogenous processes such as preference and/or technology shocks. Boivin and Giannoni(2006) combine a DSGE and a factor model into a data-rich DSGE model, in which DSGE states are factors and factor dynamics are subject to DSGE model implied restrictions. We compare a data-richDSGE model with a standard New Keynesian core to an empirical dynamic factor model by estimating both on a rich panel of U.S. macroeconomic and financial data compiled by Stock and Watson (2008).We find that the spaces spanned by the empirical factors and by the data-rich DSGE model states are very close. This proximity allows us to propagate monetary policy and technology innovations in an otherwise non-structural dynamic factor model to obtain predictions for many more series than just a handful of traditional macro variables, including measures of real activity, price indices, labor market indicators, interest rate spreads, money and credit stocks, and exchange rates. |
|---|---|
| Descripció de l’ítem: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Descripció física: | 1 online resource (49 pages) |
| Format: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Accés: | Electronic access restricted to authorized BRAC University faculty, staff and students |