Verfügbarkeit: Microeconometrics using Stata

Microeconometrics using Stata:

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Cameron, Adrian Colin 1956- (VerfasserIn), Trivedi, Pravin K. 1943- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	College Station, Tex. Stata Press 2010
Ausgabe:	Rev. ed.
Schriftenreihe:	A Stata Press publication
Schlagworte:	Mikroökonomie Ökonometrisches Modell Stata Mikroökonomisches Modell
Online-Zugang:	Inhaltsverzeichnis Klappentext
Beschreibung:	XLII, 706 S. graph. Darst.
ISBN:	9781597180733

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Datensatz im Suchindex

_version_	1804141159682932736
adam_text	Contents List of tables xxxv List of figures xxxvii Preface to the Revised Edition xxxix Preface to the First Edition xli Stata basics 1 1.1 Interactive use .............................. і 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 Factor variables ......................... 9 1.3.5 Abbreviations, case sensitivity, and wildcards ........ 11 1.3.6 Arithmetic, relational, and logical operators ......... 12 1.3. í Error messages ......................... 12 1.4 Do-files and log files ........................... 13 1.4.1 Writing a do-file ...................,..... 13 1.4.2 Running do-files ......................... 14 1.4.3 Log files ............................. 14 1.4.4 A three-step process ...................... 15 1.4.5 Comments and long lines .................... 16 1.4.6 Different implementations of Stata .............. 17 1.5 Scalars and matrices ........................... 17 1.5.1 Scalars .............................. 17 1.5.2 Matrices ............................. 18 1.6 Using results from Stata commands ................... 18 1.6.1 Using results from the r-class command summarize ..... 18 1.6.2 Using results from the е -class command regress ....... 19 1.7 Global and local macros ......................... 21 1.7.1 Global macros .......................... 21 1.7.2 Local macros .......................... 22 1.7.3 Scalar or macro? ........................ 23 1.8 Looping commands ............................ 24 1.8.1 The foreach loop ........................ 25 1.8.2 The forvalues loop ....................... 26 1.8.3 The while loop ......................... 26 1.8.4 The continue command ..................... 27 1.9 Some useful commands .......................... 27 1.10 Template do-file .............................. 27 1.11 User-written commands ......................... 28 1.12 Stata resources .............................. 29 1.13 Exercises .................................. 29 Data management and graphics 31 2.1 Introduction ................................ 31 2.2 Types of data ............................... 31 2.2.1 Text or ASCII data ....................... 32 2.2.2 Internal numeric data ...................... 32 2.2.3 String data ........................... 33 2.2.4 Formats for displaying numeric data ............. 33 2.3 Inputting data .............................. 34 2.3.1 General principles ........................ 34 2.3.2 Inputting data already in Stata format ............ 35 2.3.3 Inputting data from the keyboard ............... 36 2.3.4 Inputting nontext data ..................... 36 2.3.5 Inputting text data from a spreadsheet ............ 37 2.3.6 Inputting text data in free format ............. . . 32 2.3.7 Inputting text data in fixed format . . . ,........ . . 38 2.3.8 Dictionary files ......................... 39 2.3.9 Common pitfalls ........................ 39 2.4 Data management ............................ 40 2.4.1 PSID example .......................... 40 2.4.2 Naming and labeling variables ................. 43 2.4.3 Viewing data .......................... 44 2.4.4 Using original documentation ................. 45 2.4.5 Missing values .......................... 45 2.4.6 Imputing missing data ..................... 47 2.4.7 Transforming data (generate, replace, egen. recode)..... 48 The generate and replace commands ............. 48 The egen command ....................... 49 The recode command ...................... 49 The by prefix .......................... 49 Indicator variables ....................... 50 Set of indicator variables .,...,.............. óO Interactions ...........,............... ül Demeaning ............................ 62 2.4.8 Saving data ........................... 52 2.4.9 Selecting the sample ...................... 63 2.5 Manipulating datasets .......................... 54 2.5.1 Ordering observations and variables .............. 55 2.5.2 Preserving and restoring a dataset .............. 55 2.5.3 Wide and long forms for a dataset .............. 55 2.5.4 Merging datasets ........................ 56 2.5.5 Appending datasets ....................... 58 2.6 Graphical display of data ........................ 58 2.6.1 Stata graph commands ..................... 59 Example graph commands ................... 59 Saving and exporting graphs .................. 60 Learning how to use graph commands ............ 61 2.6.2 Box-and-whisker plot ...................... 61 2.6.3 Histogram ............................ 63 2.6.4 Kernel density plot ....................... 63 2.6.5 Twoway scatterplots and fitted lines ............. 66 2.6.6 Lowess, kernel, local linear, and nearest-neighbor regression 67 2.6.7 Multiple scatterplots ...................... 69 2.7 Stata resources .............................. 70 2.8 Exercises .................................. 70 Linear regression basics 73 3.1 Introduction ................................ 73 3.2 Data and data summary ......................... 73 3.2.1 Data description ........................ 73 3.2.2 Variable description ....................... 74 3.2.3 Summary statistics ....................... 76 3.2.4 More-detailed summary statistics ............... 76 3.2.5 Tables for data ......................... 77 3.2.6 Statistical tests ......................... 80 3.2.7 Data plots ............................ 80 3.3 Regression in levels and logs ....................... 81 3.3.1 Basic regression theory ..................... 81 3.3.2 OLS regression and matrix algebra .............. 82 3.3.3 Properties of the OLS estimator ................ 83 3.3.4 Heteľoskedasticity-ľobust standard errors .......... 84 3.3.5 Cluster-robust standard errors ................ 84 3.3.6 Regression in logs ........................ 8o 3.4 Basic regression analysis ..................... . . , . 86 3.4.1 Correlations ........................... 86 3.4.2 The regress command ..................... 87 3.4.3 Hypothesis tests ......................... 88 3.4.4 Tables of output from several regressions ........... 89 3.4.5 Even better tables of regression output ............ 90 3.4.6 Factor variables for categorical variables and interactions . . 92 3.5 Specification analysis ........................... 94 3.5.1 Specification tests and model diagnostics ........... 94 3.5.2 Residual diagnostic plots .................... 95 3.5.3 Influential observations ..................... 96 3.5.4 Specification tests ........................ 97 Test of omitted variables .................... 98 Test of the Box -Сох model .................. 98 Test of the functional form of the conditional mean ..... 99 Heteroskedasticity test ..................... 100 Omnibus test .......................... 10 ? 3.5.5 Tests have power in more than one direction ......... 102 3.6 Prediction ................................. 104 3.6.1 In-sample prediction ....................... 104 3.6.2 MEs and elasticities ....................... 106 3.6.3 Prediction in logs: The retransformation problem ...... 108 3.6.4 Prediction exercise ....................... 109 3.7 Sampling weights ............................. Ill 3.7.1 Weights ............................. Ill 3.7.2 Weighted mean ......................... 112 3.7.3 Weighted regression ....................... 113 3.7.4 Weighted prediction and MEs ................. 114 3.8 OLS using Mata ............................. 115 3.9 Stata resources .............................. 117 3.10 Exercises .................................. 117 Simulation 119 4.1 Introduction ................................ 119 4.2 Pseudorandom-number generators: Introduction ........... 120 4.2.1 Uniform random-number generation ............. 120 4.2.2 Draws from normal ....................... 122 4.2.3 Draws from t, chi-squared, F. gamma, and beta ....... 123 4.2.4 Draws from binomial. Poisson, and negative binomial .... 124 Independent (but not identically distributed) draws from binomial ........................ 124 Independent (but not identically distributed) draws from Poisson ........................ 125 Histograms and density plots ................. 126 4.3 Distribution of the sample mean .................... 127, 4.3.1 Stata program .......................... 128 4.3.2 The simulate command ..................... 129 4.3.3 Central limit theorem simulation ............... 129 4.3.4 The postfile command ..................... 130 4.3.5 Alternative central limit theorem simulation ......... 131 4.4 Pseudorandom-number generators: Further details ......... . 131 4.4.1 Inverse-probability transformation ............... 132 4.4.2 Direct transformation ...................... 133 4.4.3 Other methods ......................... 133 4.4.4 Draws from truncated normal ................. 134 4.4.5 Draws from multivariate normal ................ 135 Direct draws from multivariate normal ............ 135 Transformation using Cholesky decomposition ........ 136 4.4.6 Draws using Markov chain Monte Carlo method ....... 136 4.5 Computing integrals ........................... 138 4.5.1 Quadrature ........................... 139 4.5.2 Monte Carlo integration .................... 139 4.5.3 Monte Carlo integration using different S ........... 140 4.6 Simulation for regression: Introduction ................. 141 4.6.1 Simulation example: OLS with χ2 errors ........... 141 4.6.2 Interpreting simulation output ................. 144 ünbiasedness of estimator .................. . 144 Standard errors ......................... 144 t statistic ............................ 144 Test size ............................. 145 Number of simulations ..................... 146 4.6.3 Variations ............................ 146 Different sample size and number of simulations ....... 146 Test power ............................ 146 Different error distributions .................. 147 4.6.4 Estimator inconsistency .................... 147 4.6.5 Simulation with endogenous regressors ............ 148 4.7 Stata resources .............................. 150 4.8 Exercises .................................. 150 GLS regression 158 5.1 Introduction ............................ . . . . 153 5.2 GLS and FGLS regression ..................,.,,,. 153 5.2.1 GLS for heterockedastic errors ................ , 153 5.2.2 GLS and FGLS ......................... io4 5.2.3 Weighted least squares and robuüt .■ standard errore ..... loo 5.2.4 Leading examples ........................ 15ο 5.3 ¿Modeling heteroskedastic data ...................... 156 5.3.1 Simulated dataset ........................ 156 5.3.2 OLS estimation ......................... 157 5.3.3 Detecting heteroskedasticity .................. 158 5.3.4 FGLS estimation ........................ 160 5.3.5 WLS estimation ......................... 162 5.4 System of linear regressions ....................... 162 5.4.1 SUR model ........................... 162 5.4.2 The sureg command ...................... 163 5.4.3 Application to two categories of expenditures ........ 164 5.4.4 Robust standard errors ..................... 166 5.4.5 Testing cross-equation constraints ............... 167 5.4.6 Imposing cross-equation constraints .............. 168 5.5 Survey data: Weighting, clustering, and stratification ......... 169 5.5.1 Survey design .......................... 170 5.5.2 Survey mean estimation .................... 173 5.5.3 Survey linear regression .................... 173 5.6 Stata resources .............................. 175 5.7 Exercises .................................. 175 Linear instrumental-variables regression 177 6.1 Introduction ................................ 177 6.2 IV estimation ............................... 177 6.2.1 Basic IV theory ......................... 177 6.2.2 Model setup ........................... 179 6.2.3 IV estimators: IV. 2SLS. and GUM ............. 180 6.2.4 Instrument validity and relevance ............... 181 6.2.5 Robust standard-error estimates ................ 182 6.3 IV example ................................ 183 6.3.1 The ivregress command .................... 183 6.3.2 Medical expenditures with one endogenous regressor .... 184 6.3.3 Available instruments ...................... 185 6.3.4 IV estimation of an exactly identified model ......... 186 6.3.5 IV estimation of an overidentified model........... 187 6.3.6 Testing for regressoľ endogeiieity ............... 188 6.3.7 Tests of overidentifying restrictions .............. 191 6.3.8 IV estimation with a binary endogenous regressor ..,,., 19? 6.4 Weak instruments ............................ . 194 6.4.1 Finite-sample properties of IV estimators ........... 194 6.4.2 Weak instruments ........................ 196 Diagnostics for weak instruments ............... 195 Formal tests for weak instruments ............... 196 6.4.3 The estât firststage command ................. 197 6.4.4 Just-identified model ...................... 197 6.4.5 Overidentified model ...................... 199 6.4.6 More than one endogenous regressor ............. 200 6.4.7 Sensitivity to choice of instruments .............. 200 6.5 Better inference with weak instruments ................. 202 6.5.1 Conditional tests and confidence intervals .......... 202 6.5.2 LIML estimator ......................... 204 6.5.3 Jackknife IV estimator ..................... 204 6.5.4 Comparison of 2SLS. LIML. JIVE, and GMM ........ 205 6.6 3SLS systems estimation ......................... 206 6.7 Stata resources .............................. 208 6.8 Exercises .................................. 208 Quantile regression VLl 7.1 Introduction ................................ 211 7.2 QR .........................., ,......... 2.1.1 7 .2.1 Conditional quantiles ...................... 212 7.2.2 Computation of QR estimates and standard errors ..... 213 7.2.3 The qreg. bsqreg. and sqreg commands ............ 213 7.3 QR for medical expenditures data .................... 214 7.3.1 Data summan ......................... 214 7.3.2 QR estimates .......................... 215 7.3.3 Interpretation of conditional quantile coefficients ...... 216 7.3.4 Retransformation ........................ 217 7.3.5 Comparison of estimates at different quantiles ........ 218 7.3.6 Heteroskedasticity test ..................... 219 7.3.7 Hypothesis tests ......................... 220 7.3.8 Graphical display of coefficients over quantiles ........ 221 7.4 QR for generated heteroskedastic data ................. 222 7.4.1 Simulated dataset ........................ 222 7.4.2 QR estimates .......................... 225 7.5 QR for count data ............................ 226 7.5.1 Quantile count regression ................... 227 7.5.2 The qcount command ...................... 228 7.5.3 Summary of doctor visits data ................. 228 7.5.4 Results from QCR ....................... 230 7.6 Stata resources .............................. 232 7.7 Exercises .................................. 232 Linear panel-data models: Basics 235 8.1 Introduction ................................ 235 8.2 Panel-data methods overview ...................... 235 8.2.1 Some basic considerations ................... 236 8.2.2 Some basic panel models .................... 237 Individual-effects model .................... 237 Fixed-effects model ....................... 237 Random-effects model ..................... 238 Pooled model or population-averaged model ......... 238 Two-way-effects model ..................... 238 Mixed linear models ...................... 239 8.2.3 Cluster-robust inference .................... 239 8.2.4 The xtreg command ...................... 239 8.2.5 Stata linear panel-data commands ............... 240 8.3 Panel-data summary ........................... 240 8.3.1 Data description and summary statistics ........... 240 8.3.2 Panel-data organization .................... 242 8.3.3 Panel-data description ..................... 243 8.3.4 Within and between variation ................. 244 8.3.5 Time-series plots for each individual ............. 247 8.3.6 Overall scatterplot ....................... 248 8.3.7 Within scatterplot ....................... 249 8.3.8 Pooled OLS regression with cluster-robust standard errors . 250 8.3.9 Time-series autocorrelations for panel data .......... 251 8.3.10 Error correlation in the RE model ............... 253 8.4 Pooled or population-averaged estimators ............... 254 8.4.1 Pooled OLS estimator ..................... 254 8.4.2 Pooled FGLS estimator or population-averaged estimator . 254 8.4.3 The xtreg. pa command .................... 255 8.4.4 Application of the xtreg, pa command ............ 256 8.5 Within estimator ............................. 257 8.5.1 Within estimator ........................ 257 8.5.2 The xtreg. fe command ..................... 257 8.5.3 Application of the xtreg. fe command ............. 2Ó8 8.5.4 Least-squares dummy-variables regression .......... 259 8.6 Between estimator ............................ 260 8.6.1 Between estimator ....................... 260 8.6.2 Application of the xtreg. be command ............ 261 8.7 RE estimator ............................... 261 8.7.1 RE estimator .......................... 262 8.7.2 The xtreg, re command ..................... 262 8.7.3 Application of the xtreg. re command ............. 263 8.8 Comparison of estimators ........................ 264 8.8.1 Estimates of variance components ............... 264 8.8.2 Within and between R-squared ................ 264 8.8.3 Estimator comparison ..................... 265 8.8.4 Fixed effects versus random effects .............. 266 8.8.5 Hausman test for fixed effects ................. 266 The hausman command .................... 267 Robust Hausman test ...................... 267 8.8.6 Prediction ............................ 268 8.9 First-difference estimator ........................ 269 8.9.1 First-difference estimator .................... 270 8.9.2 Strict and weak exogeneity ................... 271 8.10 Long panels ................................ 271 8.10.1 Long-panel dataset ....................... 271 8.10.2 Pooled OLS and PFGLS .................... 273 8.10.3 The xtpcse and xtgls commands ................ 273 8.10.4 Application of the xtgls, xtpcse, and xtscc commands .... 274 8.10.5 Separate regressions ...................... 276 8.10.6 FE and RE models ....................... 277 8.10.7 Unit roots and cointegration .................. 278 8.11 Panel-data management ......................... 280 8.11.1 Wide-form data ......................... 280 8.11.2 Convert wide form to long form ................ 280 8.11.3 Convert long form to wide form ................ 281 8.11.4 An alternative wide-form data ................. 282 8.12 Stata resources .............................. 284 8.13 Exercises .................................. 284 Linear panel-data models: Extensions 287 9.1 Introduction ................................ 287 9.2 Panel IV estimation ........................... 287 9.2.1 Panel IV ............................. 287 9.2.2 The xtivreg command ..................... 288 9.2.3 Application of the xtivreg command ............. 288 9.2.4 Panel IV extensions ....................... 290 9.3 Hausman-Taylor estimator ....................... 290 9.3.1 Hausman-Taylor estimator .................. 290 9.3.2 The xthtaylor command .................... 291 9.3.3 Application of the xthtaylor command ............ 291 9.4 Arellano-Bond estimator ........................ 293 9.4.1 Dynamic model ......................... 293 9.4.2 IV estimation in the FD model ................ 294 9.4.3 The xtabond command ..................... 295 9.4.4 Arellano-Bond estimator: Pure time series .......... 296 9.4.5 Arellano-Bond estimator: Additional regressors ....... 298 9.4.6 Specification tests ........................ 300 9.4.7 The xtdpdsys command .................... 301 9.4.8 The xtdpd command ...................... 303 9.5 Mixed linear models ........................... 305 9.5.1 Mixed linear model ....................... 305 9.5.2 The xtmixed command ..................... 306 9.5.3 Random-intercept model .................... 306 9.5.4 Cluster-robust standard errors ................ 30? 9.5.5 Random-slopes model ..................... 308 9.0.6 Random-coefficients model ................... 310 9.5.7 Two-way random-effects model ................ 311 9.6 Clustered data .............................. 312 9.6.1 Clustered dataset ........................ 312 9.6.2 Clustered data using noupanel commands .......... 313 9.6.3 Clustered data using panel commands ............ 314 9.6.4 Hierarchical linear models ................... 316 9.7 Stata resources .............................. 317 9.8 Exercises .................................. 318 10 Nonlinear regression methods 319 10.1 Introduction ................................ 319 10.2 Nonlinear example: Doctor visits .................... 320 10.2.1 Data description ........................ 320 10.2.2 Poisson model description ................... 321 10.3 Nonlinear regression methods ...................... 322 10.3.1 MLE ............................... 322 10.3.2 The poisson command ..................... 323 10.3.3 Postestimation commands ................... 324 10.3.4 NLS ............................... 325 10.3.5 The nl command ........................ 325 10.3.6 GLM ............................... 327 10.3.7 The glm command ....................... 327 10.3.8 The gmm command ....................... 328 10.3.9 Other estimators ........................ 330 10.4 Different estimates of the VCE ..................... 330 10.4.1 General framework ....................... 330 10.4.2 The vce() option ........................ 331 10.4.3 Application of the vce() option ................ 332 10.4.4 Default estimate of the VCE .................. 333 10.4.5 Robust estimate of the VCE .................. 334 10.4.6 Cluster-robust estimate of the VCE ............. 335 10.4.7 Heteroskedasticity- and autocorrelation-consistent estimate of the VCE ........................... 335 10.4.8 Bootstrap standard errors ................... 336 10.4.9 Statistical inference ....................... 336 10.5 Prediction ................................. 336 10.5.1 The predict and predictnl commands ............. 337 10.5.2 Application of predict and predictnl .............. 337 10.5.3 Out-of-sample prediction .................... 338 10.5.4 Prediction at a specified value of one of the regressors . . . 339 10.5.5 Prediction at a specified value of all the regressors ..... 340 10.5.6 Prediction of other quantities ................ . 341 10.5.7 The margins command for prediction ........., , , . 341 10.6 Marginal effects .............................. 343 10.6.1 Calculus and finite-difference methods ............ 343 10.6.2 MEs estimates AME, MEM. and MER ............. 344 10.6.3 Elasticities and semielasticities ................ 344 10.6.4 Simple interpretations of coefficients in single-index models 345 10.6.5 The margins command for marginal effects .......... 346 10.6.6 MEM: Marginal effect at mean ................ 347 Comparison of calculus and finite-difference methods .... 348 10.6.7 MER: Marginal effect at representative value ........ 348 10.6.8 AME: Average marginal effect ................. 349 10.6.9 Elasticities and semielasticities ................ 351 10.6.10 AME computed manually ................... 352 10.6.11 Polynomial regressors ...................... 354 10.6.12 Interacted regressors ...................... 355 10.6.13 Complex interactions and nonlinearities ........... 356 10.7 Model diagnostics ............................. 307 10.7.1 Goodness-of-fit measures ...............,,.,, 35 < 10.7.?, Information criteria for model comparison .......... 359 10.7.3 Residuals .......,......... . , ,........ 3o9 10.7.4 Model-specification tests ...,....,..,........ 361 10.8 Stata resources ................,,,..,,...,,,. 361 10.9 Exercises ............................,..... 361 11 Nonlinear optimization methods 368 11.1 Introduction ................................ 363 11.2 Newton— Raphson method ........................ 363 11.2.1 NR method ........................... 363 11.2.2 NR method for Poisson..................... 364 11.2.3 Poisson NR example using Mata ............... 365 Core Mata code for Poisson NR iterations .......... 365 Complete Stata and Mata code for Poisson NR iterations . 365 11.3 Gradient methods ............................. 367 11.3.1 Maximization options ...................... 367 11.3.2 Gradient methods ........................ 368 11.3.3 Messages during iterations ................... 369 11.3.4 Stopping criteria ........................ 369 11.3.5 Multiple maximums ....................... 369 11.3.6 Numerical derivatives ...................... 370 11.4 The ml command: If method ...................... 371 11.4.1 The ml command ........................ 372 11.4.2 The If method .......................... 372 11.4.3 Poisson example: Single-index model ............. 373 11.4.4 Negative binomial example: Two-index model ........ 375 11.4.5 NLS example: Nonlikelihood model .............. 376 11.5 Checking the program .......................... 376 11.5.1 Program debugging using ml check and ml trace ....... 377 11.5.2 Getting the program to run .................. 378 11.5.3 Checking the data ........................ 379 11.5.4 Multicollinearity and near collinearity ............ 379 11.5.5 Multiple optimums ....................... 380 11.5.6 Checking parameter estimation ................ 381 11.5.7 Checking standard-error estimation .............. 382 11.6 The ml command: dO. dl. d2, lfO, lfl. and 1£2 methods ........ 383 11.6.1 Evaluator functions ....................... 383 11.6.2 The dO method ......................... 385 11.6.3 The dl method ......................... 386 11.6.4 The Ifi method with the robust estimate of the Л<ГСЕ .... 387 11.6.5 The d2 and If2 methods .................... 388 11.7 The Mata optimizeQ function ...................... 389 11.7.1 Type d and gf evaluators .................... 389 11.7.2 Optimize functions ....................... 390 11.7.3 Poisson example ......................... 390 Evaluator program for Poisson MLE ............. 390 The optimizeQ function for Poisson MLE .......... 391 11.8 Generalized method of moments .................... 392 11.8.1 Definition ............................ 393 11.8.2 Nonlinear IV example ..................... 393 11.8.3 GMM using the Mata optimize() function .......... 394 11.9 Stata resources .............................. 396 11.10 Exercises .................................. 396 12 Testing methods 399 12.1 Introduction ................................ 399 12.2 Critical values and p-values ....................... 399 12.2.1 Standard normal compared with Student s t ......... 400 12.2.2 Chi-squared. compared with F ................ , 400 12.2.3 Plotting densities ........................ 400 12.2.4 Computing p-values and critical values; ............ 40Я 12.2.5 Which distributions does Stata use? ............. 403 12.3 Wald tests and confidence intervals ................... 403 12.3.1 Wald test of linear hypotheses ......, , , . ,...... 403 12.3.2 The test command ....................... 405 Test single coefficient ...................... 406 Test several hypotheses ..................... 406 Test of overall significance ................... 407 Test calculated from retrieved coefficients and VCE ..... 407 12.3.3 One-sided Wald tests ...................... 408 12.3.4 Wald test of nonlinear hypotheses (delta method) ...... 409 12.3.5 The testnl command ...................... 409 12.3.6 Wald confidence intervals .................... 410 12.3.7 The lincom command ...................... 410 12.3.8 The nlcom command (delta method) ............. 411 12.3.9 Asymmetric confidence intervals ................ 412 12.4 Likelihood-ratio tests ........................... 413 12.4.1 Likelihood-ratio tests ...................... 413 12.4.2 The litest command ...................... 415 12.4.3 Direct computation of LR tests ................ 415 12.5 Lagrange multiplier test (or score test) ................. 416 12.5.1 LM tests ............................. 416 12.5.2 The estât command ....................... 417 12.5.3 LM test by auxiliary regression ................ 417 12.6 Test size and power ............................ 419 12.6.1 Simulation DGP: OLS with chi-squared errors ........ 419 12.6.2 Test size ............................. 420 12.6.3 Test power ............................ 421 12.6.4 Asymptotic test power ..................... 424 12.7 Specification tests ............................. 425 12.7.1 Moment-based tests ....................... 425 12.7.2 Information matrix test .................... 42-5 12.7.3 Chi-squared goodness-of-fit test ................ 426 12.7 A Overidentifying restrictions test ................ 426 12.7.5 Hausman test .......................... 426 12.7.6 Other tests ........................... 427 12.8 Stata resources .............................. 427 12.9 Exercises .................................. 427 13 Bootstrap methods 429 13.1 Introduction ................................ 429 13.2 Bootstrap methods ....................,.,,.,,. 429 13.2.1 Bootstrap estimate of standard error ............. 429 13.2.2 Bootstrap methods ....................... 430 13.2.3 Asymptotic refinement ..................... 430 13.2.4 Use the bootstrap with caution ................ 430 13.3 Bootstrap pairs using the vce(bootstrap) option ............ 431 13.3.1 Bootstrap-pairs method to estimate VCE .......... 431 13.3.2 The ^(bootstrap) option ................... 432 13.3.3 Bootstrap standard-errors example .............. 432 13.3.4 How many bootstraps? ..................... 433 13.3.5 Clustered bootstraps ...................... 434 13.3.6 Bootstrap confidence intervals ................. 435 13.3.7 The postestimation estât bootstrap command ........ 436 13.3.8 Bootstrap confidence-intervals example ............ 437 13.3.9 Bootstrap estimate of bias ................... 437 13.4 Bootstrap pairs using the bootstrap command ............. 438 13.4.1 The bootstrap command .................... 438 13.4.2 Bootstrap parameter estimate from a Stata estimation command ............................ 439 13.4.3 Bootstrap standard error from a Stata estimation command 440 13.4.4 Bootstrap standard error from a user-written estimation command ........................... 440 03.4. о Bootstrap two-step estimator ................. 441 13.4.6 Bootstrap Hausman test .................... 44.3 13.4. У Bootstrap standard error of the coefficient of variation . , , 444 13.5 Bootstraps with asymptotic refinement ................. 445 13.5.1 Percontile-t method ....................... 445 13.5.2 Percentile-t Wald test ..................... 446 13.5.3 Percentile-t Wald confidence interval ............. 447 13.6 Bootstrap pairs using bsample and simulate .............. 448 13.6.1 The bsample command ..................... 448 13.6.2 The bsample command with simulate ............. 448 13.6.3 Bootstrap Monte Carlo exercise ................ 450 13.7 Alternative resampling schemes ..................... 450 13.7.1 Bootstrap pairs ......................... 451 13.7.2 Parametric bootstrap ...................... 451 13.7.3 Residual bootstrap ....................... 453 13.7.4 Wild bootstrap ......................... 454 13.7.5 Subsampling ........................... 455 13.8 Thejackknife ............................... 455 13.8.1 Jackknife method ........................ 455 13.8.2 The vce(jackknife) option and the jackknife command . . . 456 13.9 Stata resources .............................. 456 13.10 Exercises .................................. 456 14 Binary outcome models 459 14.1 Introduction ................................ 459 14.2 Some parametric models ......................... 459 14.2.1 Basic model ........................... 459 14.2.2 Logit. probit, linear probability, and clog-log models .... 460 14.3 Estimation ................................. 460 14.3.1 Latent-variable interpretation and identification ....... 461 14.3.2 ML estimation .......................... 461 14.3.3 The logit and probit commands ................ 462 14.3.4 Robust estimate of the VCE .................. 462 14.3.5 OLS estimation of LPM .................... 462 14.4 Example .................................. 463 14.4.1 Data description ........................ 463 14.4.2 Logit regression ......................... 464 14.4.3 Comparison of binary models and parameter estimates . . . 465 14.5 Hypothesis and specification tests .................... 466 14.5.1 Wald tests ............................ 467 14.5.2 Likelihood-ratio tests ...................... 46? 14.5.3 Additional model-specification tests .............. 468 Lagrange multiplier test of generalized logic ......... 468 Heteroskedastic probit regression ............... 469 14.5.4 Model comparison ........................ 470 14.6 Goodness of fit and prediction ...................... 471 14.6.1 Pseudo- R2 measure ....................... 471 14.6.2 Comparing predicted probabilities with sample frequencies . 471 14.6.3 Comparing predicted outcomes with actual outcomes .... 473 14.6.4 The predict command for fitted probabilities ......... 474 14.6.5 The prvalue command for fitted probabilities ........ 475 14.7 Marginal effects .............................. 476 14.7.1 Marginal effect at a representative value (MER) ....... 476 14.7.2 Marginal effect at the mean (MEM) ............. 477 14.7.3 Average marginal effect (AME) ................ 478 14.7.4 The prchange command .................... 479 14.8 Endogenous regressors .......................... 479 14.8.1 Example ............................. 480 14.8.9. Л -Iodei assumptions ....................... 481 14.8.3 Structural-model approach ................... 481 The ivprobit command ..................... 482 Maximum likelihood estimates ................. 482 Two-step sequential estimates ................. 483 14.8.4 IVs approach .......................... 48o 14.9 Grouped data ................................ 486 14.9.1 Estimation with aggregate data ................ 487 14.9.2 Grouped-data application ................... 487 14.10 Stata resources .............................. 489 14.11 Exercises .................................. 489 15 Multinomial models 491 15.1 Introduction ................................ 491 15.2 Multinomial models overview ...................... 491 15.2.1 Probabilities and MEs ..................... 491 15.2.2 Maximum likelihood estimation ................ 492 15.2.3 Case-specific and alternative-specific regressors ....... 493 15.2.4 Additive random-utility model ................. 493 15.2.5 Stata multinomial model commands ............. 494 15.3 Multinomial example: Choice of fishing mode ............. 494 15.3.1 Data description ........................ 494 15.3.2 Case-specific regressors ..................... 497 15.3.3 Alternative-specific regressors ................. 497 15.4 Multinomial logit model ......................... 498 15.4.1 The mlogit command ...................... 498 15.4.2 Application of the mlogit command .............. 499 15.4.3 Coefficient interpretation .................... 500 15.4.4 Predicted probabilities ..................... 501 15.4.5 MEs ............................... 502 15.5 Conditional logit model ......................... 503 15.5.1 Creating long-form data from wide-form data ........ 503 15.5.2 The asclogit command ..................... 505 15.5.3 The dogit command ...................... 506 15.5.4 Application of the asclogit command ............. 506 15.5.5 Relationship to multinomial logit model ........... 507 15.5.6 Coefficient interpretation .................... 507 15.5.7 Predicted probabilities ..................... 508 15.5.8 MEs ............................... 509 15.6 Nested logit model ............................ 511 15.6.1 Relaxing the independence of irrelevant alternatives as¬ sumption ............................. 511 15.6.2 NL model ............................ oil 15.6.3 The nlogit command ....................... olí? 15.6.4 Model estimates ......................... 513 15.6.5 Predicted probabilities ..................... 516 15.6.6 MEs ............................... 516 15.6.7 Comparison of logit models .................. 517 15.7 Multinomial probit model ........................ 517 15.7.1 MNP ............................... 517 15.7.2 The mprobit command ..................... 518 15.7.3 Maximum simulated likelihood ................ 519 15.7.4 The asmprobit command .................... 519 15.7.5 Application of the asmprobit command ............ 520 15.7.6 Predicted probabilities and MEs ................ 522 15.8 Random-parameters logit ........................ 522 15.8.1 Random-parameters logit ................... 523 15.8.2 The mixlogit command ..................... 523 15.8.3 Data preparation for mixlogit ................. 524 15.8.4 Application of the mixlogit command ............. 524 15.9 Ordered outcome models ......................... 52ъ 15.9.1 Data summary ......................... ó2o 15.9.2 Ordered outcomes .......,................ o26 15.9.3 Application of the ologit command .............. ó?. í 15.9.4 Predicted probabilities ..................... o?8 15.9.5 MEs .........................,,.,., 528 15.9.6 Other ordered models ...................... 529 15.10 Multivariate outcomes .......................... 529 15.10.1 Bivariate probit ......................... 529 15.10.2 Nonlinear SUR ......................... 532 15.11 Stata resources .............................. 532 15.12 Exercises .................................. 533 16 Tobit and selection models 535 16.1 Introduction ................................ 535 16.2 Tobit model ................................ 535 16.2.1 Regression with censored data ................. 535 16.2.2 Tobit model setup ........................ 536 16.2.3 Unknown censoring point ................... 537 16.2.4 Tobit estimation ........................ 537 16.2.5 ML estimation in Stata ..................... 538 16.3 Tobit model example ........................... 538 16.3.1 Data summary ......................... 538 16.3.2 Tobit analysis .......................... 539 16.3.3 Prediction after tobit ...................... 540 16.3.4 Marginal effects ......................... 541 Left-truncated, left-censored, and right-truncated examples 541 Left-censored case computed directly ............. 542 Marginal impact on probabilities ............... 543 16.3.5 The ivtobit command ...................... 544 16.3.6 Additional commands for censored regression ........ 545 16.4 Tobit for lognormal data ......................... 545 16.4.1 Data example .......................... 546 16.4.2 Setting the censoring point for data in logs .......... 546 16.4.3 Results .............................. 547 16.4.4 Two-limit tobit ......................... 548 16.4.5 Model diagnostics ........................ 549 16.4.6 Tests of normality and homoskedasticity ........... 550 Generalized residuals and scores ................ 550 Test of normality ........................ 551 Test of homoskedasticity .................... 552 16.4.7 Next step? ............................ 553 16.5 Two-part model in logs .......................... 553 16.5.1 Model structure ......................... 553 16.5.2 Part 1 specification ....................... 554 16.5.3 Part 2 of the two-part model .................. 555 16.6 Selection model .............................. 556 16.6.1 Model structure and assumptions ............... 556 16.6.2 ML estimation of the sample-selection model ......... 558 16.6.3 Estimation without exclusion restrictions ........... 558 16.6.4 Two-step estimation ...................... 560 16.6.5 Estimation with exclusion restrictions ............ 561 16.7 Prediction from models with outcome in logs ............. 562 16.7.1 Predictions from tobit ..................... 563 16.7.2 Predictions from two-part model ............... 564 16.7.3 Predictions from selection model ............... 565 16.8 Stata resources .............................. 565 16.9 Exercises .................................. 566 17 Count-data models 567 17.1 Introduction ................................ 567 17.2 Features of count data ......................... . об/ 17.2.1 Generated Poisson data .................... 568 17.2.2 Overdispersion and negative binomial data ......... . o69 17.2.3 Modeling strategies ....................... 570 17.2.4 Estimation methods ...................... 571 17.3 Empirical example 1........................... 571 17.3.1 Data summary ......................... 571 17.3.2 Poisson model .......................... 572 Poisson model results ...................... 573 Robust estimate of VCE for Poisson MLE .......... 574 Test of overdispersion ...................... 575 Coefficient interpretation and marginal effects ........ 576 17.3.3 NB2 model ........................... 577 NB2 model results ....................... 577 Fitted probabilities for Poisson and NB2 models ....... 579 The countfit command ..................... 579 The prvalue command ..................... 581 Discussion ............................ 581 Generalized NB model ..................... 581 17.3.4 Nonlinear least-squares estimation .............. 582 17.3.5 Hurdle model .......................... 583 Variants of the hurdle model .................. 585 Application of the hurdle model ................ 585 17.3.6 Finite-mixture models ..................... 589 FMM specification ....................... 589 Simulated FMM sample with comparisons .......... 589 ML estimation of the FMM .................. 591 The fmm command ....................... 592 Application: Poisson finite-mixture model .......... 592 Interpretation .......................... 593 Comparing marginal effects .................. 594 Application: NB finite-mixture model ............. 596 Model selection ......................... 598 Cautionary note ......................... 599 17.4 Empirical example 2........................... 599 17.4.1 Zero-inflated data ........................ 599 17.4.2 Models for zero-inflated data ................. 600 17.4.3 Results for the NB2 model ................... 601 The prcounts command .................... 602 17.4.4 Results for ZINB ........................ 603 17.4.5 Model comparison ........................ 604 The countfit command ..................... 604 Model comparison using countfit ............... 604 17.5 Models with endogenous regressors ................... 60o 17.5.1 Structural-model approach ................... 606 Model and assumptions .................... 606 Two-step estimation ...................... 60Y Application ........................... 607 17.5.2 Nonlinear IV method ...................... 610 17.6 Stata resources .............................. 611 17.7 Exercises .................................. 612 18 Nonlinear panel models 615 18.1 Introduction ................................ 615 18.2 Nonlinear panel-data overview ...................... 615 18.2.1 Some basic nonlinear panel models .............. 615 FE models ............................ 616 RE models ............................ 616 Pooled models or population-averaged models ........ 616 Comparison of models ..................... 617 18.2.2 Dynamic models ........................ 61/ 18.2.3 Stata nonlinear panel commands ............... 617 18.3 Nonlinear panel-data example ...................... 618 18.3.1 Data description and summary statistics ........... 618 18.3.2 Panel-data, organization .................... 620 18.3.3 Within and between variation ................. 620 18.3.4 FE or RE model for these data? ................ 621 18.4 Binary outcome models ......................... 621 18.4.1 Panel summary of the dependent variable .......... 621 18.4.2 Pooled logit estimator ..................... 622 18.4.3 The xtlogit command ...................... 623 18.4.4 The xtgee command ...................... 624 18.4.5 PA logit estimator ....................... 624 18.4.6 RE logit estimator ....................... 625 18.4.7 FE logit estimator ....................... 627 18.4.8 Panel logit estimator comparison ............... 629 18.4.9 Prediction and marginal effects ................ 630 18.4.10 Mixed-effects logit estimator .................. 630 18.5 Tobit model ................................ 631 1.8.5.1 Panel summary of the dependent variable .......... 631 18.5.2 RE tobit model ......................... 631 18.5.3 Generalized tobit models .................... 632 18.5.4 Parametric nonlinear panel models .............. 633 18.6 Count-data models ............................ 633 18.6.1 The xtpoisson command .................... 633 18.6.2 Panel summary of the dependent variable .......... 634 18.6.3 Pooled Poisson estimator .................... 634 18.6.4 PA Poisson estimator ...................... 635 18.6.5 RE Poisson estimators ..................... 636 18.6.6 FE Poisson estimator ...................... 638 18.6.7 Panel Poisson estimators comparison ............. 640 18.6.8 Negative binomial estimators ................. 641 18.7 Stata resources .............................. 642 18.8 Exercises .................................. 643 A Programming in Stata 645 A.I Stata matrix commands ......................... 645 A. 1.1 Stata matrix overview ..................... 645 A. 1.2 Stata matrix input and output ................ 645 Matrix input by hand ...................... 645 Matrix input from Stata estimation results .......... 646 A. 1.3 Stata matrix subscripts and combining matrices ....... 647 Α. 1.4 Matrix Operators........................ 648 Α. 1.5 Matrix functions ........................ 648 Α. 1.6 Matrix accumulation commands ................ 649 A. 1.7 OLS using Stata matrix commands .............. 650 A. 2 Programs ................................. 651 A. 2.1 Simple programs (no arguments or access to resulta) .... 651 A. 2.2 Modifying a program ...................... 6o2 A. 2.3 Programs with positional arguments ............. 652 A. 2.4 Temporary variables ...................... 653 A. 2.5 Programs with named positional arguments ......... 653 A. 2.6 Storing and retrieving program results ............ 654 A. 2.7 Programs with arguments using standard Stata syntax . . . 655 A.2.8 Ado-files ............................. 656 A.3 Program debugging ............................ 657 A. 3.1 Some simple tips ........................ 658 A. 3.2 Error messages and return code ................ 658 A. 3.3 Trace ............................... 659 Mata 661 B.I How to run Mata ............................. 661 В. 1.1 Alata commands in Alata.................... 661 8. 1.2 Alata commands in Stata .................... 662 8. 1.3 Stata commands in Mata .................... 662 8. 1.4 Interactive versus batch ase .............. . . . . 662 В. Lo Mata help ............................ 6(P В. 2 Mata matrix commands ....................... , , 663 В. .2.1 Mata matrix input ....................... 663 Matrix input by hand ...................... 663 Identity matrices, unit vectors, and matrices of constants , , 664 Matrix input from Stata data ................. 665 Matrix input from Stata matrix ................ 665 Stata interface functions .................... 666 В. 2.2 Mata matrix operators ..................... 666 Element-by-element operators ................. 666 B.2.3 Mata functions ......................... 667 Scalar and matrix functions .................. 667 Matrix inversion ......................... 668 B.2.4 Mata cross products ...................... 669 B.2.5 Mata matrix subscripts and combining matrices ....... 669 B.2.6 Transferring Mata data and matrices to Stata ........ 671 Creating Stata matrices from Mata matrices ......... 671 Creating Stata data from a Mata vector ........... 671 B.3 Programming in Mata .......................... 672 8. 3.1 Declarations ........................... 672 8. 3.2 Mata program .......................... 672 8. 3.3 Mata program with results output to Stata ......... 673 8. 3.4 Stata program that calls a Mata program .......... 673 B.3. 5 Using Mata in ado-files ..................... 674 Glossary of abbreviations 675 References 679 Author index 687 Subject index 691 Aimed at both students and researchers o£ economics and related social sciences, Microeconometrics Using Stata, Revised Edition provides the most complete and up-to-date survey of microeconometric methods available in Stata, including linear regression, simulation, instrumental-variables estimation, quantile regression, random- and fixed-effects models, linear mixed models, analytical and bootstrap inference, and nonlinear models for binary, multinomial, censored, and count outcomes for both cross-sectional and panel datasets. This revised edition incorporates new features added in Stata 11 that make it easier to implement methods presented in the original edition of the book—factor variables, the margins command for marginal effects, and the gnun command. Colin Cameron is a professor of economics at the University of California-Davis where he teaches econometrics at undergraduate and graduate levels, as well as an undergraduate course in health economics. He has additionally taught several short courses in econometrics in Europe and Australia. His research interests span a range of topics within microeconometrics. He is a past director of the Center on Quantitative Social Science Research at UC-Davis and is currently an associate editor of the Stata Journal. Pravin K. Trivedi is currently the J.H. Rudy professor in the Department of Economics at Indiana University-Bloomington. During his academic career, he has taught undergraduate- and graduate-level econometrics in the United States, Europe, and Australia. His research interests are in microeconometrics and health economics. He served as coeditor of the Econometrics Journal from 2000-2007 and has been on the editorial board of the Journal of Applied Econometrics since 1988. He has coauthored (with David Zimmer) Copula Modeling in Econometrics: An Introduction for Practitioners (2007). Cameron and Trivedi s joint work includes research articles on econometric models and tests for count data, the Econometric Society monograph Regression Analysis of Count Data, and the graduate-level text Microeconometrics: Methods and Applications.
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discipline	Informatik Soziologie Wirtschaftswissenschaften
edition	Rev. ed.
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id	DE-604.BV036094687
illustrated	Illustrated
indexdate	2024-07-09T22:11:27Z
institution	BVB
isbn	9781597180733
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-018985212
oclc_num	739097894
open_access_boolean
owner	DE-19 DE-BY-UBM DE-N2 DE-M382 DE-739 DE-703 DE-20 DE-384 DE-11 DE-188 DE-473 DE-BY-UBG DE-91 DE-BY-TUM DE-945 DE-355 DE-BY-UBR DE-521 DE-Re13 DE-BY-UBR DE-858 DE-824 DE-634 DE-Aug4 DE-1028 DE-2070s DE-92 DE-91S DE-BY-TUM DE-706 DE-83 DE-M158
owner_facet	DE-19 DE-BY-UBM DE-N2 DE-M382 DE-739 DE-703 DE-20 DE-384 DE-11 DE-188 DE-473 DE-BY-UBG DE-91 DE-BY-TUM DE-945 DE-355 DE-BY-UBR DE-521 DE-Re13 DE-BY-UBR DE-858 DE-824 DE-634 DE-Aug4 DE-1028 DE-2070s DE-92 DE-91S DE-BY-TUM DE-706 DE-83 DE-M158
physical	XLII, 706 S. graph. Darst.
publishDate	2010
publishDateSearch	2010
publishDateSort	2010
publisher	Stata Press
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series2	A Stata Press publication
spelling	Cameron, Adrian Colin 1956- Verfasser (DE-588)12870022X aut Microeconometrics using Stata A. Colin Cameron ; Pravin K. Trivedi Rev. ed. College Station, Tex. Stata Press 2010 XLII, 706 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier A Stata Press publication Mikroökonomie (DE-588)4039225-9 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Stata (DE-588)4617285-3 gnd rswk-swf Mikroökonomisches Modell (DE-588)4125908-7 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 s Mikroökonomie (DE-588)4039225-9 s Stata (DE-588)4617285-3 s DE-604 Mikroökonomisches Modell (DE-588)4125908-7 s DE-188 Trivedi, Pravin K. 1943- Verfasser (DE-588)118054791 aut Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018985212&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018985212&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext
spellingShingle	Cameron, Adrian Colin 1956- Trivedi, Pravin K. 1943- Microeconometrics using Stata Mikroökonomie (DE-588)4039225-9 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Stata (DE-588)4617285-3 gnd Mikroökonomisches Modell (DE-588)4125908-7 gnd
subject_GND	(DE-588)4039225-9 (DE-588)4043212-9 (DE-588)4617285-3 (DE-588)4125908-7
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	Mikroökonomie (DE-588)4039225-9 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Stata (DE-588)4617285-3 gnd Mikroökonomisches Modell (DE-588)4125908-7 gnd
topic_facet	Mikroökonomie Ökonometrisches Modell Stata Mikroökonomisches Modell
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018985212&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018985212&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT cameronadriancolin microeconometricsusingstata AT trivedipravink microeconometricsusingstata

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