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Working paper
SSRN
Working paper
Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility
In: CAMA Working Paper No. 32/2015
SSRN
Working paper
Efficient Estimation of Bayesian VARMAs with Time-Varying Coefficients
In: CAMA Working Paper No. 19/2015
SSRN
Working paper
Identifying noise shocks
In: Journal of economic dynamics & control, Volume 111, p. 103780
ISSN: 0165-1889
Composite Likelihood Methods for Large Bayesian VARs with Stochastic Volatility
In: CAMA Working Paper No. 26/2018
SSRN
Working paper
Stochastic Model Specification Search for Time-Varying Parameter VARs
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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Stochastic Model Specification Search for Time-Varying Parameter VARs
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.
BASE