A Comparison Study of Causal and Non-Causal Models in Libyan Agricultural output Forecasting*

Abstract

The Forecasting has been very important in decision making at all levels and sectors of the economy. This paper aims to compare between the Causal and Non-Causal Forecasting models in Libyan agricultural output during the period (2015 -2020). The study relies on secondary data on annual basis obtained from the Libyan central bank during the period (1980-2014).  The results showed that the pioneer causal models especially neural network has less error and much better performance to estimate Libyan agricultural output compared to the econometric methods. the Libyan agricultural output forecasts during the period (2015 - 2020) show that there is an increase in agricultural output at the forecasting period.

Key words: artificial neural network, agricultural output, Econometric Model, Forecasting, Libya,

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­­­­­­­­­­­­­­­­­* Full article in the Arabic Copy of this journal.

Corresponding Author: Ragab M.  Elwerfelli. Dep. of Agricultural Economics, Fac. of Agriculture, Univ. of Tripoli, Tripoli, Libya. Phone: +218927360885. Email: remw2016@Gmail.com

Received: 1/8/2016

Accepted: 29/3/2017