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Identification for prediction and decision

Author: Manski, Charles F. Publisher: Harvard University Press, 2007.Language: EnglishDescription: 348 p. : Ill. ; 24 cm.ISBN: 9780674026537Type of document: BookBibliography/Index: Includes bibliographical references and index
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Book Europe Campus
Main Collection
Print H61.4 .M36 2007
(Browse shelf)
001247619
Available 001247619
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Includes bibliographical references and index

Digitized

Identification for Prediction and Decision Contents Preface xiii Introduction 1 The Reflection Problem 1 The Law of Decreasing Credibility 2 Identification and Statistical Inference 3 Prediction and Decisions 6 Coping with Ambiguity 6 Organization of the Book 8 The Developing Literature on Partial Identification 11 I Prediction with Incomplete Data 1 Conditional Prediction 17 1.1 Predicting Criminality 17 1.2 Probabilistic Prediction 18 1.3 Estimation of Best Predictors from Random Samples 22 1.4 Extrapolation 25 1.5 Predicting High School Graduation 28 Complement 1A. Best Predictors under Square and Absolute Loss 30 Complement 1B. Nonparametric Regression Analysis 32 Complement 1 C. Word Problems 34 2 Missing Outcomes 36 2.1 Anatomy of the Problem 37 2.2 Bounding the Probability of Exiting Homelessness 40 2.3 Means of Functions of the Outcome 42 2.4 Parameters That Respect Stochastic Dominance 44 2.5 Distributional Assumptions 45 2.6 Wage Regressions and the Reservation-Wage Model of Labor Supply 48 2.7 Statistical Inference 51 Complement 2A. Interval Measurement of Outcomes 54 Complement 2B. Jointly Missing Outcomes and Covariates 56 Complement 2C. Convergence of Sets to Sets 60 3 Instrumental Variables 62 62 3.1 Distributional Assumptions and Credible Inference 3.2 Missingness at Random 64 3.3 Statistical Independence 66 3.4 Equality of Means 69 3.5 Inequality of Means 71 Complement 3A. Imputations and Nonresponse Weights Complement 3B. Conditioning on the Propensity Score Complement 3C. Word Problems 76 73 75 4 Parametric Prediction 83 4.1 The Normal-Linear Model of Market and Reservation Wages 83 4.2 Selection Models 87 4.3 Parametric Models for Best Predictors 89 Complement 4A. Minimum-Distance Estimation of Partially Identified Models 91 5 Decomposition of Mixtures 94 5.1 The Inferential Problem and Some Manifestations 94 5.2 Binary Mixing Covariates 98 5.3 Contamination through Imputation 102 5.4 Instrumental Variables 105 Complement 5A. Sharp Bounds on Parameters That Respect Stochastic Dominance 107 6 Response-Based Sampling 109 114 6.1 The Odds Ratio and Public Health 110 6.2 Bounds on Relative and Attributable Risk 6.3 Information on Marginal Distributions 6.4 Sampling from One Response Stratum 6.5 General Binary Stratifications 122 118 119 II Analysis of Treatment Response 7 The Selection Problem 127 7.1 Anatomy of the Problem 128 7.2 Sentencing and Recidivism 134 7.3 Randomized Experiments 136 7.4 Compliance with Treatment Assignment 140 7.5 Treatment by Choice 148 7.6 Treatment at Random in Nonexperimental Settings 7.7 Homogeneous Linear Response 153 Complement 7A. Perspectives on Treatment Comparison Complement 7B. Word Problems 160 151 157 8 Linear Simultaneous Equations 167 167 8.1 Simultaneity in Competitive Markets 8.2 The Linear Market Model 170 8.3 Equilibrium in Games 174 8.4 The Reflection Problem 177 9 Monotone Treatment Response 183 9.1 Shape Restrictions 183 9.2 Bounds on Parameters That Respect Stochastic Dominance 186 9.3 Bounds on Treatment Effects 189 9.4 Monotone Response and Selection 191 9.5 Bounding the Returns to Schooling 193 10 The Mixing Problem 198 10.1 Extrapolation from Experiments to Rules with Treatment Variation 198 10.2 Extrapolation from the Perry Preschool Experiment 200 10.3 Identification of Event Probabilities with the Experimental Evidence Alone 204 10.4 Treatment Response Assumptions 206 10.5 Treatment Rule Assumptions 207 10.6 Combining Assumptions 210 11 Planning under Ambiguity 211 11.1 Studying Treatment Response to Inform Treatment Choice 211 11.2 Criteria for Choice under Ambiguity 214 11.3 Treatment Using Data from an Experiment with Partial Compliance 218 11.4 An Additive Planning Problem 222 11.5 Planning with Partial Knowledge of Treatment Response 226 11.6 Planning and the Selection Problem 229 11.7 The Ethics of Fractional Treatment Rules 233 11.8 Decentralized Treatment Choice 235 Complement 11A. Minimax-Regret Rules for Two Treatments Are Fractional 237 Complement 11B. Reporting Observable Variation in Treatment Response 239 Complement 11C. Word Problems 241 12 Planning with Sample Data 243 12.1 Statistical Induction 243 12.2 Wald's Development of Statistical Decision Theory 245 12.3 Using a Randomized Experiment to Evaluate an Innovation 250 III Predicting Choice Behavior 13 Revealed Preference Analysis 259 13.1 Revealing the Preferences of an Individual 260 13.2 Random Utility Models of Population Choice Behavior 263 13.3 College Choice in America 270 13.4 Random Expected-Utility Models 274 Complement 13A. Prediction Assuming Strict Preferences 278 Complement 13B. Axiomatic Decision Theory 282 14 Measuring Expectations 284 14.1 Elicitation of Expectations from Survey Respondents 285 14.2 Illustrative Findings 290 14.3 Using Expectations Data to Predict Choice Behavior 295 14.4 Measuring Ambiguity 298 Complement 14A. The Predictive Power of Intentions Data: A Best-Case Analysis 300 Complement 14B. Measuring Expectations of Facts 305 15 Studying Human Decision Processes 15.1 As-If Rationality and Bounded Rationality 15.2 Choice Experiments 312 15.3 Prospects for a Neuroscientific Synthesis 308 309 317 References Author Index Subject Index 321 339 343

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