AbstractMacroeconomic research often relies on structural vector autoregressions to uncover empirical regularities. Critics argue the method goes awry due to lag truncation: short lag-lengths imply a poor approximation to DSGE-models. Empirically, short lag-length is deemed necessary as increased parametrization induces excessive uncertainty. The paper shows that this argument is incomplete. Longer lag-length simultaneously reduces misspecification, which in turn reduces variance. For data generated by frontier DSGE-models long-lag VARs are feasible, reduce bias and variance, and have better coverage. Thus, contrary to conventional wisdom, the trivial solution to the critique actually works.
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Bibliographic InfoPaper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 271.
Length: 28 pages
Date of creation: 01 Jun 2013
Date of revision:
VAR; SVAR; Lag-length; Truncation;
Find related papers by JEL classification:
- C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-06-30 (All new papers)
- NEP-ECM-2013-06-30 (Econometrics)
- NEP-ETS-2013-06-30 (Econometric Time Series)
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