Title
Could the test from the standard regression model could make significant regression with autoregressive Yt and Xt become insignificant?
Authors
Abstract
This paper extends Cheng, et al. (2022) and Wong and Pham (2022) to investigate whether the statistics TN for testing H0 : β = β0 versus H1 : β ̸= β0 from the traditional regression model from the standard regression model Yt = α + βXt + ut where ut is assumed to be iid N(0, σ2) could be used for regression with autoregressive Yt and Xt. To do so, we set Yt = Y1;t + Y2;t with Y2;t = ϕY Y2;t−1 + et and X2;t = ϕXX2;t−1 + εt in which et i∼id (0, σ2e) and εti ∼id (0, σ2) so that both Yt and Xt are autoregressive. We use the statistics TN for testing H0 : β = β0 versus H1 : β ̸= β0 when the actual β > 0, for example,β = 0.1. In our simulation, we found that the average rejection rate is less than the level of significance for any sample size N smaller than 100. However, for large N, say, N = 1000, the test conrms that the model is significant. Our findings confirm that the test from the standard regression model could make a significant regression with autoregressive noise become insignificant for small sample sizes, but not for very large sample sizes.
Keywords
Stationarity, autoregression, regression, time series analysis, re-gression with autoregressive noise
JEL Classication
C01, C15, C22, C58, C60
How to Cite
Wong, W.-K. & Pham, M. T. (2023). Could the test from the standard regression model could make significant regression with autoregressive Yt and Xt become insignificant?. The International Journal of Finance, 35, 1–19.