A test of correlation in the random coefficients of an autoregressive process

TL;DR

This paper investigates the effects of correlated random coefficients in autoregressive processes, analyzing stationarity, estimation properties, and proposing tests for correlation, highlighting the impact on estimator consistency.

Contribution

It provides a comprehensive analysis of correlated coefficients in AR processes, including stationarity conditions, asymptotic properties, and a new testing procedure for correlation.

Findings

Correlated coefficients can lead to inconsistency in AR parameter estimation.

A new consistent estimator for correlated coefficients is proposed.

A hypothesis test for the existence of correlation in coefficients is developed.

Abstract

A random coefficient autoregressive process is deeply investigated in which the coefficients are correlated. First we look at the existence of a strictly stationary causal solution, we give the second-order stationarity conditions and the autocorrelation function of the process. Then we study some asymptotic properties of the empirical mean and the usual estimators of the process, such as convergence, asymptotic normality and rates of convergence, supplied with the appropriate assumptions on the driving perturbations. Our objective is to get an overview of the influence of correlated coefficients in the estimation step, through a simple model. In particular, the lack of consistency is shown for the estimation of the autoregressive parameter when the independence hypothesis is violated in the random coefficients. Finally, a consistent estimation is given together with a testing procedure for the existence of correlation in the coefficients. While convergence properties rely on the ergodicity, we use a martingale approach to reach most of the results.