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Joshua D. Angrist - One of the best experts on this subject based on the ideXlab platform.
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Instrumental Variables methods in experimental criminological research what why and how
Journal of Experimental Criminology, 2006Co-Authors: Joshua D. AngristAbstract:Quantitative criminology focuses on straightforward causal questions that are ideally addressed with randomized experiments. In practice, however, traditional randomized trials are difficult to implement in the untidy world of criminal justice. Even when randomized trials are implemented, not everyone is treated as intended and some control subjects may obtain experimental services. Treatments may also be more complicated than a simple yes/no coding can capture. This paper argues that the Instrumental Variables methods (IV) used by economists to solve omitted Variables bias problems in observational studies also solve the major statistical problems that arise in imperfect criminological experiments. In general, IV methods estimate the causal effect of treatment on subjects that are induced to comply with a treatment by virtue of the random assignment of intended treatment. The use of IV in criminology is illustrated through a re-analysis of the Minneapolis Domestic Violence Experiment.
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Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments
Journal of Economic Perspectives, 2001Co-Authors: Joshua D. Angrist, Alan B. KruegerAbstract:Instrumental Variables was first used in the 1920s to estimate supply and demand elasticities and later to correct for measurement error in single equation models. Recently, Instrumental Variables have been widely used to reduce bias from omitted Variables in estimates of causal relationships. Intuitively, Instrumental Variables methods use only a portion of the variability in key Variables to estimate the relationships of interest; if the instruments are valid, that portion is unrelated to the omitted Variables. We discuss the mechanics of Instrumental Variables and the qualities that make for a good instrument, devoting particular attention to instruments derived from “natural experiments.”
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Instrumental Variables and the search for identification from supply and demand to natural experiments
Journal of Economic Perspectives, 2001Co-Authors: Joshua D. Angrist, Alan B. KruegerAbstract:The method of Instrumental Variables was first used in the 1920s to estimate supply and demand elasticities, and later used to correct for measurement error in single-equation models. Recently, Instrumental Variables have been widely used to reduce bias from omitted Variables in estimates of causal relationships such as the effect of schooling on earnings. Intuitively, Instrumental Variables methods use only a portion of the variability in key Variables to estimate the relationships of interest; if the instruments are valid, that portion is unrelated to the omitted Variables. We discuss the mechanics of Instrumental Variables, and the qualities that make for a good instrument, devoting particular attention to instruments that are derived from 'natural experiments.' A key feature of the natural experiments approach is the transparency and refutability of identifying assumptions. We also discuss the use of Instrumental Variables in randomized experiments.
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the interpretation of Instrumental Variables estimators in simultaneous equations models with an application to the demand for fish
The Review of Economic Studies, 2000Co-Authors: Joshua D. Angrist, Kathryn Graddy, Guido W. ImbensAbstract:In markets where prices are determined by the intersection of supply and demand curves, standard identification results require the presence of instruments that shift one curve but not the other. These results are typically presented in the context of linear models with fixed coefficients and additive residuals. The first contribution of this paper is an investigation of the consequences of relaxing both the linearity and the additivity assumption for the interpretation of linear Instrumental Variables estimators. Without these assumptions, the standard linear Instrumental Variables estimator identifies a weighted average of the derivative of the behavioural relationship of interest. A second contribution is the formulation of critical identifying assumptions in terms of demand and supply at different prices and instruments, rather than in terms of functional-form specific residuals. Our approach to the simultaneous equations problem and the average-derivative interpretation of Instrumental Variables estimates is illustrated by estimating the demand for fresh whiting at the Fulton fish market. Strong and credible instruments for identification of this demand function are available in the form of weather conditions at sea.
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jackknife Instrumental Variables estimation
Journal of Applied Econometrics, 1999Co-Authors: Joshua D. Angrist, Guido W. Imbens, Alan B. KruegerAbstract:Two-stage-least-squares (2SLS) estimates are biased towards OLS estimates. This bias grows with the degree of over-identification and can generate highly misleading results. In this paper we propose two simple alternatives to 2SLS and limited-information-maximum-likelihood (LIML) estimators for models with more instruments than endogenous regressors. These estimators can be interpreted as Instrumental Variables procedures using an instrument that is independent of disturbances even in finite samples. Independence is achieved by using a `leave-one-out' jackknife-type fitted value in place of the usual first-stage equation. The new estimators are first-order equivalent to 2SLS but with finite-sample properties superior to those of 2SLS and similar to LIML when there are many instruments. Moreover, the jackknife estimators appear to be less sensitive than LIML to deviations from the linear reduced form used in classical simultaneous equations models.
Alan B. Krueger - One of the best experts on this subject based on the ideXlab platform.
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Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments
Journal of Economic Perspectives, 2001Co-Authors: Joshua D. Angrist, Alan B. KruegerAbstract:Instrumental Variables was first used in the 1920s to estimate supply and demand elasticities and later to correct for measurement error in single equation models. Recently, Instrumental Variables have been widely used to reduce bias from omitted Variables in estimates of causal relationships. Intuitively, Instrumental Variables methods use only a portion of the variability in key Variables to estimate the relationships of interest; if the instruments are valid, that portion is unrelated to the omitted Variables. We discuss the mechanics of Instrumental Variables and the qualities that make for a good instrument, devoting particular attention to instruments derived from “natural experiments.”
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Instrumental Variables and the search for identification from supply and demand to natural experiments
Journal of Economic Perspectives, 2001Co-Authors: Joshua D. Angrist, Alan B. KruegerAbstract:The method of Instrumental Variables was first used in the 1920s to estimate supply and demand elasticities, and later used to correct for measurement error in single-equation models. Recently, Instrumental Variables have been widely used to reduce bias from omitted Variables in estimates of causal relationships such as the effect of schooling on earnings. Intuitively, Instrumental Variables methods use only a portion of the variability in key Variables to estimate the relationships of interest; if the instruments are valid, that portion is unrelated to the omitted Variables. We discuss the mechanics of Instrumental Variables, and the qualities that make for a good instrument, devoting particular attention to instruments that are derived from 'natural experiments.' A key feature of the natural experiments approach is the transparency and refutability of identifying assumptions. We also discuss the use of Instrumental Variables in randomized experiments.
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jackknife Instrumental Variables estimation
Journal of Applied Econometrics, 1999Co-Authors: Joshua D. Angrist, Guido W. Imbens, Alan B. KruegerAbstract:Two-stage-least-squares (2SLS) estimates are biased towards OLS estimates. This bias grows with the degree of over-identification and can generate highly misleading results. In this paper we propose two simple alternatives to 2SLS and limited-information-maximum-likelihood (LIML) estimators for models with more instruments than endogenous regressors. These estimators can be interpreted as Instrumental Variables procedures using an instrument that is independent of disturbances even in finite samples. Independence is achieved by using a `leave-one-out' jackknife-type fitted value in place of the usual first-stage equation. The new estimators are first-order equivalent to 2SLS but with finite-sample properties superior to those of 2SLS and similar to LIML when there are many instruments. Moreover, the jackknife estimators appear to be less sensitive than LIML to deviations from the linear reduced form used in classical simultaneous equations models.
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Split Sample Instrumental Variables
1995Co-Authors: Joshua D. Angrist, Alan B. KruegerAbstract:Instrumental Variables (IV) estimates tend to be biased in the same direction as Ordinary Least Squares (OLS) in finite samples if the instruments are weak. To address this problem we propose a new IV estimator which we call Split Sample Instrumental Variables (SSIV). SSIV works as follows: we randomly split the sample in half, and use one half of the sample to estimate parameters of the first-stage equation. We then use these estimated first-stage parameters to construct fitted values and second-stage parameter estimates using data from the other half sample. SSIV is biased toward zero, rather than toward the plim of the OLS estimate. However, an unbiased estimate of the attenuation bias of SSIV can be calculated. We us this estimate of the attenutation bias to derive an estimator that is asymptotically unbiased as the number of instruments tends to infinity, holding the number of observations per instrument fixed. We label this new estimator Unbiased Split Sample Instrumental Variables (USSIV). We apply SSIV and USSIV to the data used by Angrist and Krueger (1991) to estimate the payoff to education.
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split sample Instrumental Variables estimates of the return to schooling
Journal of Business & Economic Statistics, 1995Co-Authors: Joshua D. Angrist, Alan B. KruegerAbstract:This article reevaluates recent Instrumental Variables (IV) estimates of the returns to schooling in light of the fact that two-stage least squares is biased in the same direction as ordinary least squares (OLS) even in very large samples. We propose a split-sample Instrumental Variables (SSIV) estimator that is not biased toward OLS. SSIV uses one-half of a sample to estimate parameters of the first-stage equation. Estimated first-stage parameters are then used to construct fitted values and second-stage parameter estimates in the other half sample. SSIV is biased toward 0, but this bias can be corrected. The splt-sample estimators confirm and reinforce some previous findings on the returns to schooling but fail to confirm others.
Whitney K Newey - One of the best experts on this subject based on the ideXlab platform.
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nonparametric Instrumental Variables estimation
The American Economic Review, 2013Co-Authors: Whitney K NeweyAbstract:In many economic models, objects of interest are functions which satisfy conditional moment restrictions. Economics does not restrict the functional form of these models, motivating nonparametric methods. In this paper we review identification results and describe a simple nonparametric Instrumental Variables (NPIV) estimator. We also consider a simple method of inference. In addition we show how the ability to uncover nonlinearities with conditional moment restrictions is related to the strength of the instruments. We point to applications where important nonlinearities can be found with NPIV and applications where they cannot.
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estimation with many Instrumental Variables
Journal of Business & Economic Statistics, 2006Co-Authors: Christian Hansen, Jerry A Hausman, Whitney K NeweyAbstract:Using many valid Instrumental Variables has the potential to improve efficiency but makes the usual inference procedures inaccurate. We give corrected standard errors, an extension of Bekker to nonnormal disturbances, that adjust for many instruments. We find that this adjustment is useful in empirical work, simulations, and in the asymptotic theory. Use of the corrected standard errors in t-ratios leads to an asymptotic approximation order that is the same when the number of Instrumental Variables grows as when the number of instruments is fixed. We also give a version of the Kleibergen weak instrument statistic that is robust to many instruments.
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efficient Instrumental Variables estimation of nonlinear models
Econometrica, 1990Co-Authors: Whitney K NeweyAbstract:This paper considers asymptotically efficient Instrumental Variables estimation of nonlinear models in an i.i.d. environment. The optimal instruments are estimated by nonparametric methods, either nearest neighbor or series regression. Ways of choosing the degree of approximation of the nonparametric instruments are discussed. Asymptotic efficiency is shown. The finite sample properties of the estimators are examined in a small sampling study of an endogenous dummy variable model. Copyright 1990 by The Econometric Society.
Joel L Horowitz - One of the best experts on this subject based on the ideXlab platform.
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applied nonparametric Instrumental Variables estimation
Econometrica, 2011Co-Authors: Joel L HorowitzAbstract:Instrumental Variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory Variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and Instrumental Variables are used identify and estimate these parameters. However, linear and other finite-dimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an Instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric Instrumental Variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators.
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asymptotic normality of a nonparametric Instrumental Variables estimator
International Economic Review, 2007Co-Authors: Joel L HorowitzAbstract:This article gives conditions under which the nonparametric Instrumental Variables estimator of Hall and Horowitz ("Annals of Statistics" 33 (December 2005), 2904-2929) is asymptotically normally distributed. With sufficiently large samples, the asymptotic normality result can be used to form confidence intervals for the unknown function that is estimated by the Hall-Horowitz procedure. The article reports the results of a Monte Carlo investigation of the finite-sample coverage probabilities of the confidence intervals. Copyright 2007 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.
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nonparametric methods for inference in the presence of Instrumental Variables
Annals of Statistics, 2003Co-Authors: Peter Hall, Joel L HorowitzAbstract:We suggest two nonparametric approaches, based on kernel methods and orthogonal series, respectively, to estimating regression functions in the presence of Instrumental Variables. For the first time in this class of problems we derive optimal convergence rates, and show that they are attained by particular estimators. In the presence of Instrumental Variables the relation that identifies the regression function also defines an ill-posed inverse problem, the "difficulty" of which depends on eigenvalues of a certain integral operator which is determined by the joint density of endogenous and Instrumental Variables. We delineate the role played by problem difficulty in determining both the optimal convergence rate and the appropriate choice of smoothing parameter.
James H Stock - One of the best experts on this subject based on the ideXlab platform.
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weak instruments in Instrumental Variables regression theory and practice
Annual Review of Economics, 2019Co-Authors: Isaiah Andrews, James H StockAbstract:When instruments are weakly correlated with endogenous regressors, conventional methods for Instrumental Variables (IV) estimation and inference become unreliable. A large literature in econometric...
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optimal two sided invariant similar tests for Instrumental Variables regression
Econometrica, 2006Co-Authors: Donald W K Andrews, Marcelo J Moreira, James H StockAbstract:This paper considers tests of the parameter on an endogenous variable in an Instrumental Variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We consider tests that are similar and satisfy a natural rotational invariance condition. We determine a two-sided power envelope for invariant similar tests. This allows us to assess and compare the power properties of tests such as the conditional likelihood ratio (CLR), the Lagrange multiplier, and the Anderson-Rubin tests. We find that the CLR test is quite close to being uniformly most powerful invariant among a class of two-sided tests. Copyright The Econometric Society 2006.
