Markov Chain Approach to Forecast in the Binomial Autoregressive Models

- Journal title : Communications for Statistical Applications and Methods
- Volume 17, Issue 3, 2010, pp.441-450
- Publisher : The Korean Statistical Society
- DOI : 10.5351/CKSS.2010.17.3.441

Title & Authors

Markov Chain Approach to Forecast in the Binomial Autoregressive Models

Kim, Hee-Young; Park, You-Sung;

Kim, Hee-Young; Park, You-Sung;

Abstract

In this paper we consider the problem of forecasting binomial time series, modelled by the binomial autoregressive model. This paper considers proposed by McKenzie (1985) and is extended to a higher order by (2009). Since the binomial autoregressive model is a Markov chain, we can apply the earlier work of Bu and McCabe (2008) for integer valued autoregressive(INAR) model to the binomial autoregressive model. We will discuss how to compute the h-step-ahead forecast of the conditional probabilities of when T periods are used in fitting. Then we obtain the maximum likelihood estimator of binomial autoregressive model and use it to derive the maximum likelihood estimator of the h-step-ahead forecast of the conditional probabilities of . The methodology is illustrated by applying it to a data set previously analyzed by (2009).

Keywords

Binomial thinning;binomial AR(p) model;Markov chain;

Language

English

Cited by

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