Econometric AnalysisFor a one-year graduate course in Econometrics. This text has two objectives. The first is to introduce students to applied econometrics, including basic techniques in regression analysis and some of the rich variety of models that are used when the linear model proves inadequate or inappropriate. The second is to present students with sufficient theoretical background that they will recognize new variants of the models learned about here as merely natural extensions that fit within a common body of principles. The Fifth Edition features a complete update of techniques and developments, a reorganization of material for improved presentation, and new material and applications. |
Contents
Introduction | 6 |
The Classical Multiple Linear Regression Model | 7 |
Least Squares | 19 |
Copyright | |
39 other sections not shown
Other editions - View all
Common terms and phrases
analysis application assumed assumption asymptotic covariance matrix asymptotically normally autocorrelation B₁ B₂ Chapter chi-squared chi-squared distribution coefficients cointegrating column computed conditional consider consistent estimator constant term convergence correlation covariance matrix critical value data set degrees of freedom denote density dependent variable derivatives discussed disturbances dummy variable Econometrics equal equation estimator of ẞ example FGLS finite GMM estimator heteroscedasticity homoscedasticity income instrumental variables iteration Lagrange multiplier least squares estimator likelihood function linear model linear regression log-likelihood log-likelihood function logit model maximum likelihood estimator mean method nonlinear regression normal distribution null hypothesis observations obtain ordinary least squares panel data parameter vector plim probability probit model problem produces random variable regressors restrictions sample Section specification standard errors standard normal suggested sum of squares Table test statistic Theorem variance Wald test x₁ y₁ zero