Residual empirical processes for long and short memory time series

TL;DR

This paper analyzes the residual empirical process in long- and short-memory time series regression models, revealing limitations of existing tests and proposing new statistics for better distribution testing in complex models.

Contribution

It establishes a uniform expansion for the residual empirical process and introduces new test statistics for long-memory noise in regression and autoregressive models.

Findings

Limit distribution of KS test does not hold with unknown intercept or unit root.

New test statistics are proposed for long-memory noise.

Results apply to stochastic regression and unstable autoregressive models.

Abstract

This paper studies the residual empirical process of long- and short-memory time series regression models and establishes its uniform expansion under a general framework. The results are applied to the stochastic regression models and unstable autoregressive models. For the long-memory noise, it is shown that the limit distribution of the Kolmogorov-Smirnov test statistic studied in Ho and Hsing [Ann. Statist. 24 (1996) 992-1024] does not hold when the stochastic regression model includes an unknown intercept or when the characteristic polynomial of the unstable autoregressive model has a unit root. To this end, two new statistics are proposed to test for the distribution of the long-memory noises of stochastic regression models and unstable autoregressive models. (With Correction.)