NEURAL NETWORK APPLICATIONS IN AGRICULTURAL ECONOMICS
Neural networks have become very important tools in many areas including economic researches. The objectives of this thesis are to examine the fundamental components, concepts and theory of neural network methods from econometric and statistic perspective, with particular focus on econometrically an...
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| Format: | Others |
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UKnowledge
2005
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| Online Access: | http://uknowledge.uky.edu/gradschool_diss/228 http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1231&context=gradschool_diss |
| Summary: | Neural networks have become very important tools in many areas including economic researches. The objectives of this thesis are to examine the fundamental components, concepts and theory of neural network methods from econometric and statistic perspective, with particular focus on econometrically and statistically relevant models. In order to evaluate the relative effectiveness of econometric and neural network methods, two empirical studies are conducted by applying neural network methods in a methodological comparison fashion with traditional econometric models.Both neural networks and econometrics have similar models, common problems of modeling and interference. Neural networks and econometrics/statistics, particularly their discriminant methods, are two sides of the same coin in terms of the nature of modeling statistic issues. On one side, econometric models are sampling paradigm oriented methods, which estimate the distribution of the predictor variable separately for each class and combine these with the prior probabilities of each class occurring; while neural networks are one of the techniques based on diagnostic paradigm, which use theinformation from the samples to estimate the conditional probability of an observation belonging to each class, based on predictor variables. Hence, neural network and econometric/statistical methods (particularly, discriminant models) have the same properties, except that the natural parameterizations differ.The empirical studies indicate that neural network methods outperform or are as good as traditional econometric models including Multiple Regression Analysis, Linear Probability Model (LPM), and Logit model, in terms of minimizing the errors of in-sample predictions and out-of-sample forecasts. Although neural networks have some advantages over econometric methods, they have some limitations too. Hence, neural networks are perhaps best viewed as supplements to econometric methods in studying economic issues, and not necessarily as substitutes. |
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