Matching, regression discontinuity, difference in differences, and beyond:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Oxford University Press
[2016]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | xviii, 257 Seiten Illustrationen |
| ISBN: | 9780190258740 9780190258733 |
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| 650 | 4 | |a BUSINESS & ECONOMICS / Econometrics | |
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| adam_text | MATCHING, REGRESSION DISCONTINUITY, DIFFERENCE IN DIFFERENCES, AND
BEYOND
/ LEE, MYOUNG-JAEYYEAUTHOR
: 2016
TABLE OF CONTENTS / INHALTSVERZEICHNIS
CHAPTER 1: BASICS OF TREATMENT EFFECT ANALYSIS
CHAPTER 2: MATCHING
CHAPTER 3: NON-MATCHING AND SAMPLE SELECTION
CHAPTER 4: REGRESSION DISCONTINUITY (RD)
CHAPTER 5: DIFFERENCE IN DIFFERENCES (DD)
CHAPTER 6: TRIPLE DIFFERENCE (TD) AND BEYOND
APPENDIX
ONLINE GAUSS PROGRAMS (PS FOR PROPENSITY SCORE)
REFERENCES
DIESES SCHRIFTSTCK WURDE MASCHINELL ERZEUGT.
Titel: Matching, regression discontinuity, difference in differences, and beyond
Autor: Lee, Myoung-jae
Jahr: 2016
CONTENTS
Preface xv
1 Basics of Treatment Effect Analysis 1
1.1 Counterfactual, Intervention, and Causal Relation 1
1.1.1 Potential Outcomes and Intervention 1
1.1.2 Causality and Association 3
1.1.3 Partial Equilibrium Analysis and Remarks 4
1.2 Various Treatment Effects and No Effects 5
1.2.1 Various Effects 5
1.2.2 Three No-Effect Concepts 6
1.2.3 Remarks 7
1.3 Group-Mean Difference and Randomization 8
1.3.1 Group-Mean Difference and Mean Effect 8
1.3.2 Consequences of Randomization 10
1.3.3 Checking Out Covariate Balance 12
1.4 Overt Bias, Hidden Bias, and Selection Problems 13
1.4.1 Overt and Hidden Biases 13
1.4.2 Selection on Observables and Unobservables 14
1.4.3 Linear Models and Biases 16
1.5 Estimation with Group Mean Difference and LSE 18
1.5.1 Group-Mean Difference and LSE 18
1.5.2 Job Training Example 19
1.5.3 Linking Counterfactuals to Linear Models 21
1.6 Structural Form, Assignment, and Marginal Model 22
1.6.1 Structural versus Reduced Forms for Response 22
1.6.2 Treatment Structural Form and Assignment 23
1.6.3 Marginal Structural Model 24
1.7 Simpsons Paradox and False Covariate Control 25
2 Matching 28
2.1 Basics of Matching and Various Effects 28
2.1.1 Main Idea 28
2.1.2 Effect on Treated and Effect on Population 30
2.1.3 Dimension and Support Problems 31
2.1.4 Variables to Control 32
2.2 Implementing Matching 35
2.2.1 Decisions to Make in Matching 35
2.2.2 Matching Estimators 37
2.2.3 Asymptotic Variance Estimation 40
2.2.4 Labor Union Effect on Wage 44
2.3 Propensity Score Matching (PSM) 46
2.3.1 Propensity Score as a Balancing Score 46
2.3.2 Removing Overt Bias with Propensity Score 47
2.3.3 Implementing PSM and Bootstrap 48
2.3.4 PSM Empirical Examples 50
2.3.5 Propensity Score Specification Issues* 52
2.4 Further Remarks 54
2.4.1 Covariate Balance Check 54
2.4.2 Matching for Hidden Bias 56
2.4.3 Prognostic Score and More* 58
3 Nonmatching and Sample Selection 61
3.1 Weighting 61
3.1.1 Weighting Estimator for Effect on Population 61
3.1.2 Other Weighting Estimators and Remarks 63
3.1.3 Asymptotic Distribution of Weighting Estimators* 65
3.1.4 Job Training Effect on Unemployment 66
3.1.5 Doubly Robust Estimator* 67
3.1.6 Weighting for Missing Data* 68
3.2 Regression Imputation 69
3.2.1 Linear Regression Imputation 70
3.2.2 Regression Imputation with Propensity Score 71
3.2.3 Regression Imputation for Multiple Treatment 72
3.2.4 Regression Imputation for Continuous Treatment* 73
3.2.5 Military Service Effect on Wage 74
3.3 Complete Pairing with Double Sum 76
3.3.1 Discrete Covariates 77
3.3.2 Continuous Covariates 79
3.3.3 Nonparametric Distributional Effect Tests* 80
3.4 Treatment Effects under Sample Selection 84
3.4.1 Difficulties with Sample Selection Models 85
3.4.2 Participation, Invisible, and Visible Effects 86
3.4.3 Identification of Three Effects with Mean Differences 87
3.4.4 Religiosity Effect on Affairs 88
3.5 Effect Decomposition in Sample Selection Models* 90
3.5.1 Motivation for Decomposition 90
3.5.2 Decomposition with Linear Selection Model 91
3.5.3 Four Special Models 92
3.5.4 Race Effect on Wage 94
4 Regression Discontinuity 97
4.1 Introducing RD with Before-After 97
4.1.1 BA Examples 97
4.1.2 BA Identification Assumption 98
4.1.3 FromBAtoRD 99
4.2 RD Identification and Features 100
4.2.1 Sharp RD (SRD) and Fuzzy RD (FRD) 101
4.2.2 Identification at Cutoff 102
4.2.3 RD Main Features 104
4.2.4 Class Size Effect on Test Score 106
4.3 RD Estimators 109
4.3.1 LSE for Level Equation 109
4.3.2 IVE for Right-Left Differenced Equation 110
4.3.3 Bandwidth Choice and Remarks 112
4.3.4 High School Completion Effect on Fertility 113
4.4 Specification Tests 116
4.4.1 Breaks in Conditional Means 116
4.4.2 Continuity in Score Density 117
4.5 RD Topics* 119
4.5.1 Spatial Breaks 119
4.5.2 RD for Limited Dependent Variables 120
4.5.3 Measurement Error in Score 121
4.5.4 Regression Kink (RK) and Generalization 123
4.5.5 SRD with Multiple Scores 126
4.5.6 Quantile RD 129
5 Difference in Differences 131
5.1 DD Basics 131
5.1.1 Examples for DD 132
5.1.2 Time-Constant and Time-Varying Qualifications 133
5.1.3 Data Requirement and Notation 135
5.2 DD with Repeated Cross-Sections 136
5.2.1 Identification 136
5.2.2 Identification with Parametric Models 140
5.2.3 Schooling Effect on Fertility: Fuzzy DD 142
5.2.4 Linear Model Estimation for Two Periods or More 144
5.2.5 Earned Income Tax Credit Effect on Work 147
5.2.6 Time-Varying Qualification* 148
5.3 DD with Panel Data 150
5.3.1 Identification 150
5.3.2 Identification and Estimation with Parametric Models 152
5.3.3 Daylight Saving Time Effect on Energy 157
5.4 Panel Stayer DD for Time-Varying Qualification 158
5.4.1 Motivation 158
5.4.2 Effect on In-Stayers Identified by Stayer DD 159
5.4.3 Identification and Estimation with Panel Linear Models 160
5.4.4 Pension Effect on Health Expenditure 162
6 Triple Difference and Beyond 165
6.1 TD Basics and More 165
6.2 TD with Repeated Cross-Sections 166
6.2.1 Identification 166
6.2.2 Identification and Estimation with Linear Models 169
6.2.3 Mandated Benefit Effect on Wage 172
6.3 TD with Panel Data 174
6.3.1 Identification 174
6.3.2 Estimation with Panel Linear Model 175
6.3.3 Tax-Inclusive Price Effect on Demand 177
6.4 GDD and Beyond 178
6.4.1 Motivation for GDD and Beyond 179
6.4.2 Identification for GDD and QD 180
6.4.3 Identified Effects When Panel Linear Model Holds 181
6.4.4 LSE for DD and GDD and Testing for DD Condition 182
6.4.5 Sulfa Drug Effect on Mortality: Is DD Trustworthy? 184
6.5 Clustering Problems and Inference for DD and TD 187
6.5.1 Single Clustering 188
6.5.2 Clustering in Panel Data 194
6.5.3 DD and TD with Cluster-Specific Treatment 199
6.5.4 Details on Cluster Variance Estimator* 202
A APPENDIX 209
A.l Kernel Density and Regression Estimators 209
A.l.l Histogram-Type Density Estimator 209
A.l.2 Kernel Density Estimator 210
A. 1.3 Kernel Regression Estimator 211
A. 1.4 Local Linear Regression 213
A.2 Bootstrap 213
A.2.1 Review on Usual Asymptotic Inference 215
A.2.2 Bootstrap to Find Qu an tiles 216
A.2.3 Percentile-t and Percentile Methods 218
A.2.4 Nonparametric, Parametric, and Wild Bootstraps 219
A.3 Confounder Detection, IVE, and Selection Correction 220
A.3.1 Coherence Checks 220
A.3.2 IVE and Compiler Effect 225
A.3.3 Selection Correction Approach 230
A.4 Supplements for DD Chapter 232
A.4.1 Nonparametric Estimators for Repeated Cross-Section DD 233
A.4.2 Nonparametric Estimation for DD with Two-Wave Panel Data 233
A.4.3 Panel Linear Model Estimation for DD with One-Shot
Treatment 236
A.4.4 Change in Changes 238
References 241
Index 253
|
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| spellingShingle | Lee, Myoung-jae 1958- Matching, regression discontinuity, difference in differences, and beyond BUSINESS & ECONOMICS / Econometrics bisacsh BUSINESS & ECONOMICS / Statistics bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Statistik Wirtschaft Econometrics Statistical matching Regression analysis Economics Research Methodology BUSINESS & ECONOMICS / Econometrics BUSINESS & ECONOMICS / Statistics MATHEMATICS / Probability & Statistics / General Matching (DE-588)4212483-9 gnd Ökonometrie (DE-588)4132280-0 gnd Kausalanalyse (DE-588)4163511-5 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
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| title | Matching, regression discontinuity, difference in differences, and beyond |
| title_auth | Matching, regression discontinuity, difference in differences, and beyond |
| title_exact_search | Matching, regression discontinuity, difference in differences, and beyond |
| title_full | Matching, regression discontinuity, difference in differences, and beyond Myoung-jae Lee |
| title_fullStr | Matching, regression discontinuity, difference in differences, and beyond Myoung-jae Lee |
| title_full_unstemmed | Matching, regression discontinuity, difference in differences, and beyond Myoung-jae Lee |
| title_short | Matching, regression discontinuity, difference in differences, and beyond |
| title_sort | matching regression discontinuity difference in differences and beyond |
| topic | BUSINESS & ECONOMICS / Econometrics bisacsh BUSINESS & ECONOMICS / Statistics bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Statistik Wirtschaft Econometrics Statistical matching Regression analysis Economics Research Methodology BUSINESS & ECONOMICS / Econometrics BUSINESS & ECONOMICS / Statistics MATHEMATICS / Probability & Statistics / General Matching (DE-588)4212483-9 gnd Ökonometrie (DE-588)4132280-0 gnd Kausalanalyse (DE-588)4163511-5 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | BUSINESS & ECONOMICS / Econometrics BUSINESS & ECONOMICS / Statistics MATHEMATICS / Probability & Statistics / General Statistik Wirtschaft Econometrics Statistical matching Regression analysis Economics Research Methodology Matching Ökonometrie Kausalanalyse Regressionsanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029085908&sequence=000001&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=029085908&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT leemyoungjae matchingregressiondiscontinuitydifferenceindifferencesandbeyond |
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