Inference for Time Series Regression Models With Weakly Dependent and Heteroscedastic Errors
Document Type
Article
Publication Date
1-1-2015
Abstract
© 2015 American Statistical Association Journal of Business & Economic Statistics. Motivated by the need to assess the significance of the trend in some macroeconomic series, this article considers inference of a parameter in parametric trend functions when the errors exhibit certain degrees of nonstationarity with changing unconditional variances. We adopt the recently developed self-normalized approach to avoid the difficulty involved in the estimation of the asymptotic variance of the ordinary least-square estimator. The limiting distribution of the self-normalized quantity is nonpivotal but can be consistently approximated by using the wild bootstrap, which is not consistent in general without studentization. Numerical simulation demonstrates favorable coverage properties of the proposed method in comparison with alternative ones. The U.S. nominal wages series is analyzed to illustrate the finite sample performance. Some technical details are included in the online supplemental material.
Publication Title
Journal of Business and Economic Statistics
Recommended Citation
Rho, Y.,
&
Shao, X.
(2015).
Inference for Time Series Regression Models With Weakly Dependent and Heteroscedastic Errors.
Journal of Business and Economic Statistics,
33(3), 444-457.
http://doi.org/10.1080/07350015.2014.962698
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/9257