VECTOR AUTOREGRESSIVE COVARIANCE MATRIX ESTIMATION

Wouter J. Den Haan

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Financial Risk and Volatility Modeling Advanced Statistical Methods and Models Spatial and Panel Data Analysis

Abstract

This paper proposes a vector autoregressive (VAR) spectral estimation procedure for constructing heteroskedasticity and autocorrelation consistent (HAC) covariance matrices. We establish the consistency of the VARHAC estimator under general conditions similar to those considered in previous research, and we demonstrate that this estimator converges at a faster rate than the kernel-based estimators proposed by Andrews and Monahan (1992) and Newey and West (1994). In finite samples, Monte Carlo simulation experiments indicate that the VARHAC estimator matches, and in some cases greatly exceeds, the performance of the prewhitened kernel estimator proposed by Andrews and Monahan (1992). These simulation experiments also illustrate several important limitations of kernel-based HAC estimation procedures, and highlight the advantages of explicitly modeling the temporal properties of the error terms.

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References

1993A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems 1991Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation 1994Automatic Lag Selection in Covariance Matrix Estimation 1988Permanent and Temporary Components of Stock Prices 1993Introduction to Multiple Time Series Analysis 1995Unit Root Tests in ARMA Models with Data-Dependent Methods for the Selection of the Truncation Lag 1992An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator 1980Formulating and estimating dynamic linear rational expectations models

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