1985
DOI: 10.2307/1349204
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A Comparison of Alternative Forecasting Techniques for Livestock Prices: A Case Study

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Cited by 18 publications
(4 citation statements)
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“…The prediction of the dairy demands for the livestock is also calculated with an ARIMA model by Ahmad et al [18]. Harris et al [19] proves the effectiveness of the ARIMA model in livestock price prediction by comparing with the other models. Since the livestock food intake is less during the winter season, it shows a strong seasonal pattern which repeats every year, Therefore, the seasonal ARIMA (SARIMA) model [20] is found to be more prefect model compared to the other model such as linear regression, random walk and the vector regression.…”
Section: Related Workmentioning
confidence: 99%
“…The prediction of the dairy demands for the livestock is also calculated with an ARIMA model by Ahmad et al [18]. Harris et al [19] proves the effectiveness of the ARIMA model in livestock price prediction by comparing with the other models. Since the livestock food intake is less during the winter season, it shows a strong seasonal pattern which repeats every year, Therefore, the seasonal ARIMA (SARIMA) model [20] is found to be more prefect model compared to the other model such as linear regression, random walk and the vector regression.…”
Section: Related Workmentioning
confidence: 99%
“…There is, however, also a number of econometric models involving a set of variables determining supply of and demand for pork, whereas some forecasts are based upon a combination of forecasts obtained through ARIMA models and more comprehensive econometric models as in, for instance, Leuthold et al, 1 Bessler, 2 Brandt and Bessler, 3 and Harris and Leuthold. 4 Furthermore, a number of studies explore the forecasting properties of prices from exchanges trading hog or pork futures as in articles by Elam, 5 Fama and French, 6 Foote, Williams and Craven, 7 Leuthold and Hartmann, 8 and Wilkinson. 9 More recently, nonlinear variants of time series models have been tested out by, for instance, Chavas and Holt, 10 including forecasting with so-called neural networks (Hamm and Wade Brorsen 11 ).…”
Section: Forecasting Modelsmentioning
confidence: 99%
“…Some research has concentrated on the prediction of turning points themselves (see, for example, Kling; Zellner, Hong, and Gulati), others on evaluating a forecast's ability to predict the direction of revision (change) in a series. (See, for example, Bessler and Brandt;Bessler 1981, 1983;Harris and Leuthold;Kost;Naik and Leuthold. The latter study provides a good discussion of the literature on measures for qualitative forecast evaluation.)…”
mentioning
confidence: 99%