Microeconometrics using stata Volume 1 Cross-sectional and panel regression methods
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| Format: | Buch |
| Sprache: | English |
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College Station, Texas
Stata Press
2022
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| Ausgabe: | Second edition |
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| Online-Zugang: | Inhaltsverzeichnis |
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Datensatz im Suchindex
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| adam_text | Contents List of tables xiii List of figures XV Preface to the SecondEdition Preface to the FirstEdition 1 2 xvii xix 1 Stata basics 1.1 Interactive use ........................................................................................ 1 1.2 Documentation........................................................................................ 2 1.3 Command syntax and operators............................................................. 5 1.4 Do-files and log files............................................................................... 14 1.5 Scalars and matrices............................................................................... 19 1.6 Using results from Stata commands....................................................... 20 1.7 Global and local macros......................................................................... 23 1.8 Looping commands.................................................................................. 26 1.9 Mata and Python in Stata...................................................................... 29 1.10 Some useful commands............................................................................ 29 1.11 Template do-file........................................................................... 30 1.12 Community-contributed commands....................................................... 30 1.13 Additional resources............................................................................... 31 1.14 Exercises.................................................................................................... 31
Data management and graphics 33 2.1 Introduction.............................................................................................. 33 2.2 Types of data........................................................................................... 33 2.3 Inputting data ........................................................................................ 36 2.4 Data management 43 ..................................................................................
Contents vi 3 4 5 2.5 Manipulating datasets.............................................................................. 60 2.6 Graphical display of data ........................................................................ 67 2.7 Additional resources................................................................................. 83 2.8 Exercises...................................................................................................... 83 Linear regression basics 85 3.1 Introduction................................................................................................ 85 3.2 Data and data summary........................................................................... 85 3.3 Transformation of data • before regression................................................ »· 94 3.4 Linear regression....................................................................................... 96 3.5 Basic regression analysis........................................................................... 102 3.6 Specification analysis................................................................................. 123 3.7 Specification tests....................................................................................... 132 3.8 Sampling weights....................................................................................... 140 3.9 OLS using Mata ....................................................................................... 145 3.10 Additional resources................................................................................. 147 3.11
Exercises...................................................................................................... 147 Linear regression extensions 149 4.1 Introduction....................................................... 4.2 In-sample prediction....................................................................................,149 4.3 Out-of-sample prediction........................................................................... 157 4.4 Predictive margins.................................................................................... 161 4.5 Marginal effects.......................................................................................... 175 4.6 Regression decompositionanalysis 186 4.7 Shapley decomposition ofrelative regressor importance............................193 4.8 Difference-in-differences estimators........................................................ 195 4.9 Additional resources................................................................................. 204 4.10 Exercises...................................................................................................... 204 Simulation 207 5.1 Introduction................................................................................................ 207 5.2 Pseudorandom-number generators 208 .......................................................... ........................................................ 149
vii Contents 6 7 5.3 Distribution of the sample mean ........................................................... 214 5.4 Pseudorandom-number generators: Further details............................. 220 5.5 Computing integrals................................................................................. 227 5.6 Simulation for regression: Introduction.................................................. 232 5.7 Additional resources................................................................................. 242 5.8 Exercises...................................................................................................... 242 Linear regression with correlated errors 245 6.1 Introduction................................................................................................ 245 6.2 Generalized least-squares and FGLS regression................................... 246 6.3 Modeling heteroskedastic data.................................................................. 250 6.4 OLS for clustered data.............................................................................. 256 6.5 FGLS estimators for clustered data......................................................... 265 6.6 Fixed-effects estimator for clustered data............................................... 269 6.7 Linear mixed models for clustered data.................................................. 277 6.8 Systems of linear regressions..................................................................... 286 6.9 Survey data: Weighting, clustering, andstratification........................... 295 6.10
Additional resources................................................................................. 301 6.11 Exercises...................................................................................................... 302 Linear instrumental-variables regression 305 7.1 Introduction................................................................................................ 305 7.2 Simultaneous equations model.................................................................. 306 7.3 Instrumental-variables regression............................................................ 310 7.4 Instrumental-variables example............................................................... 316 7.5 Weak instruments....................................... ............................................. 330 7.6 Diagnostics and tests for weak instruments............................................ 339 7.7 Inference with weak instruments 353 7.8 Finite sample inference with weak instruments ................................... 362 7.9 Other estimators....................................................................................... 363 ............................................................ 7.10 Three-stage least-squares systems estimation ...................................... 367
Contents viii 7.11 Additional resources................................................................................ 368 7.12 Exercises....................................................................................................... 369 8 Linear panel-data models: Basics 373 8.1 Introduction.................................................................................................... 373 8.2 Panel-data methods overview.................................................................. 373 8.3 Summary of panel data ........................................................................... 379 8.4 Pooled or population-averaged estimators............................................ 394 8.5 8.6 Fixed-effects or within estimator............................................................... 397 • *Between estimator........................................................................................ 401 8.7 Random-effects estimator 8.8 Comparison of estimators............................................................................406 8.9 First-difference estimator...............................................................................412 ........................................................................ 402 8.10 Panel-data management............................................................................... 414 8.11 Additional resources..................................................................................... 418 8.12
Exercises.......................................................................................................... 419 9 10 Linear panel-data models: Extensions 421 9.1 Introduction.................................................................................................... 421 9.2 Panel instrumental-variables estimation 9.3 Hausman-Taylor estimator.........................................................................425 9.4 Arellano-Bond estimator 9.5 Long panels.................................................................................................... 445 9.6 Additional resources..................................................................................... 456 9.7 Exercises.......................................................................................................... 456 . ..................................................421 ............................................................................ 428 Introduction to nonlinear regression 459 10.1 Introduction.................................................................................................... 459 10.2 Binary outcome models ............................................................................... 459 10.3 Probit model................................................................................................. 462 10.4 MEs and coefficient interpretation ............................................................ 466 10.5 Logit model.................................................................................................... 472 10.6 Nonlinear least
squares.................................................................................. 474
ix Contents 11 12 10.7 Other nonlinear estimators..................................................................... 476 10.8 Additional resources.............................................................................. 477 10.9 Exercises.................................................................................................... 477 Tests of hypotheses and model specification 479 11.1 Introduction.............................................................................................. 479 11.2 Critical values and p-values.................................................................. 480 11.3 Wald tests and confidence intervals.......................................................... 485 11.4 Likelihood-ratio tests.................................................................................. 498 11.5 Lagrange multiplier test (or score test)................................................ 502 11.6 Multiple testing........................................................................................ 505 11.7 Test size and power.................................................................................. 512 11.8 The power onemean command for multiple regression........................ 519 11.9 Specification tests..................................................................................... 529 11.10 Permutation tests and randomization tests.......................................... 532 11.11 Additional resources............................................................................... 534 11.12
Exercises.................................................................................................... 534 Bootstrap methods 537 12.1 Introduction.............................................................................................. 537 12.2 Bootstrap methods.................................................................................. 537 12.3 Bootstrap pairs using the vce(bootstrap) option................................. 539 12.4 Bootstrap pairs using the bootstrap command.................................... 547 12.5 Percentile-t bootstraps with asymptotic refinement........................... 555 12.6 Wild bootstrap with asymptotic refinement....................................... 560 12.7 Bootstrap pairs using bsample and simulate....................................... 569 12.8 Alternative resampling schemes............................................................ 570 12.9 The jackknife........................................................................................... 575 12.10 Additional resources............................................................................... 576 12.11 Exercises.................................................................................................... 577
x 13 Contents Nonlinear regression methods 579 579 13.1 Introduction............................................................................................... 13.2 Nonlinear example: Doctor visits.............................................................. 580 13.3 Nonlinear regression methods................................................................ 13.4 Different estimates of the VCE................................................................. 597 13.5 Prediction...................................................................................................... 604 13.6 Predictive margins.................................................................................. 609 13.7 Marginal effects . . . . _............................................................................ 612 13.8 Model diagnostics......................................................................................... 629 13.9 Clustered data ......................................................................................... 582 632 13.10 Additional resources................................................................................... 640 14 15 13.11 Exercises..................................................................................................... 640 Flexible regression: Finite mixtures and nonparametric 643 14.1 Introduction............................................................................................... 643 14.2 Models based on finite mixtures................................................................. 644 14.3 FMM example: Earnings
of doctors....................................................... 14.4 Global polynomials...................................................................................... 665 14.5 Regression splines............................................ ........................................ 14.6 Nonparametric regression ......................................................................... *675 14.7 Partially parametric regression ................................................................. 680 14.8 Additional resources................................................................................... 681 14.9 Exercises......................................................................................................... 681 Quantile regression 650 668 683 15.1 Introduction...................................................................................................683 15.2 Conditional quantile regression............................................................. 15.3 CQR for medical expenditures data........................................................... 688 15.4 CQR for generated heteroskedastic data............................................. 699 15.5 Quantile treatment effects for a binary treatment.............................. 703 15.6 Additional resources............................................................................... 706 15.7 Exercises..................................................................................................... 707 684
Contents A Programming in Stata 709 A.l Stata matrix commands........................................................................... 709 Programs.................................................................................................. 716 А.З Program debugging.................................................................................... 722 A.4 Additional resources................................................................................. 725 Mata 727 B.l How to run Mata....................................................................................... 727 B.2 Mata matrix commands........................................................................... 729 B.3 Programming in Mata.............................................................................. 738 B.4 Additional resources................................................................................. 740 Optimization in Mata 741 A.2 B C xi C.l Mata moptimize() function C.2 Mata optimize() function C.3 ................................................................... 741 . .................................................................... 751 Additional resources................................................................................ 754 Glossary of abbreviations 755 References 761 Author index 777 Subject index 783
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| indexdate | 2024-12-24T09:24:38Z |
| institution | BVB |
| isbn | 9781597183611 159718361X |
| language | English |
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| spellingShingle | Cameron, Adrian Colin 1956- Trivedi, Pravin K. 1943- Microeconometrics using stata Stata (DE-588)4617285-3 gnd Mikroökonomisches Modell (DE-588)4125908-7 gnd Mikroökonomie (DE-588)4039225-9 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| subject_GND | (DE-588)4617285-3 (DE-588)4125908-7 (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 Volume 1 Cross-sectional and panel regression methods A. Colin Cameron (Department of Economics, University of California, Davis, CA and School of Economics, University of Sydney, Sydney, Australia), Pravin K. Trivedi (School of Economics, University of Queensland, Brisbane, Australia and Department of Economics, Indiana University, Bloomington, IN) |
| title_fullStr | Microeconometrics using stata Volume 1 Cross-sectional and panel regression methods A. Colin Cameron (Department of Economics, University of California, Davis, CA and School of Economics, University of Sydney, Sydney, Australia), Pravin K. Trivedi (School of Economics, University of Queensland, Brisbane, Australia and Department of Economics, Indiana University, Bloomington, IN) |
| title_full_unstemmed | Microeconometrics using stata Volume 1 Cross-sectional and panel regression methods A. Colin Cameron (Department of Economics, University of California, Davis, CA and School of Economics, University of Sydney, Sydney, Australia), Pravin K. Trivedi (School of Economics, University of Queensland, Brisbane, Australia and Department of Economics, Indiana University, Bloomington, IN) |
| title_short | Microeconometrics using stata |
| title_sort | microeconometrics using stata cross sectional and panel regression methods |
| topic | Stata (DE-588)4617285-3 gnd Mikroökonomisches Modell (DE-588)4125908-7 gnd Mikroökonomie (DE-588)4039225-9 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| topic_facet | Stata Mikroökonomisches Modell Mikroökonomie Ökonometrisches Modell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033671411&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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