Understanding Instrumental Variables in Models with Essential Heterogeneity
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[1] A. Shaked,et al. Relaxing price competition through product differentiation , 1982 .
[2] Edward Vytlacil,et al. Local Instrumental Variables , 2000 .
[3] P. Bickel. Some contributions to the theory of order statistics , 1967 .
[4] James Durbin,et al. Errors in variables , 1954 .
[5] J. Heckman,et al. Instrumental Variables, Selection Models, and Tight Bounds on the Average Treatment Effect , 2000 .
[6] J. Heckman,et al. Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College , 2003 .
[7] James J. Heckman,et al. The relationship between treatment parameters within a latent variable framework , 2000 .
[8] J. Sargan. THE ESTIMATION OF ECONOMIC RELATIONSHIPS USING INSTRUMENTAL VARIABLES , 1958 .
[9] J. Heckman. Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture , 2001, Journal of Political Economy.
[10] J. Hausman. Specification tests in econometrics , 1978 .
[11] James J. Heckman,et al. Characterizing Selection Bias Using Experimental Data , 1998 .
[12] Donald W. K. Andrews,et al. Semiparametric Estimation of the Intercept of a Sample Selection Model , 1998 .
[13] E. Prescott,et al. Sequential location among firms with foresight , 1977 .
[14] Joshua D. Angrist,et al. Identification of Causal Effects Using Instrumental Variables , 1993 .
[15] James J. Heckman,et al. Using matching, instrumental variables and control functions to estimate economic choice models , 2004 .
[16] J. Heckman,et al. Policy-Relevant Treatment Effects , 2001 .
[17] H. Theil. A Rank-Invariant Method of Linear and Polynomial Regression Analysis , 1992 .
[18] James J. Heckman,et al. Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs , 2005 .
[19] T. Thompson. IDENTIFICATION OF SEMIPARAMETRIC DISCRETE CHOICE MODELS , 1989 .
[20] James J. Heckman,et al. Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments , 2007 .
[21] Petra E. Todd,et al. Matching As An Econometric Evaluation Estimator , 1998 .
[22] James J. Heckman,et al. Alternative methods for solving the problem of selection bias in evaluating the impact of treatments , 1986 .
[23] James J. Heckman,et al. Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation , 2007 .
[24] S. Yitzhaki,et al. The Gini Instrumental Variable, or the “double instrumental variable” estimator , 2004 .
[25] James J. Heckman,et al. Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males , 1998, Journal of Political Economy.
[26] H. White. Asymptotic theory for econometricians , 1985 .
[27] Bo E. Honoré,et al. The Empirical Content of the Roy Model , 1990 .
[28] J. Angrist,et al. Empirical Strategies in Labor Economics , 1998 .
[29] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[30] James J. Heckman,et al. Four Parameters of Interest in the Evaluation of Social Programs , 2001 .
[31] H. James. VARIETIES OF SELECTION BIAS , 1990 .
[32] I. Walker,et al. The marginal and average returns to schooling in the UK , 1999 .
[33] L. Hansen. Large Sample Properties of Generalized Method of Moments Estimators , 1982 .
[34] J. Heckman,et al. Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice , 2003, SSRN Electronic Journal.
[35] C. Manski,et al. Monotone Instrumental Variables with an Application to the Returns to Schooling , 1998 .
[36] Robert D. Mare,et al. Social Background and School Continuation Decisions , 1980 .
[37] J J Heckman,et al. Local instrumental variables and latent variable models for identifying and bounding treatment effects. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[38] Shlomo Yitzhaki,et al. Gini’s Mean difference: a superior measure of variability for non-normal distributions , 2003 .
[39] G. Imbens. Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review , 2004 .
[40] Donald W. K. Andrews,et al. An introduction to econometric applications of empirical process theory for dependent random variables , 1993 .
[41] Jeffrey R. Kling,et al. Interpreting Instrumental Variables Estimates of the Returns to Schooling , 2000 .
[42] J. Heckman. Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations. , 1997 .
[43] J. Heckman,et al. Longitudinal Analysis of Labor Market Data: Alternative methods for evaluating the impact of interventions , 1985 .
[44] J. Heckman,et al. Identification and SQRT N Efficient Estimation of Semiparametric Panel Data Models with Binary Dependent Variables and a Latent Factor , 2000 .
[45] J. Powell,et al. Semiparametric estimation of censored selection models with a nonparametric selection mechanism , 1993 .
[46] R. Quandt. The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes , 1958 .
[47] J. Angrist,et al. Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity , 1995 .
[48] Jianqing Fan,et al. Local polynomial modelling and its applications , 1994 .
[49] David Card. Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems , 2000 .
[50] J. Heckman,et al. The Economics and Econometrics of Active Labor Market Programs , 1999 .
[51] De-Min Wu,et al. Alternative Tests of Independence between Stochastic Regressors and Disturbances , 1973 .
[52] J J Heckman,et al. Sources of selection bias in evaluating social programs: an interpretation of conventional measures and evidence on the effectiveness of matching as a program evaluation method. , 1996, Proceedings of the National Academy of Sciences of the United States of America.
[53] H. Theil. Principles of econometrics , 1971 .
[54] S. Yitzhaki. On Using Linear Regressions in Welfare Economics , 1996 .
[55] James J. Heckman,et al. Alternative methods for evaluating the impact of interventions: An overview , 1985 .
[56] J. Heckman. Sample selection bias as a specification error , 1979 .
[57] J. Heckman. The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models , 1976 .
[58] J. Heckman. Shadow prices, market wages, and labor supply , 1974 .
[59] J. Heckman,et al. Removing the Veil of Ignorance in Assessing the Distributional Impacts of Social Policies , 2002, SSRN Electronic Journal.
[60] Lung-fei Lee. Generalized Econometric Models with Selectivity , 1983 .
[61] A Note on Additive Separability and Latent Index Models of Binary Choice: Representation Results , 2006 .
[62] Robert A. Moffitt,et al. The Estimation of Wage Gains and Welfare Gains in Self-selection , 1987 .
[63] R. Quandt. A New Approach to Estimating Switching Regressions , 1972 .
[64] W. Newey,et al. Convergence rates and asymptotic normality for series estimators , 1997 .
[65] T. Bresnahan. Competition and Collusion in the American Automobile Industry: The 1955 Price War , 1987 .
[66] James L. Powell,et al. Estimation of semiparametric models , 1994 .
[67] T. Haavelmo. The Statistical Implications of a System of Simultaneous Equations , 1943 .
[68] E. Vytlacil. Independence, Monotonicity, and Latent Index Models: An Equivalence Result , 2002 .