| preface table of contents part i 1 what is econometrics? 1.1 introduction 1.2 a brief history 1.3 critiques of econometrics 1.4 looking ahead notes references 2 basic statistical concepts 2.1 introduction 2.2 methods of estimation 2.3 properties of estimators 2.4 hypothesis testing 2.5 confidence intervals 2.6 descriptive statistics notes problems references .appendix simple linear regression 3.1 introduction 312 least squares estimation and the classical assumptions 3.3 statistical properties of least squares 3.4 estimation of σ2 3.5 maximum likelihood estimatior 3.6 a measure of fit 3.7 prediction 3.8 residual analysis 3.9 numerical example 3.10 empirical example problems references appendix 4 multiple regression analysis 4.1 introduction 4.2 least squares estimation 4.3 residual interpretation of multiple regression estimates 4.4 overspecification and underspecification of the regression equation 4.5 r-squared versus r-bar-squared 4.6 testing linear restrictions 4.7 dummy variables note problems references appendix 5 violations of the classical assumptions 5.1 introduction 5.2 the zero mean assumption 5.3 stochastic explanatory variables 5.4 normality of the disturbances 5.5 heteroskedasticity 5.6 autocorrelation notes problems references 6 distributed lags and dynamic models 6.1 introduction 6.2 infinite distributed lag 6.2.1 adaptive expectations model (aem) 6.2.2 partial adjustment model (pam) 6.3 estimation and testing of dynamic models with serial correlation 6.3.1 a lagged dependent variable model with ar(1) disturbances 6.3.2 a lagged dependent variable model with ma(1) disturbances 6.4 autoregressive distributed lag note problems references part ii 7 the general linear model: the basics 7.1 introduction 7.2 least squares estimation 7.3 partitioned regression and the frisch-waugh-lovell theorem 7.4 maximum likelihood estimation 7.5 prediction 7.6 confidence intervals and test of hypotheses 7.7 joint confidence intervals and test of hypotheses 7.8 restricted mle and restricted least squares 7.9 likelihood ratio, wald and lagrange multiplier tests notes problems references appendix 8 regression diagnostics and specification tests 8.1 influential observations 8.2 recursive residuals 8.3 specification tests 8.4 nonlinear least squares and the gauss-newton regression 8.5 testing linear versus log-linear functional form notes problems references 9 generalized least squares 9.1 introduction 9.2 generalized least squares 9.3 special forms of ω 9.4 maximum likelihood estimation 9.5 test of hypotheses 9.6 prediction 9.7 unknown ω 9.8 the w, lr and lm statistics revisited 9.9 spatial error correlation note problems references 10 seemingly unrelated regressions 10.1 introduction 10.2 feasible gls estimation 10.3 testing diagonality of the variance-covariance matrix 10.4 seemingly unrelated regressions with unequal observations 10.5 empirical example problems references 11 simultaneous equations model 11.1 introduction 11.1.1 simultaneous bias 11.1.2 the identification problem 11.2 single equation estimation: two-stage least squares 11.3 system estimation: three-stage least squares 11.4 the identification problem revisited: the rank condition of identification 11.5 test for over-identification restrictions 11.6 hausman's specification test 11.7 empirical example note problems references 12 pooling time-series of cross-section data 12.1 introduction 12.2 the error components procedure 12.2.1 the fixed effects model 12.2.2 the random effects model 12.2.3 maximum likelihood estimation 12.2.4 prediction 12.2.5 empirical example 12.2.6 testing in a pooled model 12.3 time-wise autocorrelated and cross-sectionally heteroskedastic procedures 12.4 a comparison of the two procedures problems references 13 limited dependent variables 13.1 introduction 13.2 the linear probability model 13.3 functional form: logit and probit 13.4 grouped data 13.5 individual data: probit and logit 13.6 the binary response model regression 13.7 asymptotic variances for predictions and marginal effects 13.8 goodness of fit measures 13.9 empirical example 13.10 multinomial choice models 13.10.1 ordered response models 13.10.2 unordered response models 13.11 the censored regression model 13.12 the truncated regression model 13.13 sample selectivity notes problems references appendix 14 time-series analysis 14.1 introduction 14.2 stationarity 14.3 the box and jenkins method 14.4 vector autoregression 14.5 unit roots 14.6 trend stationary versus difference stationary 14.7 cointegration 14.8 autoregressive conditional heteroskedasticity note problems references appendix list of figures list of tables index |
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