Bayesian Dynamic Factor Analysis of a Simple Monetary DSGE Model /
When estimating DSGE models, the number of observable economic variables is usually kept small, and it is conveniently assumed that DSGE model variables are perfectly measured by a single data series. Building upon Boivin and Giannoni (2006), we relax these two assumptions and estimate a fairly simp...
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| Định dạng: | Tạp chí |
| Ngôn ngữ: | English |
| Được phát hành: |
Washington, D.C. :
International Monetary Fund,
2011.
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| Loạt: | IMF Working Papers; Working Paper ;
No. 2011/219 |
| Truy cập trực tuyến: | Full text available on IMF |
| Tóm tắt: | When estimating DSGE models, the number of observable economic variables is usually kept small, and it is conveniently assumed that DSGE model variables are perfectly measured by a single data series. Building upon Boivin and Giannoni (2006), we relax these two assumptions and estimate a fairly simple monetary DSGE model on a richer data set. Using post-1983 U.S.data on real output, inflation, nominal interest rates, measures of inverse money velocity, and a large panel of informational series, we compare the data-rich DSGE model with the regular - few observables, perfect measurement - DSGE model in terms of deep parameter estimates, propagation of monetary policy and technology shocks and sources of business cycle fluctuations. We document that the data-rich DSGE model generates a higher implied duration of Calvo price contracts and a lower slope of the New Keynesian Phillips curve. To reduce the computational costs of the likelihood-based estimation, we employed a novel speedup as in Jungbacker and Koopman (2008) and achieved the time savings of 60 percent. |
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| Mô tả sách: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Mô tả vật lý: | 1 online resource (60 pages) |
| Định dạng: | Mode of access: Internet |
| số ISSN: | 1018-5941 |
| Truy cập: | Electronic access restricted to authorized BRAC University faculty, staff and students |