Forecasting of Categorical Time Series Using a Regression Model
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Helmut Pruscha
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.
© Heldermann Verlag
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Articles in the same Issue
- NP-Optimal Kernels for Nonparametric Sequential Detection Rules
- Availability Formulas and Performance Measures for Separable Degradable Networks
- Bayesian Predictions for Exponentially Distributed Failure Times With One Change-Point
- Bayes Inference Problems in Failure-Repair Processes
- Forecasting of Categorical Time Series Using a Regression Model
- Estimation of a Threshold-Value in the Context of Air Pollution and Health
- Estimation of the Jump-Point in a Hazard Function
- Distributions of the Estimated Process Capability Index Cpk
