The aim of this study was to choose forecasting model for nominal and real price of beef, sheep meat, milk, and wool. Initially the stationary of the series was tested, and then in order to investigate whether series are stochastic, nonparametric test of Vald-Wulfowitz was applied. Based on the tests results, all of the selected nominal and real prices were recognized to be predictable. The models applied to forecast are ARIMA, and Artificial Neural Network (ANN). The findings indicated relative superiority of ARIMA in comparison with ANN in predicting nominal prices of selected products. However, in the case of real prices, ANN showed a comparative superiority. It was also found that in the case of nominal series, increase in the forecast period lead to increased forecast error.
(2013). Choosing Forecasting Model for Livestock Products Prices. Agricultural Economics and Development, 21(1), 101-129. doi: 10.30490/aead.2013.58680
MLA
. "Choosing Forecasting Model for Livestock Products Prices". Agricultural Economics and Development, 21, 1, 2013, 101-129. doi: 10.30490/aead.2013.58680
HARVARD
(2013). 'Choosing Forecasting Model for Livestock Products Prices', Agricultural Economics and Development, 21(1), pp. 101-129. doi: 10.30490/aead.2013.58680
VANCOUVER
Choosing Forecasting Model for Livestock Products Prices. Agricultural Economics and Development, 2013; 21(1): 101-129. doi: 10.30490/aead.2013.58680
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