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Forecasting growth with time series models

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1993-12
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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 I(1) model, gives uniform weights to all observed growths. Finally, a second order integrated ARIMA model gives maximum weights to the last observed gro~1h andı minimum weights to the observed growths at the beginning of the sample period.
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ARIMA models, Integrated processes, Regression, Stationary processes
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