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Markov Regression Models for Time Series: A Quasi-Likelihood Approach
Scott L. Zeger and Bahjat Qaqish
Vol. 44, No. 4 (Dec., 1988), pp. 1019-1031
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2531732
Page Count: 13
You can always find the topics here!Topics: Time series models, Autocorrelation, Time series, Parametric models, Markov models, Regression analysis, Statistical models, Time dependence, Musical intervals, Statistical discrepancies
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This paper discusses a quasi-likelihood (QL) approach to regression analysis with time series data. We consider a class of Markov models, referred to by Cox (1981, Scandinavian Journal of Statistics 8, 93-115) as "observation-driven" models in which the conditional means and variances given the past are explicit functions of past outcomes. The class includes autoregressive and Markov chain models for continuous and categorical observations as well as models for counts (e.g., Poisson) and continuous outcomes with constant coefficient of variation (e.g., gamma). We focus on Poisson and gamma data for illustration. Analogous to QL for independent observations, large-sample properties of the regression coefficients depend only on correct specification of the first conditional moment.
Biometrics © 1988 International Biometric Society