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Micro-Econometrics for Policy, Program and Treatment Effects [Pehme köide]

(Singapore Management University)
  • Formaat: Paperback / softback, 264 pages, kõrgus x laius x paksus: 233x157x15 mm, kaal: 413 g
  • Sari: Advanced Texts in Econometrics
  • Ilmumisaeg: 07-Apr-2005
  • Kirjastus: Oxford University Press
  • ISBN-10: 0199267693
  • ISBN-13: 9780199267699
Teised raamatud teemal:
Micro-Econometrics for Policy, Program and Treatment EffectsSuurem pilt
  • Formaat: Paperback / softback, 264 pages, kõrgus x laius x paksus: 233x157x15 mm, kaal: 413 g
  • Sari: Advanced Texts in Econometrics
  • Ilmumisaeg: 07-Apr-2005
  • Kirjastus: Oxford University Press
  • ISBN-10: 0199267693
  • ISBN-13: 9780199267699
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780199267699.html
Märksõnad:
In many disciplines of science it is vital to know the effect of a 'treatment' on a response variable of interest; the effect being known as the 'treatment effect'. Here, the treatment can be a drug, an education program or an economic policy, and the response variable can be an illness, academic achievement or GDP. Once the effect is found, it is possible to intervene to adjust the treatment and attain a desired level of the response variable.

A basic way to measure the treatment effect is to compare two groups, one of which received the treatment and the other did not. If the two groups are homogenous in all aspects other than their treatment status, then the difference between their response outcomes is the desired treatment effect. But if they differ in some aspects in addition to the treatment status, the difference in the response outcomes may be due to the combined influence of more than one factor. In non-experimental data where the treatment is not randomly assigned but self-selected, the subjects tend to differ in observed or unobserved characteristics. It is therefore imperative that the comparison be carried out with subjects similar in their characteristics. This book explains how this problem can be overcome so the attributable effect of the treatment can be found.

This book brings to the fore recent advances in econometrics for treatment effects. The purpose of this book is to put together various economic treatments effect models in a coherent fashion, make it clear which can be parameters of interest, and show how they can be identified and estimated under weak assumptions. The emphasis throughout the book is on semi- and non-parametric estimation methods, but traditional parametric approaches are also discussed. This book is ideally suited to researchers and graduate students with a basic knowledge of econometrics.
1 Tour of the book 1(6)
2 Basics of treatment effect analysis 7(36)
2.1 Treatment intervention, counter-factual, and causal relation
7(4)
2.1.1 Potential outcomes and intervention
7(2)
2.1.2 Causality and association
9(1)
2.1.3 Partial equilibrium analysis and remarks
10(1)
2.2 Various treatment effects and no effects
11(5)
2.2.1 Various effects
11(2)
2.2.2 Three no-effect concepts
13(1)
2.2.3 Further remarks
14(2)
2.3 Group-mean difference and randomization
16(5)
2.3.1 Group-mean difference and mean effect
16(2)
2.3.2 Consequences of randomization
18(1)
2.3.3 Checking out covariate balance
19(2)
2.4 Overt bias, hidden (covert) bias, and selection problems
21(5)
2.4.1 Overt and hidden biases
21(1)
2.4.2 Selection on observables and unobservables
22(3)
2.4.3 Linear models and biases
25(1)
2.5 Estimation with group mean difference and LSE
26(6)
2.5.1 Group-mean difference and LSE
26(2)
2.5.2 A job-training example
28(2)
2.5.3 Linking counter-factuals to linear models
30(2)
2.6 Structural form equations and treatment effect
32(3)
2.7 On mean independence and independence
35(3)
2.7.1 Independence and conditional independence
35(1)
2.7.2 Symmetric and asymmetric mean-independence
36(1)
2.7.3 Joint and marginal independence
37(1)
2.8 Illustration of biases and Simpson's Paradox
38(5)
2.8.1 Illustration of biases
38(2)
2.8.2 Source of overt bias
40(1)
2.8.3 Simpson's Paradox
41(2)
3 Controlling for covariates 43(36)
3.1 Variables to control for
43(6)
3.1.1 Must cases
44(1)
3.1.2 No-no cases
45(1)
3.1.3 Yes/no cases
46(1)
3.1.4 Option case
47(1)
3.1.5 Proxy cases
48(1)
3.2 Comparison group and controlling for observed variables
49(7)
3.2.1 Comparison group bias
49(2)
3.2.2 Dimension and support problems in conditioning
51(2)
3.2.3 Parametric models to avoid dimension and support problems
53(1)
3.2.4 Two-stage method for a semi-linear model
54(2)
3.3 Regression discontinuity design (RDD) and before-after (BA)
56(9)
3.3.1 Parametric regression discontinuity
56(2)
3.3.2 Sharp nonparametric regression discontinuity
58(3)
3.3.3 Fuzzy nonparametric regression discontinuity
61(3)
3.3.4 Before-after (BA)
64(1)
3.4 Treatment effect estimator with weighting
65(7)
3.4.1 Effect on the untreated
67(1)
3.4.2 Effects on the treated and on the population
68(1)
3.4.3 Efficiency bounds and efficient estimators
69(2)
3.4.4 An empirical example
71(1)
3.5 Complete pairing with double sums
72(7)
3.5.1 Discrete covariates
72(2)
3.5.2 Continuous or mixed (continuous or discrete) covariates
74(2)
3.5.3 An empirical example
76(3)
4 Matching 79(38)
4.1 Estimators with matching
80(5)
4.1.1 Effects on the treated
80(2)
4.1.2 Effects on the population
82(2)
4.1.3 Estimating asymptotic variance
84(1)
4.2 Implementing matching
85(7)
4.2.1 Decisions to make in matching
85(3)
4.2.2 Evaluating matching success
88(2)
4.2.3 Empirical examples
90(2)
4.3 Propensity score matching
92(5)
4.3.1 Balancing observables with propensity score
93(1)
4.3.2 Removing overt bias with propensity-score
93(2)
4.3.3 Empirical examples
95(2)
4.4 Matching for hidden bias
97(2)
4.5 Difference in differences (DD)
99(12)
4.5.1 Mixture of before-after and matching
99(1)
4.5.2 DD for post-treatment treated in no-mover panels
100(3)
4.5.3 DD with repeated cross-sections or panels with movers
103(2)
4.5.4 Linear models for DD
105(3)
4.5.5 Estimation of DD
108(3)
4.6 Triple differences (TD)
111(6)
4.6.1 TD for qualified post-treatment treated
112(1)
4.6.2 Linear models for TD
113(2)
4.6.3 An empirical example
115(2)
5 Design and instrument for hidden bias 117(30)
5.1 Conditions for zero hidden bias
117(2)
5.2 Multiple ordered treatment groups
119(4)
5.2.1 Partial treatment
119(3)
5.2.2 Reverse treatment
122(1)
5.3 Multiple responses
123(2)
5.4 Multiple control groups
125(4)
5.5 Instrumental variable estimator (IVE)
129(7)
5.5.1 Potential treatments
129(2)
5.5.2 Sources for instruments
131(3)
5.5.3 Relation to regression discontinuity design
134(2)
5.6 Wald estimator, IVE, and compliers
136(11)
5.6.1 Wald estimator under constant effects
136(2)
5.6.2 IVE for heterogenous effects
138(1)
5.6.3 Wald estimator as effect on compliers
139(3)
5.6.4 Weighting estimators for complier effects
142(5)
6 Other approaches for hidden bias 147(24)
6.1 Sensitivity analysis
147(13)
6.1.1 Unobserved confounder affecting treatment
148(4)
6.1.2 Unobserved confounder affecting treatment and response
152(5)
6.1.3 Average of ratios of biased to true effects
157(3)
6.2 Selection correction methods
160(3)
6.3 Nonparametric bounding approaches
163(4)
6.4 Controlling for post-treatment variables to avoid confounder
167(4)
7 Multiple and dynamic treatments 171(20)
7.1 Multiple treatments
171(6)
7.1.1 Parameters of interest
172(2)
7.1.2 Balancing score and propensity score matching
174(3)
7.2 Treatment duration effects with time-varying covariates
177(4)
7.3 Dynamic treatment effects with interim outcomes
181(10)
7.3.1 Motivation with two-period linear models
181(5)
7.3.2 G algorithm under no unobserved confounder
186(2)
7.3.3 G algorithm for three or more periods
188(3)
Appendix 191(42)
A.1 Kernel nonparametric regression
191(5)
A.2 Appendix for
Chapter 2
196(5)
A.2.1 Comparison to a probabilistic causality
196(2)
A.2.2 Learning about joint distribution from marginals
198(3)
A.3 Appendix for
Chapter 3
201(3)
A.3.1 Derivation for a semi-linear model
201(1)
A.3.2 Derivation for weighting estimators
202(2)
A.4 Appendix for
Chapter 4
204(10)
A.4.1 Non-sequential matching with network flow algorithm
204(2)
A.4.2 Greedy non-sequential multiple matching
206(3)
A.4.3 Nonparametric matching and support discrepancy
209(5)
A.5 Appendix for
Chapter 5
214(7)
A.5.1 Some remarks on LATE
214(2)
A.5.2 Outcome distributions for compliers
216(3)
A.5.3 Median treatment effect
219(2)
A.6 Appendix for
Chapter 6
221(5)
A.6.1 Controlling for affected covariates in a linear model
221(3)
A.6.2 Controlling for affected mean-surrogates
224(2)
A.7 Appendix for
Chapter 7
226(7)
A.7.1 Regression models for discrete cardinal treatments
226(2)
A.7.2 Complete pairing for censored responses
228(5)
References 233(12)
Index 245


Myoung-jae Lee, who is currently at Singapore Management University, has held regular positions at Pennsylvania State University, Tilburg University (the Netherlands), Tsukuba University (Japan), and Sungkyunkwan University (Korea).