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Stochastic Model Specification Search for Time-Varying Parameter VARs

Stochastic Model Specification

Eric Eisenstat, Joshua Chan and Rodney Stachan

This article develops a new econometric methodology for performing stochastic model specification search (SMSS) in the vast model space of time-varying parameter VARs with stochastic volatility and correlated state transitions.

This is motivated by the concern of over-fitting 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 timevarying 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 GDP during a period of very low interest rates.

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Updated:   18 February 2017 / Responsible Officer:  Dean, Business & Economics / Page Contact:  College Web Team