Download or read book Revisiting Instrumental Variables and the Classic Control Function Approach with Implications for Parametric and Non Parametric Regressions written by Kyoo il Kim and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We show that the well-known numerical equivalence between two-stage least squares (2SLS) and the classic control function (CF) estimator raises an interesting and unrecognized puzzle. The classic CF approach maintains that the regression error is mean independent of the instruments conditional on the CF control, which is not required by 2SLS, and could easily be violated. We show that the classic CF estimator can be modified to allow the mean of the error to depend in a general way on the instruments and control by adding the unconditional moment restrictions maintained by 2SLS. In this case 2SLS and our generalized CF estimator are no longer numerically equivalent, although asymptotically both converge to the true value. We then show that our generalized CF estimator is consistent in parametric or non-parametric settings with endogenous regressors and additive errors. For example, our estimator is consistent when the conditional mean of the error depends on the instruments while the nonparametric estimator of Newey, Powell, and Vella (1999) based on the classic CF restriction is not. Our new approach is also not subject to the ill-posed inverse problem that affects the non-parametric estimator of Newey and Powell (2003). Our estimator is easy to implement in standard programming packages - it is a multi-step least squares estimator - and our monte carlos show that our new estimator performs well while the classical CF estimator and the non-parametric analog of Newey, Powell, and Vella (1999) can be biased in non-linear settings when the conditional mean of the error depends on the instruments.
Download or read book Revisiting Instrumental Variables and the Clasc Control Function Approach with Implications for Parametric and Non parametric Regressions written by Kyoo il Kim and published by . This book was released on 2011 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Revisiting Instrumental Variables and the Classic Control Function Approach with Implications for Parametric and Non Parametric Regressions written by and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A New Control Function Approach for Non Parametric Regressions with Endogenous Variables written by Kyoo il Kim and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: When the endogenous variable enters the structural equation non-parametrically the linear Instrumental Variable (IV) estimator is no longer consistent. Non-parametric IV (NPIV) can be used but it requires one to impose restrictions during estimation to make the problem well-posed. The non-parametric control function estimator of Newey, Powell, and Vella (1999) (NPV-CF) is an alternative approach that uses the residuals from the conditional mean decomposition of the endogenous variable as controls in the structural equation. While computationally simple identification relies upon independence between the instruments and the expected value of the structural error conditional on the controls, which is hard to motivate in many economic settings including estimation of returns to education, production functions, and demand or supply elasticities. We develop an estimator for non-linear and non-parametric regressions that maintains the simplicity of the NPV-CF estimator but allows the conditional expectation of the structural error to depend on both the control variables and the instruments. Our approach combines the conditional moment restrictions (CMRs) from NPIV with the controls from NPV-CF setting. We show that the CMRs place shape restrictions on the conditional expectation of the error given instruments and controls that are sufficient for identification. When sieves are used to approximate both the structural function and the control function our estimator reduces to a series of Least Squares regressions. Our monte carlos are based on the economic settings suggested above and illustrate that our new estimator performs well when the NPV-CF estimator is biased. Our empirical example replicates NPV-CF and we reject the maintained assumption of the independence of the instruments and the expected value of the structural error conditional on the controls in their setting.
Download or read book Instrumental Variables written by Roger John Bowden and published by Cambridge University Press. This book was released on 1990-01-26 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will be useful for advanced undergraduates and graduates, and be a source of reference for researchers in econometrics and statistics.
Download or read book Inference of Limited Dependent Variables Models written by Jiro Hodoshima and published by . This book was released on 1984 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Instrumental Variables Selection Models and Tight Bounds on the Average Treatment Effect written by James Joseph Heckman and published by . This book was released on 2000 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper exposits and relates two distinct approaches to bounding the average treatment effect. One approach, based on instrumental variables, is due to Manski (1990, 1994), who derives tight bounds on the average treatment effect under a mean independence form of the instrumental variables (IV) condition. The second approach, based on latent index models, is due to Heckman and Vytlacil (1999, 2000a), who derive bounds on the average treatment effect that exploit the assumption of a nonparametric selection model with an exclusion restriction. Their conditions imply the instrumental variable condition studied by Manski, so that their conditions are stronger than the Manski conditions. In this paper, we study the relationship between the two sets of bounds implied by these alternative conditions. We show that: (1) the Heckman and Vytlacil bounds are tight given their assumption of a nonparametric selection model; (2) the Manski bounds simplify to the Heckman and Vytlacil bounds under the nonparametric selection model assumption.
Download or read book Instrumental Variables Regressions with Honestly Uncertain Exclusion Restrictions written by Aart Kraay and published by World Bank Publications. This book was released on 2008 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The validity of instrumental variables (IV) regression models depends crucially on fundamentally untestable exclusion restrictions. Typically exclusion restrictions are assumed to hold exactly in the relevant population, yet in many empirical applications there are reasonable prior grounds to doubt their literal truth. In this paper I show how to incorporate prior uncertainty about the validity of the exclusion restriction into linear IV models, and explore the consequences for inference. In particular I provide a mapping from prior uncertainty about the exclusion restriction into increased uncertainty about parameters of interest. Moderate prior uncertainty about exclusion restrictions can lead to a substantial loss of precision in estimates of structural parameters. This loss of precision is relatively more important in situations where IV estimates appear to be more precise, for example in larger samples or with stronger instruments. The author illustrates these points using several prominent recent empirical papers that use linear IV models.
Download or read book Instrumental Variables Regressions Involving Seasonal Data written by David E. A. Giles and published by . This book was released on 1983 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Control written by Shankar P. Bhattacharyya and published by Prentice Hall. This book was released on 1995 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crucial in the analysis and design of control systems, this book presents a unified approach to robust stability theory, including both linear and nonlinear systems, and provides a self-contained and complete account of the available results in the field of robust control under parametric uncertainty.
Download or read book Instrumental Variables written by James J. Heckman and published by . This book was released on 1995 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers the use of instrumental variables to estimate the mean effect of treatment on the treated. It reviews previous work on this topic by Heckman and Robb (1985, 1986) and demonstrates that (a) unless the effect of treatment is the same for everyone (conditional on observables), or (b) treatment effects are variable across persons but the person-specific component of the variability not forecastable by observables does not determine participation in the program, widely-used instrumental variable methods produce inconsistent estimators of the parameter of interest. Neither assumption is very palatable. The first assumes a homogeneity that is implausible. The second assumes either very rich data available to the econometrician or that the persons being studied either do not have better information than the econometrician or that they do not use it. Instrumental variable methods do not provide a general solution to the evaluation problem.
Download or read book Semiparametric Estimation of Instrumental Variable Models for Causal Effects written by Alberto Abadie and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article introduces a new class of instrumental variable (IV) estimators of causal treatment effects for linear and nonlinear models with covariates. The rationale for focusing on nonlinear models is to improve the approximation to the causal response function of interest. For example, if the dependent variable is binary or limited, or if the effect of the treatment varies with covariates, a nonlinear model is likely to be appropriate. However, identification is not attained through functional form restrictions. This paper shows how to estimate a well-defined approximation to a nonlinear causal response function of unknown functional form using simple parametric models. As an important special case, I introduce a linear model that provides the best linear approximation to an underlying causal relation. It is shown that Two Stage Least Squares (2SLS) does not always have this property and some possible interpretations of 2SLS coefficients are brie y studied. The ideas and estimators in this paper are illustrated using instrumental variables to estimate the effects of 401(k) retirement programs on savings.
Download or read book Variable Selection by Regularization Methods for Generalized Mixed Models written by Andreas Groll and published by Cuvillier Verlag. This book was released on 2011-12-13 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: A regression analysis describes the dependency of random variables in the form of a functional relationship. One distinguishes between the dependent response variable and one or more independent influence variables. There is a variety of model classes and inference methods available, ranging from the conventional linear regression model up to recent non- and semiparametric regression models. The so-called generalized regression models form a methodically consistent framework incorporating many regression approaches with response variables that are not necessarily normally distributed, including the conventional linear regression model based on the normal distribution assumption as a special case. When repeated measurements are modeled in addition to fixed effects also random effects or coefficients can be included. Such models are known as Random Effects Models or Mixed Models. As a consequence, regression procedures are applicable extremely versatile and consider very different problems. In this dissertation regularization techniques for generalized mixed models are developed that are able to perform variable selection. These techniques are especially appropriate when many potential influence variables are present and existing approaches tend to fail. First of all a componentwise boosting technique for generalized linear mixed models is presented which is based on the likelihood function and works by iteratively fitting the residuals using weak learners. The complexity of the resulting estimator is determined by information criteria. For the estimation of variance components two approaches are considered, an estimator resulting from maximizing the profile likelihood, and an estimator which can be calculated using an approximative EM-algorithm. Then the boosting concept is extended to mixed models with ordinal response variables. Two different types of ordered models are considered, the threshold model, also known as cumulative model, and the sequential model. Both are based on the assumption that the observed response variable results from a categorized version of a latent metric variable. In the further course of the thesis the boosting approach is extended to additive predictors. The unknown functions to be estimated are expanded in B-spline basis functions, whose smoothness is controlled by penalty terms. Finally, a suitable L1-regularization technique for generalized linear models is presented, which is based on a combination of Fisher scoring and gradient optimization. Extensive simulation studies and numerous applications illustrate the competitiveness of the methods constructed in this thesis compared to conventional approaches. For the calculation of standard errors bootstrap methods are used.
Download or read book Instrumental Variables Revisited written by Aris Spanos and published by . This book was released on 1987 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Exploring the Use of a Nonparametrically Generated Instrumental Variable in the Estimation of a Linear Parametric Equation electronic Resource written by Frank T. Denton and published by Hamilton, Ont. : Research Institute for Quantitative Studies in Economics and Population, McMaster University. This book was released on 2004 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Labor Economics written by Orley Ashenfelter and published by Elsevier. This book was released on 1999-11-18 with total page 800 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the continually evolving field of labour economics.
Download or read book Instrumental Variables Regressions with Honestly Uncertain Exclusion Restrictions written by Aart Kraay and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The validity of instrumental variables (IV) regression models depends crucially on fundamentally untestable exclusion restrictions. Typically exclusion restrictions are assumed to hold exactly in the relevant population, yet in many empirical applications there are reasonable prior grounds to doubt their literal truth. In this paper I show how to incorporate prior uncertainty about the validity of the exclusion restriction into linear IV models, and explore the consequences for inference. In particular I provide a mapping from prior uncertainty about the exclusion restriction into increased uncertainty about parameters of interest. Moderate prior uncertainty about exclusion restrictions can lead to a substantial loss of precision in estimates of structural parameters. This loss of precision is relatively more important in situations where IV estimates appear to be more precise, for example in larger samples or with stronger instruments. The author illustrates these points using several prominent recent empirical papers that use linear IV models.
