Matrix algebra; Probability abd distribution theory; Statistical inference; Computation and optimization; The classical multiple linear regression model - specification and estimation; Inference and prediction; Functional form, nonlinearity, and specification; Data problems; Nonlinear regression models; Nonspherical disturbances; generalized regression, and GMM estimation; Autocorrelated disturbances; Models for panel data; Systems of regression equations; Regressions with lagged variables; Time-series models; Models with discrete dependent variables; Limited dependent variable and duration models.
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analysis assume assumption asymptotic covariance matrix asymptotic distribution autocorrelation Chapter characteristic roots chi-squared chi-squared distribution classical regression model coefficients column computed consider consistent estimator constant term converges correlation critical value data set degrees of freedom dependent variable derivatives diagonal discussed dummy variable earlier econometrics efficient equal equation example FGLS estimator given heteroscedasticity homoscedasticity implies income iteration lag model Lagrange multiplier Lagrange multiplier test least squares estimator least squares regression least squares residuals likelihood function log likelihood log-likelihood function log-linear model maximum likelihood estimator mean method Newton's method nonlinear regression normally distributed Note observations obtain OLS estimator ordinary least squares plim probability probit model problem produces random variable regressors restrictions Section slope solution standard errors standard normal sum of squares Suppose Table test statistic theorem tion unbiased estimator uncorrelated Var[b Var[e variance matrix Wald statistic Wald test zero