Micro-econometrics for policy, program, and treatment effects /
General Material Designation
[Book]
First Statement of Responsibility
Myoung-jae Lee.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
New York :
Name of Publisher, Distributor, etc.
Oxford University Press,
Date of Publication, Distribution, etc.
2005.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource
SERIES
Series Title
Advanced texts in econometrics.
GENERAL NOTES
Text of Note
Title from title screen (viewed Mar. 2, 2006).
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
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1 Tour of the book; 2 Basics of treatment effect analysis; 2.1 Treatment intervention, counter-factual, and causal relation; 2.1.1 Potential outcomes and intervention; 2.1.2 Causality and association; 2.1.3 Partial equilibrium analysis and remarks; 2.2 Various treatment effects and no effects; 2.2.1 Various effects; 2.2.2 Three no-effect concepts; 2.2.3 Further remarks; 2.3 Group-mean difference and randomization; 2.3.1 Group-mean difference and mean effect; 2.3.2 Consequences of randomization; 2.3.3 Checking out covariate balance
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2.4 Overt bias, hidden (covert) bias, and selection problems2.4.1 Overt and hidden biases; 2.4.2 Selection on observables and unobservables; 2.4.3 Linear models and biases; 2.5 Estimation with group mean difference and LSE; 2.5.1 Group-mean difference and LSE; 2.5.2 A job-training example; 2.5.3 Linking counter-factuals to linear models; 2.6 Structural form equations and treatment effect; 2.7 On mean independence and independence*; 2.7.1 Independence and conditional independence; 2.7.2 Symmetric and asymmetric mean-independence; 2.7.3 Joint and marginal independence
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2.8 Illustration of biases and Simpson's Paradox*2.8.1 Illustration of biases; 2.8.2 Source of overt bias; 2.8.3 Simpson's Paradox; 3 Controlling for covariates; 3.1 Variables to control for; 3.1.1 Must cases; 3.1.2 No-no cases; 3.1.3 Yes/no cases; 3.1.4 Option case; 3.1.5 Proxy cases; 3.2 Comparison group and controlling for observed variables; 3.2.1 Comparison group bias; 3.2.2 Dimension and support problems in conditioning; 3.2.3 Parametric models to avoid dimension and support problems; 3.2.4 Two-stage method for a semi-linear model*
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3.3 Regression discontinuity design (RDD) and before-after (BA)3.3.1 Parametric regression discontinuity; 3.3.2 Sharp nonparametric regression discontinuity; 3.3.3 Fuzzy nonparametric regression discontinuity; 3.3.4 Before-after (BA); 3.4 Treatment effect estimator with weighting*; 3.4.1 Effect on the untreated; 3.4.2 Effects on the treated and on the population; 3.4.3 Effciency bounds and effcient estimators; 3.4.4 An empirical example; 3.5 Complete pairing with double sums*; 3.5.1 Discrete covariates; 3.5.2 Continuous or mixed (continuous or discrete) covariates; 3.5.3 An empirical example
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4 Matching4.1 Estimators with matching; 4.1.1 Effects on the treated; 4.1.2 Effects on the population; 4.1.3 Estimating asymptotic variance; 4.2 Implementing matching; 4.2.1 Decisions to make in matching; 4.2.2 Evaluating matching success; 4.2.3 Empirical examples; 4.3 Propensity score matching; 4.3.1 Balancing observables with propensity score; 4.3.2 Removing overt bias with propensity-score; 4.3.3 Empirical examples; 4.4 Matching for hidden bias; 4.5 Difference in differences (DD); 4.5.1 Mixture of before-after and matching; 4.5.2 DD for post-treatment treated in no-mover panels
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SUMMARY OR ABSTRACT
Text of Note
Focusing on non-experimental microeconometric estimation, this work provides literature on how to measure accurately the effects of a treatment, such as a drug, educational programme, or tax regime, on a response variable like an illness, GPA, or income.
OTHER EDITION IN ANOTHER MEDIUM
Title
Micro-econometrics for policy, program, and treatment effects.