Simulation-Based Tests of;Forward-Looking Models Under VAR Learning Dynamics
In this paper we propose simulation-based techniques to investigate the finite;sample performance of likelihood ratio (LR) tests for the nonlinear restrictions;that arise when a class of forward-looking (FL) models, typically used in monetary;policy analysis, is evaluated with Vector Autoregressive (VAR) models. We;consider both `one-shot' tests and sequences of tests under a particular form of;adaptive learning dynamics, where `boundedly rational' agents use VARs recursively;to update their beliefs. The analysis is based on the comparison of the likelihood of the unrestricted and restricted VAR, and the p-values associated;with the LR statistics are computed by Monte Carlo simulation. We also address;the case where the variables of the FL model are approximated as non-stationary;cointegrated processes. Application to the New Keynesian Phillips Curve in the;euro area shows that the FL model of inflation dynamics is not rejected once the;suggested simulation-based tests are applied. The result is robust to specification of the VAR as a stationary (albeit highly persistent) or cointegrated system.;However, in the second case the imposition of cointegration restrictions changes;the estimated degree of price stickiness.
Monte Carlo test, VAR, adaptive learning, cross-equation restrictions, forward-looking model, new Keynesian Phillips curve, simulation techniques
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