TITLE : ECONOMETRICS METHODS. 4TH ED.

 
CONTENTS
 
About the Authors         iii
 
Preface          xv
 
1. Relationships between Two Variables            1
 
1.1 Examples of Bivariate Relationships         1
1.2 The Correlation Coefficient       6
1.3 Probability Models for Two Variables         12
1.4 The Two-Variable Linear Regression Model        15
1.5 Inference in the Two-Variable, Least-Squares Model       23
1.6 Analysis of Variance in the Two-Variable Regression Model     29
1.7 Prediction in the Two-Variable Regression Model         31
1.8 Gasoline Consumption: A Preliminary Analysis       33
 
 
2. Further Aspect of Two-Variable Relationships       41
 
2.1 Time as a Regressor          42
2.2 Transformation of Variables        44
2.3 An Empirical Example of a Nonlinear Relation: U.S. Inflation
    and Unemployment          49
2.4 Lagged Dependent Variable as Regressor            52
2.5 Stationary and Nonstationary Series       57
2.6 Maximum Likelihood Estimation of the Autoregressive Equation   61
 
 
3. The k-Variable Linear Equation          69
 
3.1 Matrix Formulation of the k-Variable Model       70
3.2 Partial Correlation Coefficients          76
3.3 The Geometry of Least Squares              83
3.4 Inference in the k-Variable Equation         86
3.5 Prediction           99
 
 
4. Some Tests of the k-Variable Linear Equation for Specification
Error
109
 
4.1 Specification Error       109
4.2 Model Evaluation and Diagnostic Tests           112
4.3 Tests of Parameter Constancy             113
4.4 A Numerical Illustration            121
4.5 Tests of Structural Change       126
4.6 Dummy Variables            133
 
 
5. Maximum Likelihood (ML), Generalized Least Squares (GLS), and
   Instrumental Variable (IV) Estimators            142
 
5.1 Maximum Likelihood Estimators           142
5.2 ML Estimation of the Linear Model              145
5.3 Likelihood Ratio, Wald, and Lagrange Multiplier Tests          147
5.4 ML Estimation of the Linear Model with Nonspherical Disturbances
    151
5.5 Instrumental Variable (IV) Estimators         153
 
 
6. Heteroscedasticity and Autocorrelation        162
 
6.1 Properties of OLS Estimators       163
6.2 Tests for Heteroscedasticity          166
6.3 Estimation Under Heteroscedasticity          170
6.4 Autocorrelated Disturbances           174
6.5 OLS and Autocorrelated Disturbances        176
6.6 Testing for Autocorrelated Disturbances         178
6.7 Estimation of Relationships with Autocorrelated Disturbances
188
6.8 Forecasting with Autocorrelated Disturbances          192
6.9 Autoegressive conditional Heteroscedasticity (ARCH)     195
 
 
7. Univariate Time Series Modeling             204
 
7.1 A Rationale for Univariate Analysis           205
7.2 Properties of AR, MA and ARMA Processes          207
7.3 Testing for Stationarity           215
7.4 Identification, Estimation, and Testing of ARIMA Models		228
7.5 Forecasting	          231
7.6 Seasonality         235
7.7 A Numerical Example: Monthly Housing Starts          236
 
 
8. Autoregressive Distributed Lag Relationships          244
 
8.1 Autoregressive Distributed Lag Relations             244
8.2 Specification and Testing          248
8.3 Nonstationary Regressors        259
8.4 A Numerical Example          265
8.5 Nonnested Models            280
 
 
9. Multiple Equation Models       287
 
9.1 Vector Autoregressions (VARs)           287
9.2 Estimation of VARs          295
9.3 Vector Error Correction Models           301
9.4 Simultaneous Structural Equation Models            305
9.5 Identification Conditions                309
9.6 Estimation of Structural Equations         314
 
 
10. Generalized Method of Moments          327
 
10.1 The Method of Moments       328
10.2 OLS as a Moment Problem          329
10.3 Instrumental Variables as a Moment Problem         330
10.4 GMM and the Orthogonality Condition         333
10.5 Distribution of the GMM estimator         335
10.6 Applications          336
10.7 Readings         344
 
 
11. A Smorgasbord of Computationally Intensive Methods       348
 
11.1 An Introduction to Monte Carlo Methods          348
11.2 Monte Carlo Methods and Permutation Tests              359
11.3 The Bootstrap       362
11.4 Nonparametric Density Estimation     370
11.5 Nonparametric Regression       379
11.6 References        385
 
 
12. Panel Data        388
 
12.1 Sources and Types of Panel Data          389
12.2 The Simplest Case-The Pooled Estimator          390
12.3 Two Extensions to the Simple Model          390
12.4 The Random Effects Model           391
12.5 Random Effects as a Combination of Within and Between Estimators
     392
12.6 The Fixed Effects Model in the Two-Period Case       395
12.7 The Fixed Effects Model with More Than Two Time Periods       397
12.8 The Perils of Fixed Effects Estimation       399
12.9 Fixed Effects or Random Effects?
12.10 A Wu-Hausman Test        403
12.11 Other Specification Tests and an Introduction to Chamberlain's
      Approach             404
12.12 Readings       408
 
 
13. Discrete and Limited Dependent Variable Models       412
 
13.1 Types of Discrete Choice Models        412
13.2 The Linear Probability Model          414
13.3 Example: A Simple Descriptive Model of Union Participation    415
13.4 Formulating a Probability Model       418
13.5 The Probit        419
13.6 The Logit     424
13.7 Misspecification in Binary Dependent Models          426
13.8 Extensions to the Basic Model: Grouped Data         432
13.9 Ordered Probit       434
13.10 Tobit Models          436
13.11 Two Possible Solutions            441
13.12 Treatment Effects and Two-Step Methods          446
13.13 Readings         452
 
Appendix A
 
A.1 Vectors           455
 
A.2 Matrices          459
 
Appendix B
 
B.1  Random Variables and Probability Distributions          485
B.2 The Univariate Normal Probability Distribution          486
B.3 Bivariate Distributions       487
B.4 Relations between the Normal, x2, t, and F Distributions       489
B.5 Expectations in Bivariate Distributions        490
B.6 Multivariate Densities       490
B.7 Multivariate Normal pdf       492
B.8 Distributions of Quadratic Forms        493
B.9 Independence of Quadratic Forms     495
B.10 Independence of a Quadratic Form and a Linear Function        496
 
 
Appendix C              497
 
Appendix D           499
 
Index         521