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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apply assume assumption asymptotic covariance matrix asymptotic distribution autocorrelation Chapter characteristic roots chi-squared chi-squared distribution classical regression model coefficients cointegrating column computed consider consistent estimator constant term converges correlation covariance matrix critical value data set degrees of freedom density dependent variable derivatives diagonal discussion disturbances dummy variable earlier econometrics efficient equal equation esti example FGLS estimator finite given GMM estimator heteroscedasticity homoscedasticity income instrumental variables inverse iteration Lagrange multiplier least squares estimator likelihood function likelihood ratio linear model linear regression log-likelihood log-likelihood function logit model mator maximum likelihood estimator mean method multivariate normal distribution observations obtain ordinary least squares parameters plim Poisson positive definite probability probit model problem produces random variable regressors restrictions sample Section slope solution specification squared residuals standard errors standard normal sum of squares Suppose Table test statistic Theorem tion variance vector Wald statistic Wald test zero