Ebook_Applied Econometrics using SAS and .......-经管之家官网！

Here is several ebook.
1)   Applied Econometrics using SAS System
2)  Applied Econometrics using MATLAB
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(SAS book)
Contents:
Preface xi
Acknowledgments xv
1 Introduction to Regression Analysis 1
1.1 Introduction 1
1.2 Matrix Form of the Multiple Regression Model 3
1.3 Basic Theory of Least Squares 3
1.4 Analysis of Variance 5
1.5 The Frisch–Waugh Theorem 6
1.6 Goodness of Fit 6
1.7 Hypothesis Testing and Confidence Intervals 7
1.8 Some Further Notes 8
2 Regression Analysis Using Proc IML and Proc Reg 9
2.1 Introduction 9
2.2 Regression Analysis Using Proc IML 9
2.3 Analyzing the Data Using Proc Reg 12
2.4 Extending the Investment Equation Model to the Complete Data Set 14
2.5 Plotting the Data 15
2.6 Correlation Between Variables 16
2.7 Predictions of the Dependent Variable 18
2.8 Residual Analysis 21
2.9 Multicollinearity 24
3 Hypothesis Testing 27
3.1 Introduction 27
3.2 Using SAS to Conduct the General Linear Hypothesis 29
3.3 The Restricted Least Squares Estimator 31
3.4 Alternative Methods of Testing the General Linear Hypothesis 33
3.5 Testing for Structural Breaks in Data 38
3.6 The CUSUM Test 41
3.7 Models with Dummy Variables 45
vii
4 Instrumental Variables 52
4.1 Introduction 52
4.2 Omitted Variable Bias 53
4.3 Measurement Errors 54
4.4 Instrumental Variable Estimation 55
4.5 Specification Tests 61
5 Nonspherical Disturbances and Heteroscedasticity 70
5.1 Introduction 70
5.2 Nonspherical Disturbances 71
5.3 Detecting Heteroscedasticity 72
5.4 Formal Hypothesis Tests to Detect Heteroscedasticity 74
5.5 Estimation of b Revisited 80
5.6 Weighted Least Squares and FGLS Estimation 84
5.7 Autoregressive Conditional Heteroscedasticity 87
6 Autocorrelation 93
6.1 Introduction 93
6.2 Problems Associated with OLS Estimation Under Autocorrelation 94
6.3 Estimation Under the Assumption of Serial Correlation 95
6.4 Detecting Autocorrelation 96
6.5 Using SAS to Fit the AR Models 101
7 Panel Data Analysis 110
7.1 What is Panel Data? 110
7.2 Panel Data Models 111
7.3 The Pooled Regression Model 112
7.4 The Fixed Effects Model 113
7.5 Random Effects Models 123
8 Systems of Regression Equations 132
8.1 Introduction 132
8.2 Estimation Using Generalized Least Squares 133
8.3 Special Cases of the Seemingly Unrelated Regression Model 133
8.4 Feasible Generalized Least Squares 134
9 Simultaneous Equations 142
9.1 Introduction 142
9.2 Problems with OLS Estimation 142
9.3 Structural and Reduced Form Equations 144
9.4 The Problem of Identification 145
9.5 Estimation of Simultaneous Equation Models 147
9.6 Hausman’s Specification Test 151
10 Discrete Choice Models 153
10.1 Introduction 153
10.2 Binary Response Models 154
10.3 Poisson Regression 163
viii CONTENTS
11 Duration Analysis 169
11.1 Introduction 169
11.2 Failure Times and Censoring 169
11.3 The Survival and Hazard Functions 170
11.4 Commonly Used Distribution Functions in Duration Analysis 178
11.5 Regression Analysis with Duration Data 186
12 Special Topics 202
12.1 Iterative FGLS Estimation Under Heteroscedasticity 202
12.2 Maximum Likelihood Estimation Under Heteroscedasticity 202
12.3 Harvey’s Multiplicative Heteroscedasticity 204
12.4 Groupwise Heteroscedasticity 205
12.5 Hausman–Taylor Estimator for the Random Effects Model 210
12.6 Robust Estimation of Covariance Matrices in Panel Data 219
12.7 Dynamic Panel Data Models 220
12.8 Heterogeneity and Autocorrelation in Panel Data Models 224
12.9 Autocorrelation in Panel Data 227
Appendix A Basic Matrix Algebra for Econometrics 237
A.1 Matrix Definitions 237
A.2 Matrix Operations 238
A.3 Basic Laws of Matrix Algebra 239
A.4 Identity Matrix 240
A.5 Transpose of a Matrix 240
A.6 Determinants 241
A.7 Trace of a Matrix 241
A.8 Matrix Inverses 242
A.9 Idempotent Matrices 243
A.10 Kronecker Products 244
A.11 Some Common Matrix Notations 244
A.12 Linear Dependence and Rank 245
A.13 Differential Calculus in Matrix Algebra 246
A.14 Solving a System of Linear Equations in Proc IML 248
Appendix B Basic Matrix Operations in Proc IML 249
B.1 Assigning Scalars 249
B.2 Creating Matrices and Vectors 249
B.3 Elementary Matrix Operations 250
B.4 Comparison Operators 251
B.5 Matrix-Generating Functions 251
B.6 Subset of Matrices 251
B.7 Subscript Reduction Operators 251
B.8 The Diag and VecDiag Commands 252
B.9 Concatenation of Matrices 252
B.10 Control Statements 252
B.11 Calculating Summary Statistics in Proc IML 253
Appendix C Simulating the Large Sample Properties of the OLS Estimators 255
Appendix D Introduction to Bootstrap Estimation 262
D.1 Introduction 262
D.2 Calculating Standard Errors 264
CONTENTS ix
D.3 Bootstrapping in SAS 264
D.4 Bootstrapping in Regression Analysis 265
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(Matlab book)
Contents
1 Introduction 1
2 Regression using MATLAB 5
2.1 Design of the regression library . . . . . . . . . . . . . . . . . 6
2.2 The ols function . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Selecting a least-squares algorithm . . . . . . . . . . . . . . . 12
2.4 Using the results structure . . . . . . . . . . . . . . . . . . . . 17
2.5 Performance pro¯ling the regression toolbox . . . . . . . . . . 28
2.6 Using the regression library . . . . . . . . . . . . . . . . . . . 30
2.6.1 A Monte Carlo experiment . . . . . . . . . . . . . . . 31
2.6.2 Dealing with serial correlation . . . . . . . . . . . . . 32
2.6.3 Implementing statistical tests . . . . . . . . . . . . . . 38
2.7 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 41
Chapter 2 Appendix 42
3 Utility Functions 45
3.1 Calendar function utilities . . . . . . . . . . . . . . . . . . . . 45
3.2 Printing and plotting matrices . . . . . . . . . . . . . . . . . 49
3.3 Data transformation utilities . . . . . . . . . . . . . . . . . . 65
3.4 Gauss functions . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.5 Wrapper functions . . . . . . . . . . . . . . . . . . . . . . . . 73
3.6 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 76
Chapter 3 Appendix 77
4 Regression Diagnostics 80
4.1 Collinearity diagnostics and procedures . . . . . . . . . . . . 80
4.2 Outlier diagnostics and procedures . . . . . . . . . . . . . . . 94
4.3 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 100
vi
CONTENTS vii
Chapter 4 Appendix 101
5 VAR and Error Correction Models 103
5.1 VAR models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.2 Error correction models . . . . . . . . . . . . . . . . . . . . . 113
5.3 Bayesian variants . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.3.1 Theil-Goldberger estimation of these models . . . . . 138
5.4 Forecasting the models . . . . . . . . . . . . . . . . . . . . . . 139
5.5 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 145
Chapter 5 Appendix 148
6 Markov Chain Monte Carlo Models 151
6.1 The Bayesian Regression Model . . . . . . . . . . . . . . . . . 154
6.2 The Gibbs Sampler . . . . . . . . . . . . . . . . . . . . . . . . 156
6.2.1 Monitoring convergence of the sampler . . . . . . . . . 159
6.2.2 Autocorrelation estimates . . . . . . . . . . . . . . . . 163
6.2.3 Raftery-Lewis diagnostics . . . . . . . . . . . . . . . . 163
6.2.4 Geweke diagnostics . . . . . . . . . . . . . . . . . . . . 165
6.3 A heteroscedastic linear model . . . . . . . . . . . . . . . . . 169
6.4 Gibbs sampling functions . . . . . . . . . . . . . . . . . . . . 175
6.5 Metropolis sampling . . . . . . . . . . . . . . . . . . . . . . . 184
6.6 Functions in the Gibbs sampling library . . . . . . . . . . . . 190
6.7 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 197
Chapter 6 Appendix 199
7 Limited Dependent Variable Models 204
7.1 Logit and probit regressions . . . . . . . . . . . . . . . . . . . 206
7.2 Gibbs sampling logit/probit models . . . . . . . . . . . . . . . 211
7.2.1 The probit g function . . . . . . . . . . . . . . . . . . 218
7.3 Tobit models . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
7.4 Gibbs sampling Tobit models . . . . . . . . . . . . . . . . . . 224
7.5 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 227
Chapter 7 Appendix 228
8 Simultaneous Equation Models 230
8.1 Two-stage least-squares models . . . . . . . . . . . . . . . . . 230
8.2 Three-stage least-squares models . . . . . . . . . . . . . . . . 235
8.3 Seemingly unrelated regression models . . . . . . . . . . . . . 240
CONTENTS viii
8.4 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 244
Chapter 8 Appendix 246
9 Distribution functions library 247
9.1 The pdf, cdf, inv and rnd functions . . . . . . . . . . . . . . . 248
9.2 The specialized functions . . . . . . . . . . . . . . . . . . . . 249
9.3 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 256
Chapter 9 Appendix 257
10 Optimization functions library 260
10.1 Simplex optimization . . . . . . . . . . . . . . . . . . . . . . . 261
10.1.1 Univariate simplex optimization . . . . . . . . . . . . 261
10.1.2 Multivariate simplex optimization . . . . . . . . . . . 268
10.2 EM algorithms for optimization . . . . . . . . . . . . . . . . . 269
10.3 Multivariate gradient optimization . . . . . . . . . . . . . . . 278
10.4 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 287
Chapter 10 Appendix 288
11 Handling sparse matrices 289
11.1 Computational savings with sparse matrices . . . . . . . . . . 289
11.2 Estimation using sparse matrix algorithms . . . . . . . . . . . 297
11.3 Gibbs sampling and sparse matrices . . . . . . . . . . . . . . 304
11.4 Chapter summary . . . . . . . . . . . . . . . . . . . . . . . . 309
Chapter 11 Appendix 310
References 313
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