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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assume assumption asymptotic covariance matrix asymptotic distribution biased Chapter characteristic roots chi-squared distribution classical regression model column computed confidence interval consider consistent estimator constant term convergence correlation covariance matrix critical value degrees of freedom density derivatives deviations discussion disturbance dummy variable earlier econometrics equal equation example expected value F statistic forecast Gauss–Markov theorem given heteroscedasticity implies income independent Lagrange multiplier Lagrange multiplier test least squares estimator least squares regression likelihood function limiting distribution linear model log likelihood log-linear model maximum likelihood estimator mean square measure multiple regression nonlinear nonlinear regression nonstochastic normal distribution observations obtain ordinary least squares percent plim positive definite probability problem quadratic form random variable regressors restrictions Section slope solution standard errors standard normal sum of squares Suppose Table test statistic test the hypothesis theorem tion unbiased estimator variance matrix variation Wald test zero