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FORECASTING EXPONENTIAL AUTOREGRESSIVE MODELS OF ORDER 1
Author(s) -
AlQassam M. S.,
Lane J. A.
Publication year - 1989
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1989.tb00018.x
Subject(s) - autoregressive model , extrapolation , mathematics , exponential function , linearization , sequence (biology) , star model , econometrics , mathematical optimization , nonlinear system , autoregressive integrated moving average , statistics , time series , mathematical analysis , physics , quantum mechanics , biology , genetics
. Exact forecasting of the non‐linear EXPAR(1) model for several steps ahead involves a sequence of numerical integrations, thus motivating the search for reasonable approximations. A method based on the assumption of approximately normal forecast errors is shown to give forecasts which perform well in both qualitative and numerical comparisons with two alternative approximations based on naive extrapolation and linearization of the autoregression function.