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      RANDOM COEFFICIENT AUTOREGRESSIVE PROCESSES:A MARKOV CHAIN ANALYSIS OF STATIONARITY AND FINITENESS OF MOMENTS

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      Journal of Time Series Analysis

      Wiley

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          Most cited references 3

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          Random Coefficient Autoregressive Models: An Introduction

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            Geometric ergodicity of Harris recurrent Marcov chains with applications to renewal theory

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              The existence of moments for stationary Markov chains

               R L Tweedie (1983)
              We give conditions under which the stationary distribution π of a Markov chain admits moments of the general form ∫ f ( x ) π ( dx ), where f is a general function; specific examples include f ( x ) = x r and f ( x ) = e sx . In general the time-dependent moments of the chain then converge to the stationary moments. We show that in special cases this convergence of moments occurs at a geometric rate. The results are applied to random walk on [0, ∞).
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                Author and article information

                Journal
                Journal of Time Series Analysis
                J Time Series Analysis
                Wiley
                0143-9782
                1467-9892
                January 1985
                January 1985
                : 6
                : 1
                : 1-14
                Article
                10.1111/j.1467-9892.1985.tb00394.x
                © 1985

                http://doi.wiley.com/10.1002/tdm_license_1.1

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