| Description |
1 online resource (1 electronic resource (49 pages)) : color illustrations |
| Series |
IMF working paper, 1934-7073 ; WP/11/216 |
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IMF working paper ; WP/11/216.
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| Contents |
Cover Page; Title Page; Copyright Page; Contents; I. Introduction; II. Two models; A. Dynamic factor model); B. Data-rich dsge model; III. Econometric methodology; A. Estimation of the data-rich dsge model; B. Estimation of the dynamic factor model; IV. Data; V. Empirical analysis; A. Priors and posteriors; B. Empirical factors and estimated dsge model states; C. How well factors trace data; D. Comparing factor spaces; E. Propagation of monetary policy and technology innovations; VI. Conclusions; Appendix A. DFM: Gibbs sampler: drawing transition equation matrix |
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Appendix B. DATA: Description and transformationsAppendix C. Tables and figures; Figure C1. DFM: Principal components analysis; Table C1. DFM: Principal components analysis; Table C2. Pure DFM: Fraction of unconditional variance captured by factors; Table C3. Data-Rich DSGE model: Fraction of unconditional variance captured by dsge model states); Table C4. Pure DFM: Unconditional variance captured by factors; Table C5. Data-rich dsge model: fraction of unconditional variance captured by dsge model states; Table C6. Regressing data-rich dsge model states on dfm factors |
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Table C7. Regressing dfm factors on data-rich dsge model statesFigure C2. Data-rich dsge model (iid errors): estimated model states; Figure C3. Pure dfm (iid errors): estimated factors; Figure C4. Do empirical factors and dsge model state variables span the same space?; Figure C5. Impact of monetary policy innovation on core macro series; Figure C6. Impact of monetary policy innovation on non-core macro series; Figure C7. Impact of technology innovation on core macro series; Figure C8. Impact of technology innovation on non-core macro series; References; Footnotes |
| Summary |
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-rich DSGE 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 |
| Notes |
At head of title: IMF Institute |
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Title from PDF title page (IMF, viewed Jan. 10, 2011) |
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"September 2011." |
| Bibliography |
Includes bibliographical references (pages 46-49) |
| Subject |
Macroeconomics -- Econometric models.
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Equilibrium (Economics) -- Econometric models
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Equilibrium (Economics) -- Econometric models
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Macroeconomics -- Econometric models
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| Form |
Electronic book
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| Author |
IMF Institute, issuing body.
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| ISBN |
128356565X |
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9781283565653 |
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9781463916602 |
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1463916604 |
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