Stochastic Model Specification Search for Time-Varying Parameter VARs
Date
2016
Authors
Eisenstat, Eric
Chan, Chi Chun (Joshua)
Strachan, Rodney
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Publisher
Marcel Dekker Inc.
Abstract
This article develops a new econometric methodology for performing stochastic model specification search (SMSS) in the vast model space of time-varying parameter vector
autoregressions (VARs) with stochastic volatility and correlated state transitions. This is motivated by the concern of overfitting and the typically imprecise inference in these highly parameterized models. For each VAR coefficient, this new method automatically decides whether it is constant or time-varying. Moreover, it can be used to shrink an otherwise unrestricted time-varying parameter VAR to a stationary VAR, thus providing an easy way to (probabilistically) impose stationarity in time-varying parameter models. We demonstrate the effectiveness of the approach with a topical application, where we investigate the dynamic effects of structural shocks in government spending on U.S. taxes and gross domestic product (GDP) during a period of very low interest rates.
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Source
Econometric Reviews
Type
Journal article
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Restricted until
2037-12-31