A heteroskedasticity and autocorrelation robust F test using an orthonormal series variance estimator

作者：Sun Yixiao^*

来源：Econometrics Journal, 2013, 16(1): 1-26.

DOI：10.1111/j.1368-423X.2012.00390.x

• Asymptotic expansion
• F-distribution
• Fixed-smoothing asymptotics
• Heteroskedasticity and autocorrelation robust standard error
• Increasing-smoothing asymptotics
• Long-run variance
• Non-parametric series method

The paper develops a new heteroskedasticity and autocorrelation robust test in a time series setting. The test is based on a series long-run variance matrix estimator that involves projecting the time series of interest onto a set of orthonormal bases and using the sample variance of the projection coefficients as the long-run variance estimator. When the number of orthonormal bases K is fixed, a finite-sample-corrected Wald statistic converges to a standard F distribution. When K grows with the sample size, the usual uncorrected Wald statistic converges to a chi-square distribution. We show that critical values from the F distribution are second-order correct under the conventional increasing smoothing asymptotics. Simulations show that the F approximation is more accurate than the chi-square approximation in finite samples.

• 出版日期2013