This book introduces students to applied econometrics including basic techniques in regression analysis and some of the rich variety of models that are used.Includes self-contained summaries of the matrix algebra, statistical theory, and mathematical statistics used in the book. Covers Estimator, ML, GMM, and 2 step; Panel data, heteroscedasticity, qualitative responsive models, limited dependent variables, and emphasizes nonlinear models. Also discusses current topics in applied econometrics, such as GMM estimation methods, Lagrange multiplier tests, and time series analysis.
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apply assume assumption asymptotic covariance matrix asymptotic distribution autocorrelation Chapter characteristic roots chi-squared chi-squared distribution coefﬁcients cointegrating column computed consider consistent estimator constant term converges correlation covariance matrix critical value deﬁned degrees of freedom density derivatives diagonal difﬁcult discussion disturbances dummy variable econometrics effects efﬁcient equal equation esti example FGLS estimator ﬁnd ﬁnite ﬁrms ﬁrst ﬁt ﬁxed GMM estimator heteroscedasticity homoscedasticity identiﬁed income instrumental variables iteration Lagrange multiplier least squares estimator likelihood function likelihood ratio linear regression log-likelihood log-likelihood function logit model mator maximum likelihood estimator mean method normal distribution observations obtain ordinary least squares parameters plim Poisson positive deﬁnite probability probit model problem produces random variable regressors restrictions sample Section signiﬁcant slope solution speciﬁcation squared residuals standard errors standard normal sum of squares Suppose Table test statistic THEOREM tion Var[b variance vector Wald test zero