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 distribution classical regression model coefficients column computed consider consistent estimator constant term converges correlation covariance matrix critical value data set degrees of freedom density dependent variable derivatives diagonal discussed disturbances dummy variable earlier econometrics efficient equal equation example F statistic FGLS estimator finite given heteroscedasticity homoscedasticity implies income instrumental variables inverse iteration lag model Lagrange multiplier least squares estimator least squares regression likelihood function log likelihood log-likelihood function maximum likelihood estimator mean method nonlinear nonlinear regression normal distribution observations obtain OLS estimator ordinary least squares plim polynomial positive definite probability probit model problem produces random variable regressors restrictions ri ri ri sample Section slope solution standard errors standard normal sum of squares Suppose Table test statistic theorem tion truncated unbiased estimator variance matrix Wald test zero