For graduate-level courses in Introduction to Econometrics. A standard text/reference in courses that include basic techniques in regression analysis and extensions used when linear models prove inadequate or inappropriate. Areas of application include Economics, Sociology, Political Science, Medical Research, Transport Research, and Environmental Economics. This book introduces students to the broad field of applied econometrics. An effective bridge to both on-the-job problems and to the professional literature, it features extensive applications and presents sufficient theoretical background to enable students to recognize new variants of the models that they learn about here as merely natural extensions that fit within a common body of principles.
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analysis apply assume assumption asymptotic covariance matrix asymptotic distribution autocorrelation Chapter chi-squared distribution coefficients cointegration column computed conditional 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 forecast given GMM estimator heteroscedasticity homoscedasticity income instrumental variables inverse iteration Lagrange multiplier least squares estimator least squares residuals likelihood function likelihood ratio linear model linear regression log-likelihood log-likelihood function logit model maximum likelihood estimator method nonlinear regression normal distribution observations obtain ordinary least squares parameters plim Poisson probability probit model problem produces random variable regressors restrictions Section slope solution specification standard errors standard normal suggested sum of squares Suppose Table test statistic Theorem time-series tion truncated unbiased unrestricted variance vector Wald statistic Wald test zero