Forecasting with latent structure time series models: an application to nominal interest rates

✍ Scribed by Sridhar Iyer; Rick L. Andrews

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 195 KB
• Volume: 18
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/(sici)1099-131x(199911)18:6<395::aid-for731>3.0.co;2-e

✦ Synopsis

In this paper we develop a latent structure extension of a commonly used structural time series model and use the model as a basis for forecasting. Each unobserved regime has its own unique slope and variances to describe the process generating the data, and at any given time period the model predicts a priori which regime best characterizes the data. This is accomplished by using a multinomial logit model in which the primary explanatory variable is a measure of how consistent each regime has been with recent observations. The model is especially well suited to forecasting series which are subject to frequent and/or major shocks. An application to nominal interest rates shows that the behaviour of the three-month US Treasury bill rate is adequately explained by three regimes. The forecasting accuracy is superior to that produced by a traditional single-regime model and a standard ARIMA model with a conditionally heteroscedastic error.