Abstract
We are dealing with the prediction of forthcoming outcomes of a categorical time series. We will assume that the evolution of the time series is driven by a covariate process and by former outcomes and that the covariate process itself obeys an autoregressive law. Two forecasting methods are presented. The first is based on an integral formula for the probabilities of forthcoming events and by a Monte Carlo evaluation of this integral. The second method makes use of an approximation formula for conditional expectations. The procedures proposed are illustrated by an application to data on forest damages.
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Pruscha, H. (1995). Forecast Methods in Regression Models for Categorical Time Series. In: Seeber, G.U.H., Francis, B.J., Hatzinger, R., Steckel-Berger, G. (eds) Statistical Modelling. Lecture Notes in Statistics, vol 104. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0789-4_29
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DOI: https://doi.org/10.1007/978-1-4612-0789-4_29
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94565-1
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