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Optimal transformations for categorical autoregressive time series

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  • S. Van Buuren

Abstract

This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. ‘Optimal’ implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze categorical time series. The method uses an alternating least squares algorithm that consists of two main steps: estimation and transformation. Nominal, ordinal and numerical data can be analyzed. Some alternative applications of the general idea are highlighted: intervention analysis, smoothing categorical time series, predictable components, spatial modeling and cross‐sectional multivariate analysis. Limitations, modeling issues and possible extensions are briefly indicated.

Suggested Citation

  • S. Van Buuren, 1997. "Optimal transformations for categorical autoregressive time series," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 51(1), pages 90-106, March.
    • Handle: RePEc:bla:stanee:v:51:y:1997:i:1:p:90-106
    DOI: 10.1111/1467-9574.00039
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