Download or read book Endogeneity and Measurement Error in Nonparametric and Semiparametric Models written by Suyong Song and published by . This book was released on 2010 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has long been an area of interest to consider a consistent estimation of nonlinear models with measurement error or endogeneity in the explanatory variables. Contrast to linear parametric models, both topics in nonlinear models are difficult to correct for. As a result, many of studies have addressed only one of them in nonlinear models, although controlling for only one mostly fails to identify economically meaningful structural parameters. Thus, this dissertation presents solutions to simultaneously control for both endogeneity and measurement error in general nonlinear regression models. Chapter one of this dissertation studies the identification and estimation of covariate-conditioned average marginal effects of endogenous regressors in nonseparable models when the regressors are mismeasured. Endogeneity is controlled for by making use of covariates as conditioning instruments; this ensures independence between the endogenous causes and other unobservable drivers of the dependent variable. Moreover, distributions of the underlying true causes from their error-laden measurements are recovered. Specifically, it is shown that two error-laden measurements of the unobserved true causes are sufficient to identify objects of interest and to deliver consistent estimators. Chapter two develops semiparametric estimation of models defined by conditional moment restrictions, where the unknown functions depend on endogenous variables which are contaminated by nonclassical measurement errors. A two-stage estimation procedure is proposed to recover the true conditional density of endogenous variables given conditioning variables masked by measurement errors, and to rectify the difficulty associated with endogeneity of the unknown functions. Chapter three investigates empirical importance of endogeneity and measurement error in economic examples. The proposed methods in chapter one and two are applied to topics of interest, the impact of family income on children's achievement and the estimation of Engel curves, respectively. The first application finds that the effects of family income on both math and reading scores from the proposed estimator are positive and that the magnitudes of the income effects are substantially larger than previously recognized. From the second application, findings indicate that correcting for both endogeneity and measurement error obtains significantly different shapes of Engel curves, compared to the method which ignores measurement error on total expenditure.

Download or read book Nonparametric and Semiparametric Methods in Econometrics and Statistics written by William A. Barnett and published by Cambridge University Press. This book was released on 1991-06-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.

Download or read book Methods for Nonparametric and Semiparametric Regressions with Endogeneity written by Xiaohong Chen and published by . This book was released on 2016 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper reviews recent advances in estimation and inference for nonparametric and semiparametric models with endogeneity. It first describes methods of sieves and penalization for estimating unknown functions identified via conditional moment restrictions. Examples include nonparametric instrumental variables regression (NPIV), nonparametric quantile IV regression and many more semi-nonparametric structural models. Asymptotic properties of the sieve estimators and the sieve Wald, quasi-likelihood ratio (QLR) hypothesis tests of functionals with nonparametric endogeneity are presented. For sieve NPIV estimation, the rate-adaptive data-driven choices of sieve regularization parameters and the sieve score bootstrap uniform confidence bands are described. Finally, simple sieve variance estimation and over-identification test for semiparametric two-step GMM are reviewed. Monte Carlo examples are included.

Download or read book Nonparametric Econometrics written by Qi Li and published by Princeton University Press. This book was released on 2023-07-18 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.

Download or read book Semiparametric and Nonparametric Econometrics written by Aman Ullah and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last three decades much research in empirical and theoretical economics has been carried on under various assumptions. For example a parametric functional form of the regression model, the heteroskedasticity, and the autocorrelation is always as sumed, usually linear. Also, the errors are assumed to follow certain parametric distri butions, often normal. A disadvantage of parametric econometrics based on these assumptions is that it may not be robust to the slight data inconsistency with the particular parametric specification. Indeed any misspecification in the functional form may lead to erroneous conclusions. In view of these problems, recently there has been significant interest in 'the semiparametric/nonparametric approaches to econometrics. The semiparametric approach considers econometric models where one component has a parametric and the other, which is unknown, a nonparametric specification (Manski 1984 and Horowitz and Neumann 1987, among others). The purely non parametric approach, on the other hand, does not specify any component of the model a priori. The main ingredient of this approach is the data based estimation of the unknown joint density due to Rosenblatt (1956). Since then, especially in the last decade, a vast amount of literature has appeared on nonparametric estimation in statistics journals. However, this literature is mostly highly technical and this may partly be the reason why very little is known about it in econometrics, although see Bierens (1987) and Ullah (1988).

Download or read book The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics written by Jeffrey Racine and published by Oxford University Press. This book was released on 2014-04 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.

Download or read book Endogenous Econometric Models and Multi Stage Estimation in High Dimensional Settings written by Ying Zhu and published by . This book was released on 2015 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometric models based on observational data are often endogenous due to measurement error, autocorrelated errors, simultaneity and omitted variables, non-random sampling, self-selection, etc. Parameter estimates of these models without corrective measures may be inconsistent. The potential high-dimensional feature of these models (where the dimension of the parameters of interests is comparable to or even larger than the sample size) further complicates the statistical estimation and inference. My dissertation studies two different types of high-dimensional endogenous econometrics problems in depth and develops statistical tools together with their theoretical guarantees. The first essay in this dissertation explores the validity of the two-stage regularized least squares estimation procedure for sparse linear models in high-dimensional settings with possibly many endogenous regressors. The second essay is focused on the semiparametric sample selection model in high-dimensional settings under a weak nonparametric restriction on the form of the selection correction, for which a multi-stage projection-based regularized procedure is proposed. The number of regressors in the main equation, p, and the number of regressors in the first-stage equation, d, can grow with and exceed the sample size n in the respective models. The analysis considers the sparsity case where the number of non-zero components in the vectors of coefficients is bounded above by some integer which is allowed to grow with n but slowly compared to n, or the vectors of coefficients can be approximated by exactly sparse vectors. Simulations are conducted to gain insight on the small-sample performance of these high-dimensional multi-stage estimators. The proposed estimators in the second essay are also applied to study the pricing decisions of the gasoline retailers in the Greater Saint Louis area. The main theoretical results of both essays are finite-sample bounds from which sufficient scaling conditions on the sample size for estimation consistency and variable selection consistency (i.e., the multi-stage high-dimensional estimation procedures correctly select the non-zero coefficients in the main equation with high probability) are established. A technical issue regarding the so-called "restricted eigenvalue (RE) condition" for estimation consistency and the "mutual incoherence (MI) condition" for selection consistency arises in these multi-stage estimation procedures from allowing the number of regressors in the main equation to exceed n and this paper provides analysis to verify these RE and MI conditions. In particular, for the semiparametric sample selection model, these verifications also provide a finite-sample guarantee of the population identification condition required by the semiparametric sample selection models. In the second essay, statistical efficiency of the proposed estimators is studied via lower bounds on minimax risks and the result shows that, for a family of models with exactly sparse structure on the coefficient vector in the main equation, one of the proposed estimators attains the smallest estimation error up to the (n, d, p)-scaling among a class of procedures in worst-case scenarios. Inference procedures for the coefficients of the main equation, one based on a pivotal Dantzig selector to construct non-asymptotic confidence sets and one based on a post-selection strategy (when perfect or near-perfect selection of the high-dimensional coefficients is achieved), are discussed. Other theoretical contributions of this essay include establishing the non-asymptotic counterpart of the familiar asymptotic "oracle" type of results from previous literature: the estimator of the coefficients in the main equation behaves as if the unknown nonparametric component were known, provided the nonparametric component is sufficiently smooth.

Download or read book Nonparametric Econometric Methods and Application written by Thanasis Stengos and published by MDPI. This book was released on 2019-05-20 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present Special Issue collects a number of new contributions both at the theoretical level and in terms of applications in the areas of nonparametric and semiparametric econometric methods. In particular, this collection of papers that cover areas such as developments in local smoothing techniques, splines, series estimators, and wavelets will add to the existing rich literature on these subjects and enhance our ability to use data to test economic hypotheses in a variety of fields, such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth, to name a few.

Download or read book Identification and Estimation of Single Index Models with Measurement Error and Endogeneity written by Yingyao Hu and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric and Nonparametric Econometrics written by Aman Ullah and published by Physica. This book was released on 1989-01-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last three decades much research in empirical and theoretical economics has been carried on under various assumptions. For example a parametric functional form of the regression model, the heteroskedasticity, and the autocorrelation is always as sumed, usually linear. Also, the errors are assumed to follow certain parametric distri butions, often normal. A disadvantage of parametric econometrics based on these assumptions is that it may not be robust to the slight data inconsistency with the particular parametric specification. Indeed any misspecification in the functional form may lead to erroneous conclusions. In view of these problems, recently there has been significant interest in 'the semiparametric/nonparametric approaches to econometrics. The semiparametric approach considers econometric models where one component has a parametric and the other, which is unknown, a nonparametric specification (Manski 1984 and Horowitz and Neumann 1987, among others). The purely non parametric approach, on the other hand, does not specify any component of the model a priori. The main ingredient of this approach is the data based estimation of the unknown joint density due to Rosenblatt (1956). Since then, especially in the last decade, a vast amount of literature has appeared on nonparametric estimation in statistics journals. However, this literature is mostly highly technical and this may partly be the reason why very little is known about it in econometrics, although see Bierens (1987) and Ullah (1988).

Download or read book Dealing with Endogeneity in Regression Models with Dynamic Coefficients written by Chang-Jin Kim and published by Now Publishers Inc. This book was released on 2010 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this monograph is to present a unified econometric framework for dealing with the issues of endogeneity in Markov-switching models and time-varying parameter models, as developed by Kim (2004, 2006, 2009), Kim and Nelson (2006), Kim et al. (2008), and Kim and Kim (2009). While Cogley and Sargent (2002), Primiceri (2005), Sims and Zha (2006), and Sims et al. (2008) consider estimation of simultaneous equations models with stochastic coefficients as a system, we deal with the LIML (limited information maximum likelihood) estimation of a single equation of interest out of a simultaneous equations model. Our main focus is on the two-step estimation procedures based on the control function approach, and we show how the problem of generated regressors can be addressed in second-step regressions.

Download or read book Semiparametric Structural Models of Binary Response written by Andrew Chesher and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper studies the partial identifying power of structural single equation threshold crossing models for binary responses when explanatory variables may be endogenous. The paper derives the sharp identified set of threshold functions for the case in which explanatory variables are discrete and provides a constructive proof of sharpness. There is special attention to a widely employed semiparametric shape restriction which requires the threshold crossing function to be a monotone function of a linear index involving the observable explanatory variables. It is shown that the restriction brings great computational benefits, allowing direct calculation of the identified set of index coefficients without calculating the nonparametrically specified threshold function. With the restriction in place the methods of the paper can be applied to produce identified sets in a class of binary response models with mis-measured explanatory variables. -- Binary Response ; Endogeneity ; Incomplete models ; Index Restrictions ; Instrumental variables ; Measurement Error Models ; Partial Identification ; Probit Models ; Shape Restrictions ; Threshold Crossing Models

Download or read book A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models written by Yang Li and published by . This book was released on 2019 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: Marketing variables that are included in consumer discrete choice models are often endogenous. Extant treatments using likelihood-based estimators impose parametric distributional assumptions, such as normality, on the source of endogeneity. These assumptions are restrictive as misspecified distributions have an impact on parameter estimates and associated elasticities. The normality assumption for endogeneity can be inconsistent with some marginal cost specifications given a price setting process, although being consistent with other specifications. In this paper we propose a heterogeneous Bayesian semiparametric approach for modeling choice endogeneity which offers a flexible and robust alternative to parametric methods. Specifically, we construct centered Dirichlet process mixtures (CDPM) to allow uncertainty over the distribution of endogeneity errors. In a similar vein, we also model consumer preference heterogeneity non-parametrically via a CDPM. Results on simulated data show that incorrect distributional assumptions can lead to poor recovery of model parameters and price elasticities, whereas, the proposed semiparametric model is able to robustly recover the true parameters in an efficient fashion. In addition, the CDPM offers the benefits of automatically inferring the number of mixture components that are appropriate for a given data set and is able to reconstruct the shape of the underlying distributions for endogeneity and heterogeneity errors. We apply our approach to two scanner panel data sets. Model comparison statistics indicate the superiority of the semiparametric specification and the results show that parameter and elasticity estimates are sensitive to the choice of distributional forms. Moreover, the CDPM specification yields evidence of multimodality, skewness, and outlying observations in these real data sets.

Download or read book Measurement Error and Latent Variables in Econometrics written by T. Wansbeek and published by North Holland. This book was released on 2000-12-08 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book first discusses in depth various aspects of the well-known inconsistency that arises when explanatory variables in a linear regression model are measured with error. Despite this inconsistency, the region where the true regression coeffecients lies can sometimes be characterized in a useful way, especially when bounds are known on the measurement error variance but also when such information is absent. Wage discrimination with imperfect productivity measurement is discussed as an important special case. Next, it is shown that the inconsistency is not accidental but fundamental. Due to an identification problem, no consistent estimators may exist at all. Additional information is desirable. This information can be of various types. One type is exact prior knowledge about functions of the parameters. This leads to the CALS estimator. Another major type is in the form of instrumental variables. Many aspects of this are discussed, including heteroskedasticity, combination of data from different sources, construction of instruments from the available data, and the LIML estimator, which is especially relevant when the instruments are weak. The scope is then widened to an embedding of the regression equation with measurement error in a multiple equations setting, leading to the exploratory factor analysis (EFA) model. This marks the step from measurement error to latent variables. Estimation of the EFA model leads to an eigenvalue problem. A variety of models is reviewed that involve eignevalue problems as their common characteristic. EFA is extended to confirmatory factor analysis (CFA) by including restrictions on the parameters of the factor analysis model, and next by relating the factors to background variables. These models are all structural equation models (SEMs), a very general and important class of models, with the LISREL model as its best-known representation, encompassing almost all linear equation systems with latent variables. Estimation of SEMs can be viewed as an application of the generalized method of moments (GMM). GMM in general and for SEM in particular is discussed at great length, including the generality of GMM, optimal weighting, conditional moments, continuous updating, simulation estimation, the link with the method of maximum likelihood, and in particular testing and model evaluation for GMM. The discussion concludes with nonlinear models. The emphasis is on polynomial models and models that are nonlinear due to a filter on the dependent variables, like discrete choice models or models with ordered categorical variables.

Download or read book Nonparametric Econometric Methods written by Qi Li and published by Emerald Group Publishing. This book was released on 2009-12-04 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.

Download or read book Endogeneity in Semiparametric Threshold Regression written by Andros Kourtellos and published by . This book was released on 2017 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we investigate semiparametric threshold regression models with endogenous threshold variables based on a nonparametric control function approach. Using a series approximation we propose a two-step estimation method for the threshold parameter. For the regression coefficients, we consider least-squares estimation in the case of exogenous regressors and two-stage least-squares estimation in the case of endogenous regressors. We show that our estimators are consistent and derive their asymptotic distribution for weakly dependent data. Furthermore, we propose a test for the endogeneity of the threshold variable, which is valid regardless of whether the threshold effect is zero or not. Finally, we assess the performance of our methods using a Monte Carlo simulation.

Download or read book Nonparametric and Semiparametric Models written by Wolfgang Karl Härdle and published by Springer Science & Business Media. This book was released on 2012-08-27 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.