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A time deformation model and its time-varying autocorrelation: An application to US unemployment data

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  • Vijverberg, Chu-Ping C.

Abstract

A G-Lambda model is characterized by a constant mean, a finite variance and a covariance that is a function of both time and lags. The Box-Cox transformation of the time scale transforms a non-stationary G-Lambda model into a stationary model. This paper explores the time-varying behavior of the G-Lambda model. Simulation results indicate that it is possible to distinguish between the G-Lambda model and other better-known models such as the ARIMA, ARFIMA and STAR models. Applying the model to US unemployment data, the performance of the G-Lambda model varies as the start of the forecast periods changes. However, the results of the sign test and the Diebold-Mariano test indicate that the G-Lambda model has significantly better long-term forecasts than other models.

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  • Vijverberg, Chu-Ping C., 2009. "A time deformation model and its time-varying autocorrelation: An application to US unemployment data," International Journal of Forecasting, Elsevier, vol. 25(1), pages 128-145.
    • Handle: RePEc:eee:intfor:v:25:y:2009:i:1:p:128-145
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