Maximum-Likelihood Estimation in a Special Integer Autoregressive Model

Robert Jung () and Andrew Tremayne

Econometrics, 2020, vol. 8, issue 2, 1-15

Abstract: The paper is concerned with estimation and application of a special stationary integer autoregressive model where multiple binomial thinnings are not independent of one another. Parameter estimation in such models has hitherto been accomplished using method of moments, or nonlinear least squares, but not maximum likelihood. We obtain the conditional distribution needed to implement maximum likelihood. The sampling performance of the new estimator is compared to extant ones by reporting the results of some simulation experiments. An application to a stock-type data set of financial counts is provided and the conditional distribution is used to compare two competing models and in forecasting.

Keywords: autoregression; counts; maximum-likelihood; binomial-thinning (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:8:y:2020:i:2:p:24-:d:368766

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