| 计量经济学-第5版 |
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2020-06-05 00:00:00 |
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计量经济学-第5版 内容简介
this book is intended for a first year graduate course in
econometrics. i tried to strike a balance between a rigorous
approach that proves theorems， and a completely empirical approach
where no theorems are proved. some of the strengths of this book
lie in presenting some difficult material in a simple， yet rigorous
manner. for example， chapter 12 on pooling time-series of
cross-section data is drawn from my area of expertise in
econometrics and the intent here is to make this material more
accessible to the general readership of econometrics.
计量经济学-第5版 目录
preface
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
3 simple linear regression
3.1 introduction
3.2 least squares estimation and the classical assumptions
3.3 statistical properties of least squares
3.4 estimation of er2
3.5 maximum likelihood estimation
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 regressionequation
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 serialcorrelation
6.3.1 a lagged dependent variable model with ar（l）disturbances
6.3.2 a lagged dependent variable model with ma（l）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-lovelltheorem
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 unequalobservations
10.5 empirical examples
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
11.2.1 spatial lag dependence
11.3 system estimation: three-stage least squares
11.4 test for over-identification restrictions
11.5 hausman's specification test
11.6 empiri,cal examples
notes
problems
references
appendix
12 pooling time-series of cross-section data
12.1 introduction
12.2 the error components model
12.2.1 the fixed effects model
12.2.2 the random effects model
12.2.3 maximum likelihood estimation
12.3 prediction
12.4 empirical example
12.5 testing in a pooled model
12.6 dynamic panel data models
12.6.1 empirical illustration
12.7 program evaluation and difference-in-differencesestimator
12.7.1 the difference-in-differences estimator
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 marginaleffects
13.8 goodness of fit measures
13.9 empirical examples
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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