Stochastic production function and estimating risk in rice production

Date

10-1980

Abstract

Risk is primarily defined as variability of outcome in conjunction with expected outcome. In broader sense, risk estimation covers, in the case of rice production, the estimation of the probability distribution of rice yield. The major intent of this study is to investigate the sensitivity of risk measurement to functional form of and estimation technique for the production function. Specifically, the effect of nitrogen input on the probability distribution of rice yield outcome at nine agronomic zonal levels stratified according to season and water stress conditions is measured.

Stochastic production functions incorporating manageable inputs and environmental factors are estimated for 1972-1977 rice data from farmers' fields in Central Luzon, Philippines. Five basic models with different sets of linear and interaction stress variables are used. The multiple regression model with normally distributed zero mean constant variance errors is assumed to approximate the production process. Initial coefficient estimates are obtained by ordinary least squares.For a sweep among empirical models indexed by transformation of the response variables, Box-Cox regression is performed. With the obtained response functions as input information, rice yield distributions at fixed levels of nitrogen and given solar radiation and water stress conditions are simulated. As descriptors of risk effects of nitrogen, the means, variances and skewness of simulated yield distributions are analyzed. It is observed that the risk effect of nitrogen is highly conditioned by solar radiation and water stress. Results indicate that the method of risk measurement used is not sensitive to the production function estimation technique. Therefore, the superiority of the simple and computationally economical least squares estimation for the standard multiple regression model is asserted. Differences in inferences on risk effects between models indicate that model formulation poses a greater problem.

Document Type

Master Thesis

Degree

Master of Science in Statistics

College

Graduate School (GS)

Adviser/Committee Chair

Ann Inez N. Gironella

Co-adviser

Isidoro P. David

Committee Member

Manuel L. Bonita

Language

English

Location

UPLB Main Library Special Collections Section (USCS)

Call Number

LG 995 1980 S81 M45

DOI

https://www.ukdr.uplb.edu.ph/cgi/ir_submit.cgi?context=etd-grad&edbypass=1&editpanel=

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