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Forecasting of Categorical Time Series Using a Regression Model

Forecasting of Categorical Time Series Using a Regression Model 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economic Quality Control de Gruyter

Forecasting of Categorical Time Series Using a Regression Model

Economic Quality Control , Volume 18 (2) – Oct 1, 2003

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Publisher
de Gruyter
Copyright
Copyright © 2003 by the
ISSN
1869-6147
eISSN
1869-6147
DOI
10.1515/EQC.2003.223
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Economic Quality Controlde Gruyter

Published: Oct 1, 2003

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