Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models

Liudas Giraitis, George Kapetanios and Tony Yates
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Liudas Giraitis: Queen Mary University of London
Tony Yates: Unversity of Bristol

No 767, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: In this paper 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 modeling VAR dynamics for non-stationary times series and estimation of time varying parameter processes by 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 7 variable data set to analyze evidence of time-variation in empirical objects of interest for the DSGE literature. The results of this paper serve as a starting point for further research on numerous open problems including establishing estimation results of time-varying parameters that are uniform in time t, constructing Bonferroni-type correction to the pointwise standard error bands and developing a valid test of the null hypothesis of no time variation.

Keywords: Kernel estimation; Time-varying VAR; Structural change; Monetary policy shock (search for similar items in EconPapers)
JEL-codes: C10 C14 E52 E61 (search for similar items in EconPapers)
Date: 2015-12-18
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Citations: View citations in EconPapers (7)

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Journal Article: Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:767

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