TESTING FOR ZERO AUTOCORRELATION IN THE PRESENCE OF STATISTICAL DEPENDENCE

Ignacio N. Lobato · John C. Nankervis · N. E. Savin

97 citations0/0 files storedEconometric Theory

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Financial Risk and Volatility Modeling Spatial and Panel Data Analysis Time Series Analysis and Forecasting

Abstract

The problem addressed in this paper is to test the null hypothesis that a time series process is uncorrelated up to lag K in the presence of statistical dependence. We propose an extension of the Box–Pierce Q-test that is asymptotically distributed as chi-square when the null is true for a very general class of dependent processes that includes non-martingale difference sequences. The test is based on a consistent estimator of the asymptotic covariance matrix of the sample autocorrelations under the null. The finite sample performance of this extension is investigated in a Monte Carlo study.

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References

1994Automatic Lag Selection in Covariance Matrix Estimation 1992An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator 1989The size and power of the variance ratio test in finite samples 1995Automatic Lag Selection in Covariance Matrix Estimation 1988The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation 1999Properties of moments of a family of GARCH processes 2000THE FUNCTIONAL CENTRAL LIMIT THEOREM AND WEAK CONVERGENCE TO STOCHASTIC INTEGRALS I 2001Testing for Autocorrelation Using a Modified Box‐Pierce Q Test

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