
Contents
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10.1 Data Errors 10.1 Data Errors
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10.1.1 Measurement Error 10.1.1 Measurement Error
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10.1.2 Errors-in-Variables Estimation 10.1.2 Errors-in-Variables Estimation
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10.1.3 Instrumental Variables Estimation and the Generalized Method of Moments 10.1.3 Instrumental Variables Estimation and the Generalized Method of Moments
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10.1.4 Current vs. Permanent Correlations 10.1.4 Current vs. Permanent Correlations
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10.2 Bias in Intergenerational Autoregressions 10.2 Bias in Intergenerational Autoregressions
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10.3 The Generated-Regressor Methodology 10.3 The Generated-Regressor Methodology
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10.3.1 Implementing the Generated-Regressor Methodology 10.3.1 Implementing the Generated-Regressor Methodology
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10.3.2 The Cochrane-Orcutt Method 10.3.2 The Cochrane-Orcutt Method
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10.3.3 “Mincer Residuals” as Generated Regressors 10.3.3 “Mincer Residuals” as Generated Regressors
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10.4 Instrumental Variables 10.4 Instrumental Variables
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10.4.1 Natural Experiments and Instrumental Variables 10.4.1 Natural Experiments and Instrumental Variables
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10.4.2 Relationship between IV and Generated Regressors 10.4.2 Relationship between IV and Generated Regressors
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10.5 Multiple Phenotypes and Single Genotypes 10.5 Multiple Phenotypes and Single Genotypes
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10.5.1 Panel-Data Model 10.5.1 Panel-Data Model
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10.6 Heterogeneity 10.6 Heterogeneity
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10.7 Sibling Effects 10.7 Sibling Effects
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10.7.1 IV estimation 10.7.1 IV estimation
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10.7.2 Seemingly Unrelated Regression 10.7.2 Seemingly Unrelated Regression
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10.8 Peer-Group Effects 10.8 Peer-Group Effects
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10.9 Neighbor Effects 10.9 Neighbor Effects
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10.10 Endogenous Behavior 10.10 Endogenous Behavior
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10.10.1 Endogenous Parenting 10.10.1 Endogenous Parenting
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10.10.2 Endogenous Child Behavior 10.10.2 Endogenous Child Behavior
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10.11 Selectivity 10.11 Selectivity
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10.12 Partial Identification 10.12 Partial Identification
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10.12.1 Counterfactuals 10.12.1 Counterfactuals
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10.12.2 Non-Parametric Bounds 10.12.2 Non-Parametric Bounds
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10.12.3 Placebo 10.12.3 Placebo
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10.13 Behavioral Genetics 10.13 Behavioral Genetics
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10.13.1 Decomposing the Phenotype Variance 10.13.1 Decomposing the Phenotype Variance
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10.13.2 Heterogeneity in Sensitivity to the Environment 10.13.2 Heterogeneity in Sensitivity to the Environment
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10.13.3 Goldberger’s Critique 10.13.3 Goldberger’s Critique
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10.14 The Structural Social Science Model 10.14 The Structural Social Science Model
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10.14.1 Gene-Environment Correlation and Interaction in SSSM 10.14.1 Gene-Environment Correlation and Interaction in SSSM
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10.14.2 Sibling Correlations 10.14.2 Sibling Correlations
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10.14.3 Sibling Interaction in SSSM 10.14.3 Sibling Interaction in SSSM
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10.14.4 Orphans 10.14.4 Orphans
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10.15 Genome-Wide Association Studies (GWAS)Close 10.15 Genome-Wide Association Studies (GWAS)Close
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10.15.1 Statistical Testing 10.15.1 Statistical Testing
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10.15.2 Stratification Bias 10.15.2 Stratification Bias
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10.16 Sequencing vs. CausalityClose 10.16 Sequencing vs. CausalityClose
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Cite
Abstract
This chapter discusses empirical methodology, focusing on attenuation bias induced by measurement error or data fluctuation and solutions to the biased and inconsistent empirical estimates of the autoregression model. It shows that the identification problem arises in large part due to the non-observability of genotypes. Even if genotypes can be observed, an identification problem in observational data might still arise because genotypes and outcomes, such as wages, schooling, mothering, and infant distress, are jointly determined. One solution is to exploit panel data, which in principle exist for generations of the same family (greatgrandchildren, grandchildren, children, parents, grandparents, greatgrandparents). A number of methodologies may be used to solve the identification problem, including the instrumental-variables estimator. The chapter also presents a methodological critique of behavioral genetics and genome-wide association studies and describes the equal-environments assumption. It argues that behavioral genetics cannot identify the role of heredity in the correlation between siblings.
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