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Forecasting economic time series that require a power transformation: Case of state tax receipts
Author(s) -
Nazmi Nader,
Leuthold Jane H.
Publication year - 1988
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.3980070303
Subject(s) - econometrics , series (stratigraphy) , time series , transformation (genetics) , multiplicative function , computer science , state (computer science) , autoregressive–moving average model , economics , autoregressive model , mathematics , statistics , algorithm , paleontology , mathematical analysis , biochemistry , chemistry , gene , biology
In this study, time series analysis is applied to the problem of forecasting state income tax receipts. The data series is of special interest since it exhibits a strong trend with a high multiplicative seasonal component. An appropriate model is identified by simultaneous estimation of the parameters of the power transformation and the ARMA model using the Schwarz (1978) Bayesian information criterion. The forecasting performance of the time series model obtained from this procedure is compared with alternative time series and regression models. The study illustrates how an information criterion can be employed for identifying time series models that require a power transformation, as exemplified by state tax receipts. It also establishes time series analysis as a viable technique for forecasting state tax receipts.