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Markov Chain Approach to Forecast in the Binomial Autoregressive Models
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 Title & Authors
Markov Chain Approach to Forecast in the Binomial Autoregressive Models
Kim, Hee-Young; Park, You-Sung;
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 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
1.
Parameter estimation for binomial AR(1) models with applications in finance and industry, Statistical Papers, 2013, 54, 3, 563  crossref(new windwow)
2.
Binomial AR(1) processes: moments, cumulants, and estimation, Statistics, 2013, 47, 3, 494  crossref(new windwow)
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