PRICE FORECASTING WITH TIME-SERIES METHODS AND NONSTATIONARY DATA: AN APPLICATION TO MONTHLY U.S. CATTLE PRICES

The forecasting performance of various multivariate as well as univariate ARIMA models is evaluated in the presence of nonstationarity. The results indicate the importance of identifying the characteristics of the time series by testing for types of nonstationarity. Procedures that permit model specifications consistent with the system’s dynamics provide the most accurate forecasts.


Issue Date:
1990-07
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/32505
Published in:
Western Journal of Agricultural Economics, Volume 15, Number 1
Page range:
123-132
Total Pages:
10




 Record created 2017-04-01, last modified 2017-04-04

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