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Statistical inference in calibrated models
Author(s) -
Canova Fabio
Publication year - 1994
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.3950090508
Subject(s) - monte carlo method , inference , econometrics , range (aeronautics) , variance (accounting) , computer science , statistical inference , sensitivity (control systems) , economics , mathematics , statistics , artificial intelligence , accounting , materials science , electronic engineering , engineering , composite material
This paper describes a Monte Carlo procedure to assess the performance of calibrated dynamic general equilibrium models. The procedure formalizes the choice of parameters and the evaluation of the model and provides an efficient way to conduct a sensitivity analysis for perturbations of the parameters within a reasonable range. As an illustration the methodology is applied to two problems: the equity premium puzzle and how much of the variance of actual US output is explained by a real business cycle model.