Vector autoregressions and reduced form representations of DSGE models

Abstract

and an anonymous referee for helpful comments and suggestions, and Juan Rubio-Ramirez for supplying the Matlab code to compute the finite order VAR representation of a state-space model. Part of this work was prepared while the author was participating in the Banco de España Visiting Fellow program. Support from Banco de España is gratefully acknowledged. The opinions and analyses in the Working Paper Series are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem. Documentos de Trabajo. N.º 0619 2006 The Working Paper Series seeks to disseminate original research in economics and finance. All papers have been anonymously refereed. By publishing these papers, the Banco de España aims to contribute to economic analysis and, in particular, to knowledge of the Spanish economy and its international environment. The opinions and analyses in the Working Paper Series are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem. The Banco de España disseminates its main reports and most of its publications via the INTERNET at the following website: http://www.bde.es. Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged.

Monetary policy and the exchange rate: Evaluation of VAR models

“…Given that we use only a subset of model variables in the VAR, we may be introducing truncation bias by estimating a finite order VAR model (see Ravenna, 2007). Kapetanios, Pagan and Scott (2007) investigate this question in a simulation exercise.…”

Are spectral estimators useful for long-run restrictions in SVARs?

“…model the truncation bias in SVARs can be substantial, as shown by Cooley and Dywer (1998), Erceg et al (2005), Ravenna (2007) or CKM. The truncation bias is also sizable for data from the model described above as will be seen in Fig.…”

“…First, an accurate representation of the true model typically requires a VAR with a high lag order, much higher than what is affordable in a sample of typical length and resulting in a sizable truncation bias-discussed for example by Chari et al (2008, henceforth ''CKM'') and Ravenna (2007). Second, there is the small sample bias in estimated coefficients known from Hurwicz (1950) and also discussed by CKM, which becomes ever more severe the smaller the sample, and the more persistent the data.…”

“…Truncation bias due to omitted lags has been stressed by CKM, Erceg et al (2005), Ravenna (2007) and Cooley and Dywer (1998). The adjusted procedure retains the OLS estimates and fills up the omitted lags with a spectral factorization of the spectral density's non-parametric estimate.…”

Monetary policy shocks and Cholesky VARs: an assessment for the Euro area

“…to ensure the 'invertibility' of the DSGE model, i.e., to ensure that a VAR can actually recover the structural shocks as modeled by the DSGE framework. Ravenna (2007) shows that truncated VARs may provide misleading indications when the true DGP is an infinite order VAR. Further investigations on the distortions coming from the truncation bias, mainly on the identification of the technology shock and the dynamic reaction of hours to it, are offered by Christiano et al (2006) and Chari et al (2008).…”