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DETECTING AND ANALYZING THE EFFECTS OF TIME‐VARYING PARAMETERS IN DSGE MODELS
Author(s) -
Canova Fabio,
Ferroni Filippo,
Matthes Christian
Publication year - 2020
Publication title -
international economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.658
H-Index - 86
eISSN - 1468-2354
pISSN - 0020-6598
DOI - 10.1111/iere.12418
Subject(s) - leverage (statistics) , inference , dynamic stochastic general equilibrium , econometrics , lti system theory , decision rule , constant (computer programming) , mathematics , invariant (physics) , computer science , economics , statistics , linear system , artificial intelligence , monetary policy , mathematical analysis , monetary economics , mathematical physics , programming language
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.