The sensitivity of DSGE models' results to data detrending

JEL codes: 
Series number: 
OeNB Worki
Version Date: 
Jun 2009

This paper aims to shed light on potential pitfalls of different data filtering and detrending procedures for the estimation of stationary DSGE models. For this purpose, a medium-sized New Keynesian model as the one developed by Smets and Wouters (2003) is used to assess the sensitivity of the structural estimates to preliminary data transformations. To examine the question, we focus on two widely used detrending and filtering methods, the HP filter and linear detrending. After comparing the properties of business cycle components, we estimate the model through Bayesian techniques using in turn the two different sets of transformed data. Empirical findings show that posterior distributions of structural parameters are rather sensitive to the choice of detrending. As a consequence, both the magnitude and the persistence of theoretical responses to shocks depend upon preliminary filtering.

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