Dynamic adjustment models of the Alberta beef industry under risk and uncertainty

Autor: Mbaga, Msafiri Daudi
Popis: The purpose of this thesis is to develop and estimate dynamic models of cow-calf and feedlot production decisions in Alberta under risk aversion and output price uncertainty. The thesis consists of two studies. The first study specifies and estimates reduced form Autoregressive Distributed Lag (ADL) and Polynomial Distributed Lag (PDL) models incorporating price uncertainty. ADL and PDL models are estimated assuming distributed lags for variance of output price. The sum of lagged coefficients for output price variance is negative and significant, as expected. The elasticity is much smaller than for the (positive) sum of lagged coefficients for expected price, as anticipated. The second study specifies and estimates dynamic Euler equation models of beef supply and investment under risk aversion and uncertainty. A beef output supply equation and an Euler equation for investment in breeding herd were specified assuming both linear and nonlinear mean-variance risk preferences. Results for the structural cow-calf models are consistent with economic theory. Output supply and investment are increasing in expected output price and decreasing in price variance, and the shadow price of capital is increasing in expected price and decreasing in price variance. There are indications that dynamics is less important in feedlot production than in cow-calf production, simply because biological lags are much shorter in feedlot production. Results for Euler equations suggest that feedlot investment decisions are influenced by expected output price variance, consistent with economic theory. To my knowledge, this is the first study of beef supply response to attempt to incorporate risk aversion. As a result, represents a significant departure from previous studies in the same area that have exclusively assumed risk neutrality by excluding the influence of uncertainty on decisions.
Databáze: Networked Digital Library of Theses & Dissertations