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A Test of Correlation in the Random Coefficients of an Autoregressive Process

Abstract

A random coefficient autoregressive process in which the coefficients are correlated is investigated. 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 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 in the random coefficients is violated. Finally, a consistent estimation is given together with a testing procedure for the existence of correlation in the coefficients. While convergence properties rely on ergodicity, we use a martingale approach to reach most of the results.

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Correspondence to F. Proïa.

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Proïa, F., Soltane, M. A Test of Correlation in the Random Coefficients of an Autoregressive Process. Math. Meth. Stat. 27, 119–144 (2018). https://doi.org/10.3103/S1066530718020035

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  • DOI: https://doi.org/10.3103/S1066530718020035

Keywords

  • RCAR process,MAprocess
  • random coefficients
  • least squares estimation
  • stationarity
  • ergodicity
  • symptotic normality
  • autocorrelation

2010 Mathematics Subject Classification

  • 62M10
  • 62F03
  • 62F12
  • 60G42