A dynamic model of the U.S. cotton market with rational expectations

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Date
1989
Authors
Tsai, Grace
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William H. Meyers
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Economics

The Department of Economic Science was founded in 1898 to teach economic theory as a truth of industrial life, and was very much concerned with applying economics to business and industry, particularly agriculture. Between 1910 and 1967 it showed the growing influence of other social studies, such as sociology, history, and political science. Today it encompasses the majors of Agricultural Business (preparing for agricultural finance and management), Business Economics, and Economics (for advanced studies in business or economics or for careers in financing, management, insurance, etc).

History
The Department of Economic Science was founded in 1898 under the Division of Industrial Science (later College of Liberal Arts and Sciences); it became co-directed by the Division of Agriculture in 1919. In 1910 it became the Department of Economics and Political Science. In 1913 it became the Department of Applied Economics and Social Science; in 1924 it became the Department of Economics, History, and Sociology; in 1931 it became the Department of Economics and Sociology. In 1967 it became the Department of Economics, and in 2007 it became co-directed by the Colleges of Agriculture and Life Sciences, Liberal Arts and Sciences, and Business.

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1898–present

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  • Department of Economic Science (1898–1910)
  • Department of Economics and Political Science (1910-1913)
  • Department of Applied Economics and Social Science (1913–1924)
  • Department of Economics, History and Sociology (1924–1931)
  • Department of Economics and Sociology (1931–1967)

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Abstract

This study presents a dynamic rational expectations model for the U.S. cotton market. The dynamic decision rules are derived from the cotton farmer and miller optimization problems; and the equilibrium movements of prices, production, land allocation, and mill consumption are solved analytically. The dynamic element in the cotton farmer and miller problems come from the cost functions. In the cotton cost function a sequential adjustment cost is used while a quadratic cost function is used in the cotton yarns cost function. These optimal decision rules are derived as functions of past values of these decision variables, expectations of future product prices, and other exogenous variables. Assuming rational expectations and knowing the orders of the Markov-processes for the relevant state variables and the disturbances, closed-form regression equations representing decision rules and stochastic processes are obtained. Then, the VAR approach and Granger-causality test are used to obtain information which help to forecast the relevant state variables at the first stage of estimation. With the specific assumption on the errors, a dynamic mill demand for cotton is estimated by using the method of nonlinear least squares and tested by using the likelihood ratio. The empirical results provide some support for the specific model. Furthermore, the empirical model provides a framework for policy evaluation.

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Sun Jan 01 00:00:00 UTC 1989