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Forecasting with latent structure time series models: an application to nominal interest rates
Author(s) -
Iyer Sridhar,
Andrews Rick L.
Publication year - 1999
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199911)18:6<395::aid-for731>3.0.co;2-e
Subject(s) - econometrics , heteroscedasticity , series (stratigraphy) , interest rate , treasury , autoregressive integrated moving average , economics , time series , computer science , statistics , mathematics , paleontology , biology , history , archaeology , monetary economics
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. Copyright © 1999 John Wiley & Sons, Ltd.