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Inference on Multivariate Heteroscedastic Time Varying Random Coefficient Models
Author(s) -
Giraitis Liudas,
Kapetanios George,
Yates Tony
Publication year - 2018
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12271
Subject(s) - mathematics , heteroscedasticity , estimator , autoregressive model , asymptotic distribution , series (stratigraphy) , pointwise , econometrics , conditional variance , multivariate statistics , time series , statistics , autoregressive conditional heteroskedasticity , volatility (finance) , paleontology , mathematical analysis , biology
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.