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Forecasting growth with time series models

✍ Scribed by Daniel Peña

Publisher
John Wiley and Sons
Year
1995
Tongue
English
Weight
485 KB
Volume
14
Category
Article
ISSN
0277-6693

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✦ Synopsis

This paper compares the structure of three models for estimating future growth in a time series. It is shown that a regression model gives minimum weight to the last observed growth and maximum weight to the observed growth in the middle of the sample period. A first-order integrated ARIMA model, or 1(1) model, gives uniform weights to all observed growths. Finally, a second-order integrated ARIMA model gives maximum weights to the last observed growth and minimum weights to the observed growths at the beginning of the sample period. The forecasting performance of these models is compared using annual output growth rates for seven countries.

KEY WORDS AFUMA models; integrated processes; regression; stationary processes adding the values of the deterministic future trend and the forecast of the stationary residual.

(2) DBerentiate the series, test for unit roots and if the series is assumed to be integrated of order one (I(1)) build a stationary ARMA model in the first Merence of the series.

mically models built in this way include a constant for many economic time series. (3) Differentiate twice the series and build the ARMA model on the second difference of the process that is assumed to be I(2). Then in most cases the 1(2) model does not include a constant tern.

The decision between these three procedures should be made by testing the number of unit roots in the time series model. However, the available tests are not very powerful, specially for short time series, and therefore it is important to understand the consequences of using these models.

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