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DETECTING AND ANALYZING THE EFFECTS OF TIME-VARYING PARAMETERS IN DSGE MODELS |
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| Authors: | Fabio Canova Filippo Ferroni Christian Matthes |
| Affiliation: | 1. Norwegian Business School, Norway;2. Federal Reserve Bank of Chicago, U.S.A.;3. Indiana University Federal Reserve Bank of Richmond, U.S.A. We thank Jesus Fernandez Villaverde (the editor), two anonymous referees, Stephane Bonhomme, Francesco Bianchi, Ferre de Graeve, Marco del Negro, James Hamilton, Lars Hansen, Michele Lenza, Frank Schorfheide, Harald Uhlig, and Tao Zha, as well as participants of many seminars and conferences for their comments and suggestions. Canova acknowledges the financial support from the Spanish Ministerio de Economia y Competitividad through the grants ECO2012-33247, ECO2015-68136-P, and FEDER, UE. The views presented in this article are not necessarily those of the Federal Reserve Bank of Richmond, the Federal Reserve Bank of Chicago, or the Federal Reserve System. Earlier versions of this article circulated under the title “Approximating Time Varying Structural Models with Time Invariant Structures.” |
| Abstract: | We study how structural parameter variations affect the decision rules and economic inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. A constant parameter model poorly approximates a time-varying data generating process (DGP), except in a handful of relevant cases. Linear approximations do not produce time-varying decision rules; higher-order approximations can do this only if parameter disturbances are treated as decision rule coefficients. Structural responses are time invariant regardless of order of approximation. Adding endogenous variations to the parameter controlling leverage in Gertler and Karadi's model substantially improves the fit of the model. |
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