Microeconometrics using Stata:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Texas
Stata Press
2009
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis Seite [665] - 672 ; Personen- u. Sachindex |
| Beschreibung: | xl, 692 Seiten Illustrationen |
| ISBN: | 9781597180481 1597180483 |
Internformat
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| 100 | 1 | |a Cameron, Adrian Colin |d 1956- |e Verfasser |0 (DE-588)12870022X |4 aut | |
| 245 | 1 | 0 | |a Microeconometrics using Stata |c A. Colin Cameron ; Pravin K. Trivedi |
| 264 | 1 | |a College Station, Texas |b Stata Press |c 2009 | |
| 300 | |a xl, 692 Seiten |b Illustrationen | ||
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| 500 | |a Literaturverzeichnis Seite [665] - 672 ; Personen- u. Sachindex | ||
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Datensatz im Suchindex
| _version_ | 1804138462477025280 |
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| adam_text | Contents
List of tables xxxv
List of figures xxxvii
Preface xxxix
Stata basics 1
1.1 Interactive use.............................. 1
1.2 Documentation.............................. 2
1.2.1 Stata manuals.......................... 2
1.2.2 Additional Stata resources................... 3
1.2.3 The help command....................... 3
1.2.4 The search, findit, and hsearch commands.......... 4
1.3 Command syntax and operators..................... 5
1.3.1 Basic command syntax..................... 5
1.3.2 Example: The summarize command ............. 6
1.3.3 Example: The regress command................ 7
1.3.4 Abbreviations, case sensitivity, and wildcards........ 9
1.3.5 Arithmetic, relational, and logical operators......... 9
1.3.6 Error messages......................... 10
1.4 Do-files and log files........................... 10
1.4.1 Writing a do-file......................... 10
1.4.2 Running do-files......................... 11
1.4.3 Log files............................. 12
1.4.4 A three-step process...................... 13
1.4.5 Comments and long lines.................... 13
1.4.6 Different implementations of Stata.............. 14
vi Contents
1.5 Scalars and matrices........................... 15
1.5.1 Scalars.............................. 15
1.5.2 Matrices............................. 15
1.6 Using results from Stata commands................... 16
1.6.1 Using results from the r-class command summarize..... 16
1.6.2 Using results from the e-class command regress....... 17
1.7 Global and local macros......................... 19
1.7.1 Global macros.......................... 19
1.7.2 Local macros .......................... 20
1.7.3 Scalar or macro? ........................ 21
1.8 Looping commands............................ 22
1.8.1 The foreach loop ........................ 23
1.8.2 The forvalues loop ....................... 23
1.8.3 The while loop ......................... 24
1.8.4 The continue command..................... 24
1.9 Some useful commands.......................... 24
1.10 Template do-file.............................. 25
1.11 User-written commands......................... 25
1.12 Stata resources.............................. 26
1.13 Exercises.................................. 26
2 Data management and graphics 29
2.1 Introduction................................ 29
2.2 Types of data............................... 29
2.2.1 Text or ASCII data....................... 30
2.2.2 Internal numeric data...................... 30
2.2.3 String data ........................... 31
2.2.4 Formats for displaying numeric data............. 31
2.3 Inputting data .............................. 32
2.3.1 General principles........................ 32
2.3.2 Inputting data already in Stata format............ 33
Contents vii
2.3.3 Inputting data from the keyboard............... 34
2.3.4 Inputting nontext data..................... 34
2.3.5 Inputting text data from a spreadsheet............ 35
2.3.6 Inputting text data in free format............... 36
2.3.7 Inputting text data in fixed format.............. 36
2.3.8 Dictionary files......................... 37
2.3.9 Common pitfalls ........................ 37
2.4 Data management ............................ 38
2.4.1 PSID example.......................... 38
2.4.2 Naming and labeling variables................. 41
2.4.3 Viewing data.......................... 42
2.4.4 Using original documentation................. 43
2.4.5 Missing values.......................... 43
2.4.6 Imputing missing data..................... 45
2.4.7 Transforming data (generate, replace, egen, recode)..... 45
The generate and replace commands............. 46
The egen command....................... 46
The recode command...................... 47
The by prefix.......................... 47
Indicator variables ....................... 47
Set of indicator variables.................... 48
Interactions........................... 49
Demeaning............................ 50
2.4.8 Saving data........................... 51
2.4.9 Selecting the sample...................... 51
2.5 Manipulating datasets.......................... 53
2.5.1 Ordering observations and variables.............. 53
2.5.2 Preserving and restoring a dataset .............. 53
2.5.3 Wide and long forms for a dataset .............. 54
viii Contents
2.5.4 Merging datasets........................ 54
2.5.5 Appending datasets....................... 56
2.6 Graphical display of data ........................ 57
2.6.1 Stata graph commands..................... 57
Example graph commands................... 57
Saving and exporting graphs.................. 58
Learning how to use graph commands ............ 59
2.6.2 Box-and-whisker plot...................... 60
2.6.3 Histogram............................ 61
2.6.4 Kernel density plot....................... 62
2.6.5 Twoway scatterplots and fitted lines ............. 64
2.6.6 Lowess, kernel, local linear, and nearest-neighbor regression 65
2.6.7 Multiple scatterplots...................... 67
2.7 Stata resources.............................. 68
2.8 Exercises.................................. 68
3 Linear regression basics 71
3.1 Introduction................................ 71
3.2 Data and data summary......................... 71
3.2.1 Data description ........................ 71
3.2.2 Variable description....................... 72
3.2.3 Summary statistics....................... 73
3.2.4 More-detailed summary statistics............... 74
3.2.5 Tables for data......................... 75
3.2.6 Statistical tests......................... 78
3.2.7 Data plots............................ 78
3.3 Regression in levels and logs....................... 79
3.3.1 Basic regression theory..................... 79
3.3.2 OLS regression and matrix algebra.............. 80
3.3.3 Properties of the OLS estimator................ 81
3.3.4 Heteroskedasticity-robust standard errors .......... 82
Contents ix
3.3.5 Cluster-robust standard errors................ 82
3.3.6 Regression in logs........................ 83
3.4 Basic regression analysis......................... 84
3.4.1 Correlations........................... 84
3.4.2 The regress command ..................... 85
3.4.3 Hypothesis tests......................... 86
3.4.4 Tables of output from several regressions........... 87
3.4.5 Even better tables of regression output............ 88
3.5 Specification analysis........................... 90
3.5.1 Specification tests and model diagnostics........... 90
3.5.2 Residual diagnostic plots.................... 91
3.5.3 Influential observations..................... 92
3.5.4 Specification tests........................ 93
Test of omitted variables.................... 93
Test of the Box-Cox model.................. 94
Test of the functional form of the conditional mean..... 95
Heteroskedasticity test..................... 96
Omnibus test.......................... 97
3.5.5 Tests have power in more than one direction......... 98
3.6 Prediction................................. 100
3.6.1 In-sample prediction...................... 100
3.6.2 Marginal effects......................... 102
3.6.3 Prediction in logs: The retransformation problem...... 103
3.6.4 Prediction exercise....................... 104
3.7 Sampling weights............................. 105
3.7.1 Weights............................. 106
3.7.2 Weighted mean......................... 106
3.7.3 Weighted regression....................... 107
3.7.4 Weighted prediction and MEs................. 109
3.8 OLS using Mata............................. 109
Contents
3.9 Stata resources.............................. Hl
3.10 Exercises.................................. ÝH
Simulation H3
4.1 Introduction................................ 113
4.2 Pseudorandom-number generators: Introduction ........... 114
4.2.1 Uniform random-number generation ............. 114
4.2.2 Draws from normal....................... 116
4.2.3 Draws from t, chi-squared, F, gamma, and beta....... 117
4.2.4 Draws from binomial, Poisson, and negative binomial .... 118
Independent (but not identically distributed) draws from
binomial........................ 118
Independent (but not identically distributed) draws from
Poisson........................ 119
Histograms and density plots ................. 120
4.3 Distribution of the sample mean .................... 121
4.3.1 Stata program.......................... 122
4.3.2 The simulate command..................... 123
4.3.3 Central limit theorem simulation............... 123
4.3.4 The postfile command..................... 124
4.3.5 Alternative central limit theorem simulation......... 125
4.4 Pseudorandom-number generators: Further details.......... 125
4.4.1 Inverse-probability transformation............... 126
4.4.2 Direct transformation...................... 127
4.4.3 Other methods......................... 127
4.4.4 Draws from truncated normal................. 128
4.4.5 Draws from multivariate normal................ 129
Direct draws from multivariate normal............. 129
Transformation using Cholesky decomposition........ 130
4.4.6 Draws using Markov chain Monte Carlo method....... 130
4.5 Computing integrals........................... 132
4.5.1 Quadrature........................... 133
Contents xi
4.5.2 Monte Carlo integration.................... 133
4.5.3 Monte Carlo integration using different S........... 134
4.6 Simulation for regression: Introduction................. 135
4.6.1 Simulation example: OLS with x2 errors........... 135
4.6.2 Interpreting simulation output................. 138
Unbiasedness of estimator................... 138
Standard errors......................... 138
t statistic ............................ 138
Test size............................. 139
Number of simulations..................... 140
4.6.3 Variations............................ 140
Different sample size and number of simulations....... 140
Test power............................ 140
Different error distributions.................. 141
4.6.4 Estimator inconsistency .................... 141
4.6.5 Simulation with endogenous regressors............ 142
4.7 Stata resources.............................. 144
4.8 Exercises.................................. 144
5 GLS regression 147
5.1 Introduction................................ 147
5.2 GLS and FGLS regression........................ 147
5.2.1 GLS for heteroskedastic errors................. 147
5.2.2 GLS and FGLS......................... 148
5.2.3 Weighted least squares and robust standard errors ..... 149
5.2.4 Leading examples........................ 149
5.3 Modeling heteroskedastic data...................... 150
5.3.1 Simulated dataset........................ 150
5.3.2 OLS estimation......................... 151
5.3.3 Detecting heteroskedasticity.................. 152
5.3.4 FGLS estimation........................ 154
xji Contents
5.3.5 WLS estimation......................... 156
5.4 System of linear regressions....................... 156
5.4.1 SUR model ........................... 156
5.4.2 The sureg command ...................... 157
5.4.3 Application to two categories of expenditures........ 158
5.4.4 Robust standard errors..................... 160
5.4.5 Testing cross-equation constraints............... 161
5.4.6 Imposing cross-equation constraints.............. 162
5.5 Survey data: Weighting, clustering, and stratification......... 163
5.5.1 Survey design.......................... 164
5.5.2 Survey mean estimation.................... 167
5.5.3 Survey linear regression .................... 167
5.6 Stata resources.............................. 169
5.7 Exercises.................................. 169
6 Linear instrumental-variables regression 171
6.1 Introduction................................ 171
6.2 IV estimation............................... 171
6.2.1 Basic IV theory......................... 171
6.2.2 Model setup........................... 173
6.2.3 IV estimators: IV, 2SLS, and GMM ............. 174
6.2.4 Instrument validity and relevance............... 175
6.2.5 Robust standard-error estimates................ 176
6.3 IV example................................ 177
6.3.1 The ivregress command .................... 177
6.3.2 Medical expenditures with one endogenous regressor .... 178
6.3.3 Available instruments...................... 179
6.3.4 IV estimation of an exactly identified model......... 180
6.3.5 IV estimation of an overidentified model........... 181
6.3.6 Testing for regressor endogeneity............... 182
6.3.7 Tests of overidentifying restrictions.............. 185
Contents xiii
6.3.8 IV estimation with a binary endogenous regressor...... 186
6.4 Weak instruments............................. 188
6.4.1 Finite-sample properties of IV estimators........... 188
6.4.2 Weak instruments........................ 189
Diagnostics for weak instruments............... 189
Formal tests for weak instruments............... 190
6.4.3 The estât firststage command................. 191
6.4.4 Just-identified model...................... 191
6.4.5 Overidentified model...................... 193
6.4.6 More than one endogenous regressor............. 195
6.4.7 Sensitivity to choice of instruments.............. 195
6.5 Better inference with weak instruments................. 197
6.5.1 Conditional tests and confidence intervals.......... 197
6.5.2 LIML estimator......................... 199
6.5.3 Jackknife IV estimator..................... 199
6.5.4 Comparison of 2SLS, LIML, JIVE, and GMM........ 200
6.6 3SLS systems estimation......................... 201
6.7 Stata resources.............................. 203
6.8 Exercises.................................. 203
7 Quantile regression 205
7.1 Introduction................................ 205
7.2 QR..................................... 205
7.2.1 Conditional quantiles...................... 206
7.2.2 Computation of QR estimates and standard errors..... 207
7.2.3 The qreg, bsqreg, and sqreg commands............ 207
7.3 QR for medical expenditures data.................... 208
7.3.1 Data summary ......................... 208
7.3.2 QR estimates.......................... 209
7.3.3 Interpretation of conditional quantile coefficients...... 210
7.3.4 Retransformation........................ 211
xjv Contents
7.3.5 Comparison of estimates at different quantiles........ 212
7.3.6 Heteroskedasticity test..................... 213
7.3.7 Hypothesis tests......................... 214
7.3.8 Graphical display of coefficients over quantiles........ 215
7.4 QR for generated heteroskedastic data................. 216
7.4.1 Simulated dataset........................ 216
7.4.2 QR estimates.......................... 219
7.5 QR for count data............................ 220
7.5.1 Quantile count regression ................... 221
7.5.2 The qcount command...................... 222
7.5.3 Summary of doctor visits data................. 222
7.5.4 Results from QCR....................... 224
7.6 Stata resources.............................. 226
7.7 Exercises.................................. 226
8 Linear panel-data models: Basics 229
8.1 Introduction................................ 229
8.2 Panel-data methods overview...................... 229
8.2.1 Some basic considerations................... 230
8.2.2 Some basic panel models.................... 231
Individual-effects model .................... 231
Fixed-effects model....................... 231
Random-effects model..................... 232
Pooled model or population-averaged model......... 232
Two-way-effects model..................... 232
Mixed linear models ...................... 233
8.2.3 Cluster-robust inference.................... 233
8.2.4 The xtreg command ...................... 233
8.2.5 Stata linear panel-data commands............... 234
8.3 Panel-data summary........................... 234
8.3.1 Data description and summary statistics........... 234
Contents xv
8.3.2 Panel-data organization .................... 236
8.3.3 Panel-data description..................... 237
8.3.4 Within and between variation................. 238
8.3.5 Time-series plots for each individual ............. 241
8.3.6 Overall scatterplot....................... 242
8.3.7 Within scatterplot ....................... 243
8.3.8 Pooled OLS regression with cluster-robust standard errors . 244
8.3.9 Time-series autocorrelations for panel data.......... 245
8.3.10 Error correlation in the RE model............... 247
8.4 Pooled or population-averaged estimators............... 248
8.4.1 Pooled OLS estimator..................... 248
8.4.2 Pooled FGLS estimator or population-averaged estimator . 248
8.4.3 The xtreg, pa command.................... 249
8.4.4 Application of the xtreg, pa command............ 250
8.5 Within estimator............................. 251
8.5.1 Within estimator........................ 251
8.5.2 The xtreg, fe command..................... 251
8.5.3 Application of the xtreg, fe command............. 252
8.5.4 Least-squares dummy-variables regression.......... 253
8.6 Between estimator............................ 254
8.6.1 Between estimator ....................... 254
8.6.2 Application of the xtreg, be command............ 255
8.7 RE estimator............................... 255
8.7.1 RE estimator.......................... 255
8.7.2 The xtreg, re command..................... 256
8.7.3 Application of the xtreg, re command............. 256
8.8 Comparison of estimators........................ 257
8.8.1 Estimates of variance components............... 257
8.8.2 Within and between R-squared................ 258
8.8.3 Estimator comparison ..................... 258
xvj Contents
8.8.4 Fixed effects versus random effects.............. 259
8.8.5 Hausman test for fixed effects................. 26°
The hausman command.................... 260
Robust Hausman test...................... 261
8.8.6 Prediction............................ 262
8.9 First-difference estimator ........................263
8.9.1 First-difference estimator....................263
8.9.2 Strict and weak exogeneity...................264
8.10 Long panels................................265
8.10.1 Long-panel dataset....................... 265
8.10.2 Pooled OLS and PFGLS.................... 266
8.10.3 The xtpcse and xtgls commands................ 267
8.10.4 Application of the xtgls, xtpcse, and xtscc commands .... 268
8.10.5 Separate regressions ...................... 270
8.10.6 FE and RE models....................... 271
8.10.7 Unit roots and cointegration.................. 272
8.11 Panel-data management.........................274
8.11.1 Wide-form data......................... 274
8.11.2 Convert wide form to long form................ 274
8.11.3 Convert long form to wide form................ 275
8.11.4 An alternative wide-form data................. 276
8.12 Stata resources.............................. 278
8.13 Exercises.................................. 278
9 Linear panel-data models: Extensions 281
9.1 Introduction................................ 281
9.2 Panel IV estimation ........................... 281
9.2.1 Panel IV............................. 281
9.2.2 The xtivreg command ..................... 282
9.2.3 Application of the xtivreg command ............. 282
9.2.4 Panel IV extensions....................... 284
Contents xvii
9.3 Hausman-Taylor estimator....................... 284
9.3.1 Hausman-Taylor estimator .................. 284
9.3.2 The xthtaylor command.................... 285
9.3.3 Application of the xthtaylor command............ 285
9.4 Arellano-Bond estimator ........................ 287
9.4.1 Dynamic model......................... 287
9.4.2 IV estimation in the FD model................ 288
9.4.3 The xtabond command..................... 289
9.4.4 Arellano-Bond estimator: Pure time series.......... 290
9.4.5 Arellano-Bond estimator: Additional regressors....... 292
9.4.6 Specification tests........................ 294
9.4.7 The xtdpdsys command.................... 295
9.4.8 The xtdpd command...................... 297
9.5 Mixed linear models........................... 298
9.5.1 Mixed linear model....................... 298
9.5.2 The xtmixed command..................... 299
9.5.3 Random-intercept model.................... 300
9.5.4 Cluster-robust standard errors................ 301
9.5.5 Random-slopes model ..................... 302
9.5.6 Random-coefficients model................... 303
9.5.7 Two-way random-effects model................ 304
9.6 Clustered data.............................. 306
9.6.1 Clustered dataset........................ 306
9.6.2 Clustered data using nonpanel commands.......... 306
9.6.3 Clustered data using panel commands............ 307
9.6.4 Hierarchical linear models................... 310
9.7 Stata resources.............................. 311
9.8 Exercises.................................. 311
10 Nonlinear regression methods 313
10.1 Introduction................................ 313
Contents
10.2 Nonlinear example: Doctor visits.................... 314
10.2.1 Data description ........................ 314
10.2.2 Poisson model description................... 315
10.3 Nonlinear regression methods...................... 316
10.3.1 MLE............................... 316
10.3.2 The poisson command..................... 317
10.3.3 Postestimation commands................... 318
10.3.4 NLS ............................... 319
10.3.5 The nl command........................ 319
10.3.6 GLM............................... 321
10.3.7 The glm command....................... 321
10.3.8 Other estimators........................ 322
10.4 Different estimates of the VCE..................... 323
10.4.1 General framework....................... 323
10.4.2 The vce() option........................ 324
10.4.3 Application of the vce() option ................ 324
10.4.4 Default estimate of the VCE.................. 326
10.4.5 Robust estimate of the VCE.................. 326
10.4.6 Cluster-robust estimate of the VCE ............. 327
10.4.7 Heteroskedasticity- and autocorrelation-consistent estimate
of the VCE ........................... 328
10.4.8 Bootstrap standard errors................... 328
10.4.9 Statistical inference....................... 329
10.5 Prediction................................. 329
10.5.1 The predict and predictnl commands............. 329
10.5.2 Application of predict and predictnl.............. 330
10.5.3 Out-of-sample prediction.................... 331
10.5.4 Prediction at a specified value of one of the regressors . . . 332
10.5.5 Prediction at a specified value of all the regressors ..... 332
10.5.6 Prediction of other quantities................. 333
Contents xix
10.6 Marginal effects.............................. 333
10.6.1 Calculus and finite-difference methods............ 334
10.6.2 MEs estimates AME, MEM, and MER............ 334
10.6.3 Elasticities and semielasticities ................ 335
10.6.4 Simple interpretations of coefficients in single-index models 336
10.6.5 The mix command....................... 337
10.6.6 MEM: Marginal effect at mean................ 337
Comparison of calculus and finite-difference methods .... 338
10.6.7 MER: Marginal effect at representative value ........ 338
10.6.8 AME: Average marginal effect................. 339
10.6.9 Elasticities and semielasticities ................ 340
10.6.10 AME computed manually................... 342
10.6.11 Polynomial regressors...................... 343
10.6.12 Interacted regressors...................... 344
10.6.13 Complex interactions and nonlinearities ........... 344
10.7 Model diagnostics............................. 345
10.7.1 Goodness-of-fit measures.................... 345
10.7.2 Information criteria for model comparison.......... 346
10.7.3 Residuals ............................ 347
10.7.4 Model-specification tests.................... 348
10.8 Stata resources.............................. 349
10.9 Exercises.................................. 349
11 Nonlinear optimization methods 351
11.1 Introduction................................ 351
11.2 Newton-Raphson method........................ 351
11.2.1 NR method........................... 351
11.2.2 NR method for Poisson..................... 352
11.2.3 Poisson NR example using Mata ............... 353
Core Mata code for Poisson NR iterations.......... 353
Complete Stata and Mata code for Poisson NR iterations . 353
Contents
11.3 Gradient methods.............................355
11.3.1 Maximization options...................... 355
11.3.2 Gradient methods........................ 356
11.3.3 Messages during iterations................... 357
11.3.4 Stopping criteria........................ 357
11.3.5 Multiple maximums....................... 357
11.3.6 Numerical derivatives...................... 358
11.4 The ml command: If method ...................... 359
11.4.1 The ml command........................ 360
11.4.2 The If method.......................... 360
11.4.3 Poisson example: Single-index model............. 361
11.4.4 Negative binomial example: Two-index model........ 362
11.4.5 NLS example: Nonlikelihood model.............. 363
11.5 Checking the program.......................... 364
11.5.1 Program debugging using ml check and ml trace....... 365
11.5.2 Getting the program to run.................. 366
11.5.3 Checking the data........................ 366
11.5.4 Multicollinearity and near collinearity ............ 367
11.5.5 Multiple optimums....................... 368
11.5.6 Checking parameter estimation................ 369
11.5.7 Checking standard-error estimation.............. 370
11.6 The ml command: dO, dl, and d2 methods .............. 371
11.6.1 Evaluator functions....................... 371
11.6.2 The dO method......................... 373
11.6.3 The dl method......................... 374
11.6.4 The dl method with the robust estimate of the VCE .... 374
11.6.5 The d2 method......................... 375
11.7 The Mata optimizeQ function...................... 376
11.7.1 Type d and v evaluators.................... 376
11.7.2 Optimize functions....................... 377
Contents xxi
11.7.3 Poisson example......................... 377
Evaluator program for Poisson MLE............. 377
The optimizeQ function for Poisson MLE .......... 378
11.8 Generalized method of moments .................... 379
11.8.1 Definition............................ 380
11.8.2 Nonlinear IV example ..................... 380
11.8.3 GMM using the Mata optimize() function.......... 381
11.9 Stata resources.............................. 383
11.10 Exercises.................................. 383
12 Testing methods 385
12.1 Introduction................................ 385
12.2 Critical values and p-values....................... 385
12.2.1 Standard normal compared with Student s t......... 386
12.2.2 Chi-squared compared with F................. 386
12.2.3 Plotting densities........................ 386
12.2.4 Computing p-values and critical values............ 388
12.2.5 Which distributions does Stata use? ............. 389
12.3 Wald tests and confidence intervals................... 389
12.3.1 Wald test of linear hypotheses................. 389
12.3.2 The test command....................... 391
Test single coefficient...................... 392
Test several hypotheses..................... 392
Test of overall significance................... 393
Test calculated from retrieved coefficients and VCE..... 393
12.3.3 One-sided Wald tests...................... 394
12.3.4 Wald test of nonlinear hypotheses (delta method)...... 395
12.3.5 The testnl command...................... 395
12.3.6 Wald confidence intervals.................... 396
12.3.7 The lincom command...................... 396
12.3.8 The nlcom command (delta method)............. 397
xxii Contents
12.3.9 Asymmetric confidence intervals................398
12.4 Likelihood-ratio tests...........................399
12.4.1 Likelihood-ratio tests...................... 399
12.4.2 The Irtest command ...................... 401
12.4.3 Direct computation of LR tests................ 401
12.5 Lagrange multiplier test (or score test)................. 402
12.5.1 LM tests............................. 402
12.5.2 The estât command....................... 403
12.5.3 LM test by auxiliary regression................ 403
12.6 Test size and power............................ 405
12.6.1 Simulation DGP: OLS with chi-squared errors........ 405
12.6.2 Test size............................. 406
12.6.3 Test power............................ 407
12.6.4 Asymptotic test power..................... 410
12.7 Specification tests............................. 411
12.7.1 Moment-based tests....................... 411
12.7.2 Information matrix test .................... 411
12.7.3 Chi-squared goodness-of-fit test................ 412
12.7.4 Overidentifying restrictions test................ 412
12.7.5 Hausman test.......................... 412
12.7.6 Other tests ........................... 413
12.8 Stata resources............................... 413
12.9 Exercises.................................. 413
13 Bootstrap methods 415
13.1 Introduction................................415
13.2 Bootstrap methods............................415
13.2.1 Bootstrap estimate of standard error............. 415
13.2.2 Bootstrap methods....................... 416
13.2.3 Asymptotic refinement.........
...................416
13.2.4 Use the bootstrap with caution................416
Contents xxiii
13.3 Bootstrap pairs using the vce(bootstrap) option............ 417
13.3.1 Bootstrap-pairs method to estimate VCE .......... 417
13.3.2 The vce(bootstrap) option................... 418
13.3.3 Bootstrap standard-errors example.............. 418
13.3.4 How many bootstraps?..................... 419
13.3.5 Clustered bootstraps...................... 420
13.3.6 Bootstrap confidence intervals................. 421
13.3.7 The postestimation estât bootstrap command........ 422
13.3.8 Bootstrap confidence-intervals example............ 423
13.3.9 Bootstrap estimate of bias................... 423
13.4 Bootstrap pairs using the bootstrap command............. 424
13.4.1 The bootstrap command.................... 424
13.4.2 Bootstrap parameter estimate from a Stata estimation
command ............................ 425
13.4.3 Bootstrap standard error from a Stata estimation command 426
13.4.4 Bootstrap standard error from a user-written estimation
command ............................ 426
13.4.5 Bootstrap two-step estimator................. 427
13.4.6 Bootstrap Hausman test.................... 429
13.4.7 Bootstrap standard error of the coefficient of variation . . . 430
13.5 Bootstraps with asymptotic refinement................. 431
13.5.1 Percentile-t method....................... 431
13.5.2 Percentile-t Wald test ..................... 432
13.5.3 Percentile-t Wald confidence interval............. 433
13.6 Bootstrap pairs using bsample and simulate.............. 434
13.6.1 The bsample command..................... 434
13.6.2 The bsample command with simulate............. 434
13.6.3 Bootstrap Monte Carlo exercise................ 436
13.7 Alternative resampling schemes..................... 436
13.7.1 Bootstrap pairs......................... 437
13.7.2 Parametric bootstrap...................... 437
XXIV
Contents
13.7.3 Residual bootstrap....................... 439
13.7.4 Wild bootstrap......................... 440
13.7.5 Subsampling........................... 441
13.8 The jackknife............................... 441
13.8.1 Jackknife method........................ 441
13.8.2 The vce(jackknife) option and the jackknife command . . . 442
13.9 Stata resources.............................. 442
13.10 Exercises.................................. 442
14 Binary outcome models 445
14.1 Introduction................................ 445
14.2 Some parametric models......................... 445
14.2.1 Basic model........................... 445
14.2.2 Logit, probit, linear probability, and clog-log models .... 446
14.3 Estimation................................. 446
14.3.1 Latent-variable interpretation and identification....... 447
14.3.2 ML estimation.......................... 447
14.3.3 The logit and probit commands................ 448
14.3.4 Robust estimate of the VCE.................. 448
14.3.5 OLS estimation of LPM.................... 448
14.4 Example.................................. 449
14.4.1 Data description ........................ 449
14.4.2 Logit regression.......................... 450
14.4.3 Comparison of binary models and parameter estimates . . . 451
14.5 Hypothesis and specification tests.................... 452
14.5.1 Wald tests............................ 453
14.5.2 Likelihood-ratio tests...................... 453
14.5.3 Additional model-specification tests.............. 454
Lagrange multiplier test of generalized logit......... 454
Heteroskedastic probit regression............... 455
14.5.4 Model comparison........................ 456
Contents xxv
14.6 Goodness of fit and prediction...................... 457
14.6.1 Pseudo-R2 measure....................... 457
14.6.2 Comparing predicted probabilities with sample frequencies . 457
14.6.3 Comparing predicted outcomes with actual outcomes .... 459
14.6.4 The predict command for fitted probabilities......... 460
14.6.5 The prvalue command for fitted probabilities ........ 461
14.7 Marginal effects.............................. 462
14.7.1 Marginal effect at a representative value (MER)....... 462
14.7.2 Marginal effect at the mean (MEM) ............. 463
14.7.3 Average marginal effect (AME)................ 464
14.7.4 The prchange command.................... 464
14.8 Endogenous regressors.......................... 465
14.8.1 Example............................. 465
14.8.2 Model assumptions....................... 466
14.8.3 Structural-model approach................... 467
The ivprobit command..................... 467
Maximum likelihood estimates................. 468
Two-step sequential estimates................. 469
14.8.4 IVs approach.......................... 471
14.9 Grouped data............................... 472
14.9.1 Estimation with aggregate data................ 473
14.9.2 Grouped-data application................... 473
14.10 Stata resources.............................. 475
14.11 Exercises.................................. 475
15 Multinomial models 477
15.1 Introduction................................ 477
15.2 Multinomial models overview...................... 477
15.2.1 Probabilities and MEs..................... 477
15.2.2 Maximum likelihood estimation................ 478
15.2.3 Case-specific and alternative-specific regressors....... 479
xxvi Contents
15.2.4 Additive random-utility model................. 479
15.2.5 Stata multinomial model commands ............. 480
15.3 Multinomial example: Choice of fishing mode............. 480
15.3.1 Data description ........................ 480
15.3.2 Case-specific regressors..................... 483
15.3.3 Alternative-specific regressors................. 483
15.4 Multinomial logit model......................... 484
15.4.1 The mlogit command...................... 484
15.4.2 Application of the mlogit command.............. 485
15.4.3 Coefficient interpretation.................... 486
15.4.4 Predicted probabilities..................... 487
15.4.5 MEs............................... 488
15.5 Conditional logit model ......................... 489
15.5.1 Creating long-form data from wide-form data........ 489
15.5.2 The asclogit command..................... 491
15.5.3 The clogit command...................... 491
15.5.4 Application of the asclogit command............. 492
15.5.5 Relationship to multinomial logit model........... 493
15.5.6 Coefficient interpretation.................... 493
15.5.7 Predicted probabilities..................... 494
15.5.8 MEs............................... 494
15.6 Nested logit model............................ 496
15.6.1 Relaxing the independence of irrelevant alternatives as-
sumption ............................. 497
15.6.2 NL model............................ 497
15.6.3 The nlogit command...................... 498
15.6.4 Model estimates......................... 499
15.6.5 Predicted probabilities..................... 501
15.6.6 MEs............................... 501
15.6.7 Comparison of logit models .................. 502
Contents xxvii
15.7 Multinomial probit model........................ 503
15.7.1 MNP............................... 503
15.7.2 The mprobit command..................... 503
15.7.3 Maximum simulated likelihood ................ 504
15.7.4 The asmprobit command.................... 505
15.7.5 Application of the asmprobit command............ 505
15.7.6 Predicted probabilities and MEs................ 507
15.8 Random-parameters logit ........................ 508
15.8.1 Random-parameters logit ................... 508
15.8.2 The mixlogit command..................... 508
15.8.3 Data preparation for mixlogit................. 509
15.8.4 Application of the mixlogit command............. 509
15.9 Ordered outcome models......................... 510
15.9.1 Data summary ......................... 511
15.9.2 Ordered outcomes........................ 512
15.9.3 Application of the ologit command.............. 512
15.9.4 Predicted probabilities..................... 513
15.9.5 MEs............................... 513
15.9.6 Other ordered models...................... 514
15.10 Multivariate outcomes.......................... 514
15.10.1 Bivariate probit......................... 515
15.10.2 Nonlinear SUR......................... 517
15.11 Stata resources.............................. 518
15.12 Exercises.................................. 518
16 Tobit and selection models 521
16.1 Introduction................................ 521
16.2 Tobit model................................ 521
16.2.1 Regression with censored data................. 521
16.2.2 Tobit model setup........................ 522
16.2.3 Unknown censoring point ................... 523
xxviii Contents
16.2.4 Tobit estimation ........................ 523
16.2.5 ML estimation in Stata..................... 524
16.3 Tobit model example........................... 524
16.3.1 Data summary ......................... 524
16.3.2 Tobit analysis.......................... 525
16.3.3 Prediction after tobit...................... 526
16.3.4 Marginal effects......................... 527
Left-truncated, left-censored, and right-truncated examples 527
Left-censored case computed directly............. 528
Marginal impact on probabilities............... 529
16.3.5 The ivtobit command...................... 530
16.3.6 Additional commands for censored regression........ 530
16.4 Tobit for lognormal data......................... 531
16.4.1 Data example.......................... 531
16.4.2 Setting the censoring point for data in logs.......... 532
16.4.3 Results.............................. 533
16.4.4 Two-limit tobit......................... 534
16.4.5 Model diagnostics........................ 534
16.4.6 Tests of normality and homoskedasticity........... 535
Generalized residuals and scores................ 535
Test of normality........................ 536
Test of homoskedasticity.................... 537
16.4.7 Next step?............................ 538
16.5 Two-part model in logs.......................... 538
16.5.1 Model structure......................... 538
16.5.2 Part 1 specification....................... 539
16.5.3 Part 2 of the two-part model.................. 540
16.6 Selection model.............................. 541
16.6.1 Model structure and assumptions............... 541
16.6.2 ML estimation of the sample-selection model......... 543
Contents xxix
16.6.3 Estimation without exclusion restrictions........... 543
16.6.4 Two-step estimation...................... 545
16.6.5 Estimation with exclusion restrictions ............ 546
16.7 Prediction from models with outcome in logs............. 547
16.7.1 Predictions from tobit..................... 548
16.7.2 Predictions from two-part model ............... 548
16.7.3 Predictions from selection model ............... 549
16.8 Stata resources.............................. 550
16.9 Exercises.................................. 550
17 Count-data models 553
17.1 Introduction................................ 553
17.2 Features of count data.......................... 553
17.2.1 Generated Poisson data .................... 554
17.2.2 Overdispersion and negative binomial data.......... 555
17.2.3 Modeling strategies....................... 556
17.2.4 Estimation methods ...................... 557
17.3 Empirical example 1........................... 557
17.3.1 Data summary ......................... 557
17.3.2 Poisson model.......................... 558
Poisson model results...................... 559
Robust estimate of VCE for Poisson MLE.......... 560
Test of overdispersion...................... 561
Coefficient interpretation and marginal effects........ 562
17.3.3 NB2 model ........................... 562
NB2 model results ....................... 563
Fitted probabilities for Poisson and NB2 models....... 565
The countfit command..................... 565
The prvalue command..................... 567
Discussion............................ 567
Generalized NB model..................... 567
xx¡í Contents
17.3.4 Nonlinear least-squares estimation .............. 568
17.3.5 Hurdle model.......................... 569
Variants of the hurdle model.................. 571
Application of the hurdle model................ 571
17.3.6 Finite-mixture models..................... 575
FMM specification....................... 575
Simulated FMM sample with comparisons.......• ¦ • 575
ML estimation of the FMM.................. 577
The fmm command....................... 578
Application: Poisson finite-mixture model.......... 578
Interpretation.......................... 579
Comparing marginal effects.................. 580
Application: NB finite-mixture model............. 582
Model selection......................... 584
Cautionary note......................... 585
17.4 Empirical example 2........................... 585
17.4.1 Zero-inflated data........................ 585
17.4.2 Models for zero-inflated data................. 586
17.4.3 Results for the NB2 model................... 587
The prcounts command .................... 588
17.4.4 Results for ZINB........................ 589
17.4.5 Model comparison........................ 590
The countfit command..................... 590
Model comparison using countfit ............... 590
17.5 Models with endogenous regressors................... 591
17.5.1 Structural-model approach................... 592
Model and assumptions .................... 592
Two-step estimation...................... 593
Application........................... 593
17.5.2 Nonlinear IV method..................... 596
Contents xxxi
17.6 Stata resources.............................. 598
17.7 Exercises.................................. 598
18 Nonlinear panel models 601
18.1 Introduction................................ 601
18.2 Nonlinear panel-data overview...................... 601
18.2.1 Some basic nonlinear panel models.............. 601
FE models............................ 602
RE models............................ 602
Pooled models or population-averaged models........ 602
Comparison of models..................... 603
18.2.2 Dynamic models ........................ 603
18.2.3 Stata nonlinear panel commands ............... 603
18.3 Nonlinear panel-data example...................... 604
18.3.1 Data description and summary statistics........... 604
18.3.2 Panel-data organization .................... 606
18.3.3 Within and between variation................. 606
18.3.4 FE or RE model for these data?................ 607
18.4 Binary outcome models ......................... 607
18.4.1 Panel summary of the dependent variable.......... 607
18.4.2 Pooled logit estimator..................... 608
18.4.3 The xtlogit command...................... 609
18.4.4 The xtgee command ...................... 610
18.4.5 PA logit estimator ....................... 610
18.4.6 RE logit estimator....................... 611
18.4.7 FE logit estimator ....................... 613
18.4.8 Panel logit estimator comparison............... 615
18.4.9 Prediction and marginal effects................ 616
18.4.10 Mixed-effects logit estimator.................. 616
18.5 Tobit model................................ 617
18.5.1 Panel summary of the dependent variable.......... 617
xxxl1 Contents
18.5.2 RE tobit model.........................617
18.5.3 Generalized tobit models.................. g!8
18.5.4 Parametric nonlinear panel models.............. 619
18.6 Count-data models................. g-^g
18.6.1 The xtpoisson command....................619
18.6.2 Panel summary of the dependent variable..........620
18.6.3 Pooled Poisson estimator.......... 620
18.6.4 PA Poisson estimator............ g2i
18.6.5 RE Poisson estimators.......... 622
18.6.6 FE Poisson estimator.......... g24
18.6.7 Panel Poisson estimators comparison............. g26
18.6.8 Negative binomial estimators........ 627
18.7 Stata resources .... „„o
......................... o/o
18.8 Exercises..............
A Programming in Stata CO1
bol
A.I Stata matrix commands ~„i
........................ ool
A. 1.1 Stata matrix overview iO1
..................... bol
A.1.2 Stata matrix input and output ......... g3l
Matrix input by hand.......... 631
Matrix input from Stata estimation results.......... 632
A.1.3 Stata matrix subscripts and combining matrices....... 633
A.1.4 Matrix operators . COa
....................... oo4
A. 1.5 Matrix functions cn,
....................... bo4
A.1.6 Matrix accumulation commands......... 635
A. 1.7 OLS using Stata matrix commands.......... g36
A.2 Programs . .
.............................. 637
A.2.1 Simple programs (no arguments or access to results)_____ 637
A.2.2 Modifying a program .... CQo
................. boo
A.2.3 Programs with positional arguments............. 638
A.2.4 Temporary variables .
...................
Contents xxxiii
A.2.5 Programs with named positional arguments......... 639
A.2.6 Storing and retrieving program results............ 640
A.2.7 Programs with arguments using standard Stata syntax . . . 641
A.2.8 Ado-files............................. 642
A.3 Program debugging............................ 643
A.3.1 Some simple tips........................ 644
A.3.2 Error messages and return code................ 644
A.3.3 Trace............................... 645
B Mata 647
B.I How to run Mata............................. 647
B.I.I Mata commands in Mata.................... 647
B.1.2 Mata commands in Stata.................... 648
B.1.3 Stata commands in Mata.................... 648
B.1.4 Interactive versus batch use.................. 648
B.1.5 Mata help............................ 648
B.2 Mata matrix commands......................... 649
B.2.1 Mata matrix input....................... 649
Matrix input by hand...................... 649
Identity matrices, unit vectors, and matrices of constants . . 650
Matrix input from Stata data................. 651
Matrix input from Stata matrix................ 651
Stata interface functions.................... 652
B.2.2 Mata matrix operators..................... 652
Element-by-element operators................. 652
B.2.3 Mata functions......................... 653
Scalar and matrix functions.................. 653
Matrix inversion......................... 654
B.2.4 Mata cross products...................... 655
B.2.5 Mata matrix subscripts and combining matrices....... 655
xxxiv Contents
B.2.6 Transferring Mata data and matrices to Stata........ 657
Creating Stata matrices from Mata matrices......... 657
Creating Stata data from a Mata vector........... 657
B.3 Programming in Mata.......................... 658
B.3.1 Declarations........................... 658
B.3.2 Mata program.......................... 658
B.3.3 Mata program with results output to Stata ......... 659
B.3.4 Stata program that calls a Mata program.......... 659
B.3.5 Using Mata in ado-files..................... 660
Glossary of abbreviations 661
References 665
Author index 673
Subject index 677
|
| any_adam_object | 1 |
| author | Cameron, Adrian Colin 1956- Trivedi, Pravin K. 1943- |
| author_GND | (DE-588)12870022X (DE-588)118054791 |
| author_facet | Cameron, Adrian Colin 1956- Trivedi, Pravin K. 1943- |
| author_role | aut aut |
| author_sort | Cameron, Adrian Colin 1956- |
| author_variant | a c c ac acc p k t pk pkt |
| building | Verbundindex |
| bvnumber | BV035206543 |
| classification_rvk | QH 320 ST 253 ST 601 |
| classification_tum | DAT 307f WIR 017f |
| ctrlnum | (OCoLC)456523433 (DE-599)BVBBV035206543 |
| discipline | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV035206543 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:28:34Z |
| institution | BVB |
| isbn | 9781597180481 1597180483 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017012872 |
| oclc_num | 456523433 |
| open_access_boolean | |
| owner | DE-20 DE-473 DE-BY-UBG DE-703 DE-N2 DE-19 DE-BY-UBM DE-384 DE-1047 DE-1028 DE-355 DE-BY-UBR DE-M382 DE-91 DE-BY-TUM DE-522 DE-83 DE-11 DE-188 DE-Re13 DE-BY-UBR |
| owner_facet | DE-20 DE-473 DE-BY-UBG DE-703 DE-N2 DE-19 DE-BY-UBM DE-384 DE-1047 DE-1028 DE-355 DE-BY-UBR DE-M382 DE-91 DE-BY-TUM DE-522 DE-83 DE-11 DE-188 DE-Re13 DE-BY-UBR |
| physical | xl, 692 Seiten Illustrationen |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Stata Press |
| record_format | marc |
| spelling | Cameron, Adrian Colin 1956- Verfasser (DE-588)12870022X aut Microeconometrics using Stata A. Colin Cameron ; Pravin K. Trivedi College Station, Texas Stata Press 2009 xl, 692 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Literaturverzeichnis Seite [665] - 672 ; Personen- u. Sachindex Mikroökonomisches Modell (DE-588)4125908-7 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf Mikroökonomie (DE-588)4039225-9 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 s Mikroökonomie (DE-588)4039225-9 s Stata (DE-588)4617285-3 s DE-604 DE-188 Mikroökonomisches Modell (DE-588)4125908-7 s Trivedi, Pravin K. 1943- Verfasser (DE-588)118054791 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017012872&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cameron, Adrian Colin 1956- Trivedi, Pravin K. 1943- Microeconometrics using Stata Mikroökonomisches Modell (DE-588)4125908-7 gnd Stata (DE-588)4617285-3 gnd Mikroökonomie (DE-588)4039225-9 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| subject_GND | (DE-588)4125908-7 (DE-588)4617285-3 (DE-588)4039225-9 (DE-588)4043212-9 |
| title | Microeconometrics using Stata |
| title_auth | Microeconometrics using Stata |
| title_exact_search | Microeconometrics using Stata |
| title_full | Microeconometrics using Stata A. Colin Cameron ; Pravin K. Trivedi |
| title_fullStr | Microeconometrics using Stata A. Colin Cameron ; Pravin K. Trivedi |
| title_full_unstemmed | Microeconometrics using Stata A. Colin Cameron ; Pravin K. Trivedi |
| title_short | Microeconometrics using Stata |
| title_sort | microeconometrics using stata |
| topic | Mikroökonomisches Modell (DE-588)4125908-7 gnd Stata (DE-588)4617285-3 gnd Mikroökonomie (DE-588)4039225-9 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| topic_facet | Mikroökonomisches Modell Stata Mikroökonomie Ökonometrisches Modell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017012872&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cameronadriancolin microeconometricsusingstata AT trivedipravink microeconometricsusingstata |