Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models

Liudas Giraitis, George Kapetanios and Tony Yates

Journal of Time Series Analysis, 2018, vol. 39, issue 2, 129-149

Abstract: In this article, we introduce the general setting of a multivariate time series autoregressive model with stochastic time†varying coefficients and time†varying conditional variance of the error process. This allows modelling VAR dynamics for non†stationary time series and estimation of time†varying parameter processes by the well†known rolling regression estimation techniques. We establish consistency, convergence rates, and asymptotic normality for kernel estimators of the paths of coefficient processes and provide pointwise valid standard errors. The method is applied to a popular seven†variable dataset to analyse evidence of time variation in empirical objects of interest for the DSGE (dynamic stochastic general equilibrium) literature.

Date: 2018
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Citations: View citations in EconPapers (35)

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https://doi.org/10.1111/jtsa.12271

Related works:
Working Paper: Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:39:y:2018:i:2:p:129-149

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