Applied Econometrics using MATLAB

gssdzc

James P. LeSage写的基于Matlab的应用计量经济学

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 profiling 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

3 Utility Functions

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 4 Regression Diagnostics 77 80

4.1 Collinearity diagnostics and procedures ............ 80
4.2 Outlier diagnostics and procedures ............... 94
4.3 Chapter summary ........................ 100


Chapter 4 Appendix 5 VAR and Error Correction Models 101 103
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 6 Markov Chain Monte Carlo Models 148 151
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 7 Limited Dependent Variable Models 199 204

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 8 Simultaneous Equation Models 228 230

8.1 Two-stage least-squares models ................. 230
8.2 Three-stage least-squares models ................ 235
8.3 Seemingly unrelated regression models ............. 240

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 10 Optimization functions library 257 260

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 11 Handling sparse matrices 288 289

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

Aniu

顶一个！

wangzhiqing1993

赞赞赞！

BrainZou

赞赞赞，顶一个