SEASONALITY, NONSTATIONARITY AND THE FORECASTING OF MONTHLY TIME SERIES

In this paper the focus is on two forecasting models for a monthly time series. The first model requires that the variable is first order and seasonally differenced. The second model considers the series only in its first order differences, while seasonality is modeled with a constant and seasonal dummies. A method to empirically distinguish between these two models is presented. The relevance of this method is established by simulation results, as well as empirical evidence, which show that,. firstly, conventional autocorrelation checks are often not discriminative, and, secondly, that considering the first model while the second is more appropriate yields a deterioration of forecasting performance.


Issue Date:
1990
Publication Type:
Working or Discussion Paper
DOI and Other Identifiers:
Record Identifier:
https://ageconsearch.umn.edu/record/272481
Language:
English
Total Pages:
25
Series Statement:
REPORT 9066/A




 Record created 2018-05-02, last modified 2020-10-28

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