Microeconometrics using stata: Volume 2 Nonlinear models and causal inference methods
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Texas
Stata Press
2022
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| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, Seite 819-1675 Diagramme |
| ISBN: | 9781597183628 1597183628 |
Internformat
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| 245 | 1 | 0 | |a Microeconometrics using stata |n Volume 2 |p Nonlinear models and causal inference methods |c 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) |
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Datensatz im Suchindex
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| adam_text | Contents 16 17 List of tables xiii List of figures XV Nonlinear optimization methods 819 16.1 Introduction.............................................................................................. 819 16.2 Ncwton R.aphson method...................................................................... 819 16.3 Gradient methods..................................................................................... 824 16.4 Overview of ml, moptimize(), and optimize() .................................... 829 16.5 The ml command: If method ................................................................ 831 16.6 Checking the program............................................................................ 837 16.7 The ml command: lf0-lf2, d0֊d2, and gfO methods........................... 844 16.8 Nonlinear instrumental-variables (GMM) example.............................. 851 16.9 Additional resources............................................................................... 854 16.10 Exercises.................................................................................................... 854 Binary outcome models 857 17.1 Introduction.............................................................................................. 857 17.2 Some parametric models......................................................................... 858 17.3 Estimation................................................................................................. 860 17.4 Example.................................................................................................... 862 17.5
Goodness of fit and prediction................................................................ 869 17.6 Marginal effects........................................................................................ 877 17.7 Clustered data ........................................................................................ 880 17.8 Additional models .................................................................................. 881 17.9 Endogenous regressors............................................................................ 887 17.10 Grouped and fractional data................................................................... 895
vi Contents 17.11 Additional resources.................................................................................... 898 17.12 Exercises..........................................................................................................898 18 Multinomial models 901 18.1 Introduction................................................................................................... 901 18.2 Multinomial models overview.................................................................... 901 18.3 Multinomial example: Choice of fishingmode.......................................... 905 18.4 Multinomial logit model.......................................................................... 908 18.5 Alternative-specific conditional logitmodel........................................... ** Nested logit model................................................................................... 914 18.7 Multinomial probit model....................................................................... 929 18.8 Alternative-specific random-parameterslogit......................................... 934 18.9 Ordered outcome models.............................................................................. 938 18.6 922 ............................................................................................. 942 18.10 Clustered data 18.11 Multivariate outcomes............................................................................. 943 18.12 Additional resources.................................................................................... 946 19 18.13
Exercises...................................................................................................... 946 Tobit and selection models 949 19.1 Introduction........................................................................ -...................... 949 19.2 Tobit model.................................................................................................. 950 19.3 Tobit model example................................................................................... 953 19.4 Tobit for lognormal data......................................................................... 961 19.5 Two-part model in logs............................................. 970 19.6 Selection models..................................................................................... 974 19.7 Nonnormal models of selection ............................................................. 982 19.8 Prediction from modelswith outcome in logs ..................................... 986 19.9 Endogenous regressors............................................................................ 989 19.10 Missing data ......................................................................................... . 991 19.11 Panel attrition ........................................................................................ 995 19.12 Additional resources................................................................................. 1019 19.13 Exercises....................................................................................................... 1019
vii Contents 20 Count-data models 1021 20.1 Introduction................................................................................................1021 20.2 Modeling strategies for count data........................................................ 1022 20.3 Poisson and negative binomial models.................................................. 1026 20.4 Hurdle model............................................................................................ 1044 20.5 Finite-mixture models............................................................................. 1050 20.6 Zero-inflated models.................................................................................1069 20.7 Endogenous regressors............................................................................. 1079 20.8 Clustered data .......................................................................................... 1089 20.9 Quantile regression for count data ........................................................ 1090 20.10 Additional resources................................................................................. 1096 20.11 Exercises...................................................................................................... 1096 21 Survival analysis for duration data 1099 21.1 Introduction................................................................................................ 1099 21.2 Data and data summary.......................................................................... 1100 21.3 Survivor and hazard
functions................................................................. 1104 21.4 Semiparametric regression model........................................................... 1109 21.5 Fully parametric regression models........................................................ 1118 21.6 Multiple-records data.................................................................................1129 21.7 Discrete-time hazards logit model........................................................... 1132 21.8 Time-varying regressors........................................................................... 1135 21.9 Clustered data.............................................................................. 1136 21.10 Additional resources................................................................................. 1137 21.11 Exercises......................................................... 22 Nonlinear panel models 1137 1139 22.1 Introduction................................................................................................ 1139 22.2 Nonlinear panel-data overview.................................................................. 1139 22.3 Nonlinear panel-data example.................................................................. 1145 22.4 Binary outcome and ordered outcome models...................................... 1148 22.5 Tobit and interval-data models.............................................................. 1167
Contents viii 22.6 Count-data models..................................................................................... 1172 22.7 Panel quantile regression............................................................................ 1184 22.8 Endogenous regressors in nonlinear panel models................................. 1187 22.9 Additional resources.................................................................................. 1188 22.10 Exercises........................................................................................................ 1188 23 24 Parametric models for heterogeneity and endogeneity 1191 23.1 Introduction................................................................................................. 1191 23.2 Finite mixtures and unobserved heterogeneity....................................... 1192 23.3 Empirical examples of FMMs . . !..........................................................1195 23.4 Nonlinear mixed-effects models................................................................ 1224 23.5 Linear structural equation models............................................................. 1231 23.6 Generalized structural equation models................................................... 1251 23.7 ERM commands for endogeneity and selection....................................... 1261 23.8 Additional resources................................................. 23.9 Exercises........................................................................................................ 1266 Randomized control trials and exogenous treatment
effects 1266 1269 24.1 Introduction..................................................................................................1269 24.2 Potential outcomes........................................................... 24.3 Randomized control trials......................................................................... 1272 24.4 Regression in an RCT............................................................................... 1282 24.5 Treatment evaluation with exogenous treatment.................................... 1290 24.6 Treatment evaluation methods and estimators....................................... 1292 24.7 Stata commands for treatment evaluation............................................. 1302 24.8 Oregon Health Insurance Experiment example....................................... 1305 24.9 Treatment-effect estimates using the OHIE data.................................... 1312 1271 24.10 Multilevel treatment effects...................................................................... 1323 24.11 Conditional quantile TEs ...................................................................... 1332 24.12 Additional resources.................................................................................. 1334 24.13 Exercises........................................................................................................ 1335
ix Contents 25 Endogenous treatment effects 1337 25.1 Introduction................................................................................................ 1337 25.2 Parametric methods for endogenous treatment...................................... 1338 25.3 ERM commands for endogenous treatment............................................ 1341 25.4 ET commands for binary endogenous treatment................................... 1348 25.5 The LATE estimator for heterogeneous effects...................................... 1356 25.6 Difference-in-differences and synthetic control...................................... 1363 25.7 Regression discontinuity design.............................................................. 1369 25.8 Conditional quantile regression with endogenous regressors.................1388 25.9 Unconditional quantiles........................................................................... 1394 25.10 Additional resources........................................................ 1401 25.11 Exercises...................................................................................................... 1402 26 Spatial regression 1405 26.1 Introduction................................... 26.2 Overview of spatial regression models......................................................1406 26.3 Geospatial data.......................................................................................... 1407 26.4 The spatial weighting matrix.................................................................. 1411 26.5 OLS regression and test for spatial
correlation...................................... 1413 26.6 Spatial dependence in the error...............................................................1414 26.7 Spatial autocorrelation regression models................................................1417 26.8 Spatial instrumental variables.................................................................. 1427 26.9 Spatial panel-data models........................................................................ 1428 1405 26.10 Additional resources................................................................................. 1429 26.11 Exercises.......................................................................................................1430 27 Semiparametric regression 1433 27.1 Introduction................................................................................................ 1433 27.2 Kernel regression....................................................................................... 1434 27.3 Series regression.......................................................................................... 1438 27.4 Nonparametric single regressor example................................................1440 27.5 Nonparametric multiple regressor example.............................................1450
Contents x 27.6 Partial linear model .................................................................................. 1453 27.7 Single-index model..................................................................................... 1456 27.8 Generalized additive models...................................................................... 1458 27.9 Additional resources.................................................................................. 1461 27.10 Exercises........................................................................................................ 1462 28 1465 Machine learning for prediction and inference 28.1 Introduction..................................................................................................1465 28.2 Measuring the predictive ability of amodel ............................................1466 28.3 Shrinkage estimators........................................... 28.4 Prediction using lasso, ridge, andelasticnet............................................1482 28.5 Dimension reduction.................................................................................. 1493 28.6 Machine learning methods for prediction................................................ 1496 28.7 Prediction application............................................................................... 1501 28.8 Machine learning for inference inpartial linear model............................ 1507 28.9 Machine learning for inference in othermodels 1477 ..................................... 1516 28.10 Additional
resources.................................................................................. 1523 28.11 Exercises........................................................................................................ 1524 29 Bayesian methods: Basics 1527 29.1 Introduction.................................................................................................. 1527 29.2 Bayesian introductory example................................................................ 1528 29.3 Bayesian methods overview...................................................................... 1532 29.4 An i.i.d. example....................................................... 1538 29.5 Linear regression.......................................................... 1549 29.6 A linear regression example...................................................................... 1552 29.7 Modifying the MH algorithm................................................................... 1560 29.8 RE model..................................................................................................... 1562 29.9 Bayesian model selection......................................................................... 1567 29.10 Bayesian prediction..................................................................................... 1569 29.11 Probit example........................................................................................... 1572
Contents xi 29.12 Additional resources.................................................................................. 1576 29.13 Exercises........................................................................................................ 1576 30 Bayesian methods: Markov chain Monte Carlo algorithms 1579 30.1 Introduction..................................................................................................1579 30.2 User-provided log likelihood...................................................................... 1579 30.3 MH algorithm in Mata............................................................................... 1584 30.4 Data augmentation and the Gibbs sampler in Mata...............................1589 30.5 Multiple imputation.................................................................................. 1595 30.6 Multiple-imputation example................................................................... 1599 30.7 Additional resources.................................................................................. 1608 30.8 Exercises........................................................................................................ 1608 Glossary of abbreviations 1611 References 1617 Author index 1635 Subject index 1641
|
| adam_txt |
Contents 16 17 List of tables xiii List of figures XV Nonlinear optimization methods 819 16.1 Introduction. 819 16.2 Ncwton R.aphson method. 819 16.3 Gradient methods. 824 16.4 Overview of ml, moptimize(), and optimize() . 829 16.5 The ml command: If method . 831 16.6 Checking the program. 837 16.7 The ml command: lf0-lf2, d0֊d2, and gfO methods. 844 16.8 Nonlinear instrumental-variables (GMM) example. 851 16.9 Additional resources. 854 16.10 Exercises. 854 Binary outcome models 857 17.1 Introduction. 857 17.2 Some parametric models. 858 17.3 Estimation. 860 17.4 Example. 862 17.5
Goodness of fit and prediction. 869 17.6 Marginal effects. 877 17.7 Clustered data . 880 17.8 Additional models . 881 17.9 Endogenous regressors. 887 17.10 Grouped and fractional data. 895
vi Contents 17.11 Additional resources. 898 17.12 Exercises.898 18 Multinomial models 901 18.1 Introduction. 901 18.2 Multinomial models overview. 901 18.3 Multinomial example: Choice of fishingmode. 905 18.4 Multinomial logit model. 908 18.5 Alternative-specific conditional logitmodel. ** Nested logit model. 914 18.7 Multinomial probit model. 929 18.8 Alternative-specific random-parameterslogit. 934 18.9 Ordered outcome models. 938 18.6 922 . 942 18.10 Clustered data 18.11 Multivariate outcomes. 943 18.12 Additional resources. 946 19 18.13
Exercises. 946 Tobit and selection models 949 19.1 Introduction. -. 949 19.2 Tobit model. 950 19.3 Tobit model example. 953 19.4 Tobit for lognormal data. 961 19.5 Two-part model in logs. 970 19.6 Selection models. 974 19.7 Nonnormal models of selection . 982 19.8 Prediction from modelswith outcome in logs . 986 19.9 Endogenous regressors. 989 19.10 Missing data . . 991 19.11 Panel attrition . 995 19.12 Additional resources. 1019 19.13 Exercises. 1019
vii Contents 20 Count-data models 1021 20.1 Introduction.1021 20.2 Modeling strategies for count data. 1022 20.3 Poisson and negative binomial models. 1026 20.4 Hurdle model. 1044 20.5 Finite-mixture models. 1050 20.6 Zero-inflated models.1069 20.7 Endogenous regressors. 1079 20.8 Clustered data . 1089 20.9 Quantile regression for count data . 1090 20.10 Additional resources. 1096 20.11 Exercises. 1096 21 Survival analysis for duration data 1099 21.1 Introduction. 1099 21.2 Data and data summary. 1100 21.3 Survivor and hazard
functions. 1104 21.4 Semiparametric regression model. 1109 21.5 Fully parametric regression models. 1118 21.6 Multiple-records data.1129 21.7 Discrete-time hazards logit model. 1132 21.8 Time-varying regressors. 1135 21.9 Clustered data. 1136 21.10 Additional resources. 1137 21.11 Exercises. 22 Nonlinear panel models 1137 1139 22.1 Introduction. 1139 22.2 Nonlinear panel-data overview. 1139 22.3 Nonlinear panel-data example. 1145 22.4 Binary outcome and ordered outcome models. 1148 22.5 Tobit and interval-data models. 1167
Contents viii 22.6 Count-data models. 1172 22.7 Panel quantile regression. 1184 22.8 Endogenous regressors in nonlinear panel models. 1187 22.9 Additional resources. 1188 22.10 Exercises. 1188 23 24 Parametric models for heterogeneity and endogeneity 1191 23.1 Introduction. 1191 23.2 Finite mixtures and unobserved heterogeneity. 1192 23.3 Empirical examples of FMMs . . !.1195 23.4 Nonlinear mixed-effects models. 1224 23.5 Linear structural equation models. 1231 23.6 Generalized structural equation models. 1251 23.7 ERM commands for endogeneity and selection. 1261 23.8 Additional resources. 23.9 Exercises. 1266 Randomized control trials and exogenous treatment
effects 1266 1269 24.1 Introduction.1269 24.2 Potential outcomes. 24.3 Randomized control trials. 1272 24.4 Regression in an RCT. 1282 24.5 Treatment evaluation with exogenous treatment. 1290 24.6 Treatment evaluation methods and estimators. 1292 24.7 Stata commands for treatment evaluation. 1302 24.8 Oregon Health Insurance Experiment example. 1305 24.9 Treatment-effect estimates using the OHIE data. 1312 1271 24.10 Multilevel treatment effects. 1323 24.11 Conditional quantile TEs . 1332 24.12 Additional resources. 1334 24.13 Exercises. 1335
ix Contents 25 Endogenous treatment effects 1337 25.1 Introduction. 1337 25.2 Parametric methods for endogenous treatment. 1338 25.3 ERM commands for endogenous treatment. 1341 25.4 ET commands for binary endogenous treatment. 1348 25.5 The LATE estimator for heterogeneous effects. 1356 25.6 Difference-in-differences and synthetic control. 1363 25.7 Regression discontinuity design. 1369 25.8 Conditional quantile regression with endogenous regressors.1388 25.9 Unconditional quantiles. 1394 25.10 Additional resources. 1401 25.11 Exercises. 1402 26 Spatial regression 1405 26.1 Introduction. 26.2 Overview of spatial regression models.1406 26.3 Geospatial data. 1407 26.4 The spatial weighting matrix. 1411 26.5 OLS regression and test for spatial
correlation. 1413 26.6 Spatial dependence in the error.1414 26.7 Spatial autocorrelation regression models.1417 26.8 Spatial instrumental variables. 1427 26.9 Spatial panel-data models. 1428 1405 26.10 Additional resources. 1429 26.11 Exercises.1430 27 Semiparametric regression 1433 27.1 Introduction. 1433 27.2 Kernel regression. 1434 27.3 Series regression. 1438 27.4 Nonparametric single regressor example.1440 27.5 Nonparametric multiple regressor example.1450
Contents x 27.6 Partial linear model . 1453 27.7 Single-index model. 1456 27.8 Generalized additive models. 1458 27.9 Additional resources. 1461 27.10 Exercises. 1462 28 1465 Machine learning for prediction and inference 28.1 Introduction.1465 28.2 Measuring the predictive ability of amodel .1466 28.3 Shrinkage estimators. 28.4 Prediction using lasso, ridge, andelasticnet.1482 28.5 Dimension reduction. 1493 28.6 Machine learning methods for prediction. 1496 28.7 Prediction application. 1501 28.8 Machine learning for inference inpartial linear model. 1507 28.9 Machine learning for inference in othermodels 1477 . 1516 28.10 Additional
resources. 1523 28.11 Exercises. 1524 29 Bayesian methods: Basics 1527 29.1 Introduction. 1527 29.2 Bayesian introductory example. 1528 29.3 Bayesian methods overview. 1532 29.4 An i.i.d. example. 1538 29.5 Linear regression. 1549 29.6 A linear regression example. 1552 29.7 Modifying the MH algorithm. 1560 29.8 RE model. 1562 29.9 Bayesian model selection. 1567 29.10 Bayesian prediction. 1569 29.11 Probit example. 1572
Contents xi 29.12 Additional resources. 1576 29.13 Exercises. 1576 30 Bayesian methods: Markov chain Monte Carlo algorithms 1579 30.1 Introduction.1579 30.2 User-provided log likelihood. 1579 30.3 MH algorithm in Mata. 1584 30.4 Data augmentation and the Gibbs sampler in Mata.1589 30.5 Multiple imputation. 1595 30.6 Multiple-imputation example. 1599 30.7 Additional resources. 1608 30.8 Exercises. 1608 Glossary of abbreviations 1611 References 1617 Author index 1635 Subject index 1641 |
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| 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- |
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| author_sort | Cameron, Adrian Colin 1956- |
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| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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Colin Cameron (Department of Economics, University of California, Davis, CA and School of Economics, University of Sydney, Sydney, Australia), Pravin K. 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| id | DE-604.BV048291434 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T20:03:44Z |
| indexdate | 2024-07-10T09:34:22Z |
| institution | BVB |
| isbn | 9781597183628 1597183628 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033671418 |
| oclc_num | 1346088660 |
| open_access_boolean | |
| owner | DE-945 DE-355 DE-BY-UBR DE-11 DE-188 DE-703 DE-N2 DE-1047 DE-898 DE-BY-UBR DE-384 DE-634 DE-19 DE-BY-UBM |
| owner_facet | DE-945 DE-355 DE-BY-UBR DE-11 DE-188 DE-703 DE-N2 DE-1047 DE-898 DE-BY-UBR DE-384 DE-634 DE-19 DE-BY-UBM |
| physical | xvii, Seite 819-1675 Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Stata Press |
| record_format | marc |
| spelling | Cameron, Adrian Colin 1956- Verfasser (DE-588)12870022X aut Microeconometrics using stata Volume 2 Nonlinear models and causal inference 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) Second edition College Station, Texas Stata Press 2022 xvii, Seite 819-1675 Diagramme txt rdacontent n rdamedia nc rdacarrier Mikroökonomie (DE-588)4039225-9 gnd rswk-swf Mikroökonomisches Modell (DE-588)4125908-7 gnd rswk-swf Stata (DE-588)4617285-3 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 Mikroökonomisches Modell (DE-588)4125908-7 s Trivedi, Pravin K. 1943- Verfasser (DE-588)118054791 aut (DE-604)BV048291216 2 Erscheint auch als Online-Ausgabe, EPUB 978-1-59718-364-2 1-59718-364-4 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033671418&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cameron, Adrian Colin 1956- Trivedi, Pravin K. 1943- Microeconometrics using stata Mikroökonomie (DE-588)4039225-9 gnd Mikroökonomisches Modell (DE-588)4125908-7 gnd Stata (DE-588)4617285-3 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| subject_GND | (DE-588)4039225-9 (DE-588)4125908-7 (DE-588)4617285-3 (DE-588)4043212-9 |
| title | Microeconometrics using stata |
| title_auth | Microeconometrics using stata |
| title_exact_search | Microeconometrics using stata |
| title_exact_search_txtP | Microeconometrics using stata |
| title_full | Microeconometrics using stata Volume 2 Nonlinear models and causal inference 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 2 Nonlinear models and causal inference 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 2 Nonlinear models and causal inference 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 nonlinear models and causal inference methods |
| topic | Mikroökonomie (DE-588)4039225-9 gnd Mikroökonomisches Modell (DE-588)4125908-7 gnd Stata (DE-588)4617285-3 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| topic_facet | Mikroökonomie Mikroökonomisches Modell Stata Ökonometrisches Modell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033671418&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV048291216 |
| work_keys_str_mv | AT cameronadriancolin microeconometricsusingstatavolume2 AT trivedipravink microeconometricsusingstatavolume2 |