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Forecasting growth with time series models
Author(s) -
Peña Daniel
Publication year - 1995
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/for.3980140203
Subject(s) - autoregressive integrated moving average , series (stratigraphy) , econometrics , growth model , sample (material) , time series , mathematics , statistics , annual growth % , economics , thermodynamics , physics , mathematical economics , biology , paleontology , agricultural economics
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.