Title page for ETD etd-11152006-205531

Type of Document Dissertation
Author Sulgham, Anil Kumar
Author's Email Address asulgh1@lsu.edu
URN etd-11152006-205531
Title Econometric Essays on Specification and Estimation of Demand Systems
Degree Doctor of Philosophy (Ph.D.)
Department Agricultural Economics & Agribusiness
Advisory Committee
Advisor Name Title
Hector O. Zapata Committee Chair
Krishna K. Paudel Committee Member
R. Wes Harrison Committee Member
Roger A. Hinson Committee Member
Sudipta Sarangi Committee Member
Dick R. Parish Dean's Representative
  • Dynamic Models
  • Static Models
  • AIDS Model
  • Demand Systems
  • U.S. Meats
  • Parametric
  • Semiparametric
Date of Defense 2006-10-25
Availability unrestricted
This dissertation focuses on two research themes related to econometric estimation of

linear almost ideal demand systems (LAIDS) for U.S. meats. The first theme addresses whether

nonstationarity (unit-roots and cointegration) contributes to a dynamic specification of LAIDS

models. The results of the effect of nonstationarity are reported in two case studies. The second theme explores the relationship between age and household size with budget shares to specify semiparametric LAIDS model. The results are reported in a third case study that compares parametric and semiparametric models estimates of price and expenditure elasticities.

The first case study conducts a comparative analysis of elasticity estimates from static

and dynamic LAIDS models. Historical meat consumption data (1975:1-2002:4) for beef, pork

and poultry products were used. Hylleberg et al. (1990) seasonal unit roots tests were conducted.

Unit roots and cointegration analysis lead to the specification of an ECM of the Engle-Granger

type for the LAIDS model. Marshallian and compensated elasticities were generated from the

static and dynamic LAIDS models. The study found some model differences in elasticity

estimates and rejected homogeneity in the dynamic model.

The second case study evaluates the forecasting performance of static and dynamic

LAIDS models. Forecast evaluation was based on mean square error (MSE) criteria and recently

developed MSE-tests. The study found ECM-LAIDS model performs uniformly better under all

forecasting horizons for the beef equation. However, in the case of the pork equation the static model performed better in one-step-ahead and two-step-ahead forecasting horizons while the

dynamic model was superior in the three-step-ahead and four-step-ahead forecasting horizons

using MSE comparisons. In testing, only the two-steps ahead was superior for pork.

The third case study specifies a semiparametric LAIDS model that maintains the linearity

assumption of prices and total expenditures and allows nonparametric effects of age and

household size. 2003 U.S. Consumer Expenditure Survey data for four meat products (beef,

pork, poultry and seafood) were used in the study. Model fit and elasticity estimates revealed negligible differences exist between parametric and semiparametric models.

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