Lecture09-2011 - Outline Basic Setup Generalize Least...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Outline Basic Setup Generalize Least Squares Estimator Estimation of Production Functions - Random Effects in Panel Data : Lecture IX Charles B. Moss 1 1 University of Florida September 22, 2011 Charles B. Moss Estimation of Production Functions - Random Effects in Panel Outline Basic Setup Generalize Least Squares Estimator 1 Basic Setup Mother of all Heteroscedasticity Variance Matrix 2 Generalize Least Squares Estimator Procedure for Estimation Help Me! System Specification Charles B. Moss Estimation of Production Functions - Random Effects in Panel Outline Basic Setup Generalize Least Squares Estimator Mother of all Heteroscedasticity Variance Matrix Basic Setup Regression analysis typically assumes that a large number of factors affect the value of the dependent variable, while some of the variables are measured directly in the model the remaining variables can be summarized by a random distribution y it = + x it + z it + it = ( + E [ z it ]) + x it + ( it + [ z it- E [ z it ]]) (1) When numerous observations on are observed over time, it is assumed that some of the omitted variables represent factors peculiar to individual and time periods. Going back to the panel specification y it = * + x it + it it = i + t + it (2) Charles B. Moss Estimation of Production Functions - Random Effects in Panel Outline Basic Setup Generalize Least Squares Estimator Mother of all Heteroscedasticity Variance Matrix i is the individual variation in the constant, t is the time variation in the constant, and u it is the pure error term. For identification purposes, we assume The expected value of each of these components is equal to zero E [ i ] = E [ t ] = E [ u it ] = 0 (3) We also assume that each of these error terms are uncorrelated E [...
View Full Document

This note was uploaded on 11/08/2011 for the course AEB 6184 taught by Professor Staff during the Spring '09 term at University of Florida.

Page1 / 15

Lecture09-2011 - Outline Basic Setup Generalize Least...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online