Forecasting unemployment using an autoregression with censored latent effects parameters

Forecasting unemployment using an autoregression with censored latent effects parameters

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Article ID: iaor2005653
Country: Netherlands
Volume: 20
Issue: 2
Start Page Number: 255
End Page Number: 271
Publication Date: Apr 2004
Journal: International Journal of Forecasting
Authors: , ,
Keywords: forecasting: applications, social
Abstract:

Monthly observed unemployment typically displays explosive behavior in recessionary periods, while there seems to be stationary behavior in expansions. Allowing parameters in an autoregression to vary across regimes, and hence over time, can capture this feature. In this paper, we put forward a new autoregressive time series model with time-varying parameters, where this variation depends on a linear indicator variable. When the value of this variable exceeds a stochastic threshold level, the parameters change. We discuss representation, estimation and interpretation of the model. Also, we analyze its forecasting performance for unemployment series of three G-7 countries, and we compare it with various related models.

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