Forecasting Performance of Structural Time Series Models

Abstract

Although theoretical research on the properties of structural time series models has regularly appeared in the literature, there is as yet scant evidence on the forecasting performance of structural models relative to more traditional methods. This study compares the empirical performance of structural time series models to four methods that are similar in complexity, using 111 business and economic time series. The structural approach appears to perform quite well on annual, quarterly, and monthly data, especially for long forecasting horizons and seasonal data. Of the more complex forecasting methods, structural models appear to be among the most accurate.

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