Mixed frequency structural models: estimation, and policy analysis
AbstractIn this paper we show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE model and that of the time series data used for its estimation generally creates identfication problems, introduces estimation bias and distorts the results of policy analysis. On the constructive side, we prove that the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identfication issues, and yield more reliable policy conclusions. The problems and possible remedy are illustrated in the context of standard structural monetary policy models.
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Bibliographic InfoPaper provided by Norges Bank in its series Working Paper with number 2013/15.
Length: 43 pages
Date of creation: 11 Jun 2013
Date of revision:
Structural VAR; DSGE models; temporal aggregation; mixed frequency data; estimation. policy analysis;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-07-05 (All new papers)
- NEP-DGE-2013-07-05 (Dynamic General Equilibrium)
- NEP-ECM-2013-07-05 (Econometrics)
- NEP-ETS-2013-07-05 (Econometric Time Series)
- NEP-MST-2013-07-05 (Market Microstructure)
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