Bayesian and nonBayesian Techniques for Forecasting Monthly Cattle Prices

Econometric and time series forecasting models for monthly prices of slaughter steers 1100-1300 pounds are evaluated using mean squared error and turning point criteria. The economic model is a two-equation recursive system of supply and demand whose reduced form is used as the forecasting equation for the econometric model and is also the information set on which all other models are based. The econometric analysis is based on constant, stochastic and Bayesian estimation procedures. The univariate time series models are estimated using Box-Jenkins techniques. Vector autoregressions (VARs), classical and Bayesian, comprise the multivariate time series models. The specification of VARs is based on the Scharwz Bayesian information (SBIC), Akaike's information (AIC) and final prediction error (FPE) criteria.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Agricultural Economics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Zapata, Hector O.

Contributors dc:contributor

• Garcia, Philip

• Economics, Agricultural

Identifier

(UMI)AAI8803244

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/69885