Forecasting of Categorical Time Series Using a Regression Model

Pruscha Helmut and Göttlein Axel
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Pruscha Helmut: Mathematical Institute, University of Munich, Theresienstr. 39, D-80333 Munich, Germany. pruscha@mathematik.uni-muenchen.de
Göttlein Axel: Forest Nutrition and Water Resources, Technical University of Munich, Am Hochanger 13, D-85354 Freising, Germany. goettlein@forst.tu-muenchen.de

Stochastics and Quality Control, 2003, vol. 18, issue 2, 223-240

Abstract: This paper deals with time series of categorical or ordinal variables, which are combined with time varying covariates. The conditional expectations (probabilities) are modelled as a regression model in a GLM-type manner, its parameters are estimated using a (partial) likelihood-approach. Special attention is given to the multivariate and the cumulative logistic regression model, with a regression term defined by a recursive scheme. The main concern is directed at forecasts for such time series. Using an approximation formula for conditional expectations l-step predictors are developed. Bias and mean square errors are estimated by using expansion formulas and by employing Box-Jenkins as well as nonparametric methods. The procedures proposed are numerically applied to a data set of yearly forest health inventories.

Keywords: Categorical time series; multivariate logistic regression; cumulative regression models; forecast methods; generalized linear models; forest inventory (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1515/EQC.2003.223

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