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Maximum-Likelihood Estimation in a Special Integer Autoregressive Model

Institut für Volkswirtschaftslehre (520K) and Computational Science Lab (CSL) Hohenheim, Universität Hohenheim, D-70593 Stuttgart, Germany
Management School, University of Liverpool, Liverpool L69 7ZH, UK
Author to whom correspondence should be addressed.
Econometrics 2020, 8(2), 24;
Received: 25 February 2020 / Revised: 14 May 2020 / Accepted: 2 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Discrete-Valued Time Series: Modelling, Estimation and Forecasting)
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. View Full-Text
Keywords: autoregression; counts; maximum-likelihood; binomial-thinning autoregression; counts; maximum-likelihood; binomial-thinning
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MDPI and ACS Style

Jung, R.C.; Tremayne, A.R. Maximum-Likelihood Estimation in a Special Integer Autoregressive Model. Econometrics 2020, 8, 24.

AMA Style

Jung RC, Tremayne AR. Maximum-Likelihood Estimation in a Special Integer Autoregressive Model. Econometrics. 2020; 8(2):24.

Chicago/Turabian Style

Jung, Robert C., and Andrew R. Tremayne. 2020. "Maximum-Likelihood Estimation in a Special Integer Autoregressive Model" Econometrics 8, no. 2: 24.

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