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Author: Eduardo García Echeverri
Publisher:
ISBN:
Category : Social mobility
Languages : en
Pages : 0
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"This dissertation consists of three chapters on nonparametric and semiparametric econometrics. Chapter 1 introduces the estimators used in the empirical applications of Chapter 2 and therefore should be read first. Chapter 3 is independent from the first two. The first chapter introduces a measure of intergenerational social mobility based on [phi]-divergences. The measure can be decomposed to study mobility in population subgroups of interest and can be used to describe mobility of multiple outcome variables across an arbitrary number of generations, unlike most indicators in the literature. The measure also fully controls for marginal distributions, meaning it is not affected by income growth or changes in income inequality. I propose two estimators for the measure: a non-parametric estimator and an estimator based on the mobility matrix. I provide conditions under which these estimators are n-consistent and asymptotically normal. In the second chapter, I use a specific [phi]-divergence (the Hellinger distance) to measure multidimensional social mobility in the USA and Germany. For this purpose, I use the Panel Study of Income Dynamics (PSID), the German Socio-Economic Panel (SOEP), and US administrative tax data. The measure reveals lower income and health mobility in the USA than Germany, but the opposite for educational mobility. It also shows income mobility for both countries is lowest in the tails of the parental income distribution and greatest in the centre. This inverted U-pattern is more pronounced in the USA. Most of these empirical findings for population subgroups are hidden to the existing indicators in the literature. Chapter 3 introduces a Low CPU Cost Semiparametric (LCS) estimator for linear single index models. The LCS estimator significantly reduces estimation time when compared to the standard semiparametric estimator in Ichimura (1993). It does so by more than 90% in medium sample sizes. Moreover, it makes estimation feasible in a regular PC when the sample size exceeds 10,000 observations. We provide conditions for consistency and asymptotic normality of the LCS estimator based on spline function theory. In our empirical application, we study determinants of expenditures in vocational rehabilitation (VR) programs using the RSA-911 data, containing information on more than 900,000 workers with disabilities. We find that minorities such as African Americans, Hispanic or females have lower expenditures in VR programs. On the other hand, expenditure is greater for more educated workers."--Pages viii-ix.
Author: Eduardo García Echeverri
Publisher:
ISBN:
Category : Social mobility
Languages : en
Pages : 0
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"This dissertation consists of three chapters on nonparametric and semiparametric econometrics. Chapter 1 introduces the estimators used in the empirical applications of Chapter 2 and therefore should be read first. Chapter 3 is independent from the first two. The first chapter introduces a measure of intergenerational social mobility based on [phi]-divergences. The measure can be decomposed to study mobility in population subgroups of interest and can be used to describe mobility of multiple outcome variables across an arbitrary number of generations, unlike most indicators in the literature. The measure also fully controls for marginal distributions, meaning it is not affected by income growth or changes in income inequality. I propose two estimators for the measure: a non-parametric estimator and an estimator based on the mobility matrix. I provide conditions under which these estimators are n-consistent and asymptotically normal. In the second chapter, I use a specific [phi]-divergence (the Hellinger distance) to measure multidimensional social mobility in the USA and Germany. For this purpose, I use the Panel Study of Income Dynamics (PSID), the German Socio-Economic Panel (SOEP), and US administrative tax data. The measure reveals lower income and health mobility in the USA than Germany, but the opposite for educational mobility. It also shows income mobility for both countries is lowest in the tails of the parental income distribution and greatest in the centre. This inverted U-pattern is more pronounced in the USA. Most of these empirical findings for population subgroups are hidden to the existing indicators in the literature. Chapter 3 introduces a Low CPU Cost Semiparametric (LCS) estimator for linear single index models. The LCS estimator significantly reduces estimation time when compared to the standard semiparametric estimator in Ichimura (1993). It does so by more than 90% in medium sample sizes. Moreover, it makes estimation feasible in a regular PC when the sample size exceeds 10,000 observations. We provide conditions for consistency and asymptotic normality of the LCS estimator based on spline function theory. In our empirical application, we study determinants of expenditures in vocational rehabilitation (VR) programs using the RSA-911 data, containing information on more than 900,000 workers with disabilities. We find that minorities such as African Americans, Hispanic or females have lower expenditures in VR programs. On the other hand, expenditure is greater for more educated workers."--Pages viii-ix.
Author: Hyungtaik Ahn
Publisher:
ISBN:
Category :
Languages : en
Pages : 402
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Author:
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ISBN:
Category :
Languages : en
Pages :
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This thesis contains three essays in non- and semiparametric econometrics, dealing with semiparametric estimation of binary response models with endogenous regressors, nonparametric estimation of distributional policy effects, and identification of unconditional partial effects in nonseparable models, respectively.
Author: William A. Barnett
Publisher: Cambridge University Press
ISBN: 9780521424318
Category : Business & Economics
Languages : en
Pages : 512
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Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
Author: Sokbae Lee
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 334
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Author: Aman Ullah
Publisher: Springer Science & Business Media
ISBN: 3642518486
Category : Business & Economics
Languages : en
Pages : 180
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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).
Author: Yun Wang
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 0
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Author: Matias Damian Cattaneo
Publisher:
ISBN:
Category :
Languages : en
Pages : 268
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Author: Shenshen Yang
Publisher:
ISBN:
Category :
Languages : en
Pages : 432
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This dissertation consists of three chapters in econometric theory, with a focus on identification and estimation of treatment effect in semi-parametric and nonparametric models, when there exists endogeneity problem. These methods are applied on policy and program evaluation in health and labor economics. \indent In the first chapter, I examine the common problem of multiple missing variables, which we refer to as multiple missingness, with non-monotone missing pattern and is usually caused by sub-sampling and a combination of different data sets. One example of this is missingness in both the endogenous treatment and outcome when two variables are collected via different stages of follow-up surveys. Two types of dependence assumptions for multiple missingness are proposed to identify the missing mechanism. The identified missing mechanisms are used later in an Augmented Inverse Propensity Weighted moment function, based on which a two-step semiparametric GMM estimator of the coefficients in the primary model is proposed. This estimator is consistent and more efficient than the previously used estimation methods because it includes incomplete observations. We demonstrate that robustness and asymptotic variances differ under two sets of identification assumptions, and we determine sufficient conditions when the proposed estimator can achieve the semiparametric efficiency bound. This method is applied to the Oregon Health Insurance Experiment and shows the significant effects of enrolling in the Oregon Health Plan on improving health-related outcomes and reducing out-of-pocket costs for medical care. The method proposed here provides unbiased and more efficient estimates. There is evidence that simply dropping the incomplete data creates downward biases for some of the chosen outcome variables. Moreover, the estimator proposed in this paper reduced standard errors by 6-24% of the estimated effects of the Oregon Health Plan. \indent The second chapter is a joint work with Sukjin Han. In this chapter, we consider how to extrapolate the general local treatment effect in a non-parametric setting, with endogenous self-selection problem and lack of external validity. For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to the policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This chapter investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables are only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to incorporate a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services. \indent In the third chapter, I investigate the partial identification bound for treatment effect in a dynamic setting. First, I develop the sharp partial identification bounds of dynamic treatment effect on conditional transition probabilities when the treatment is randomly assigned. Then I relax the randomization assumption and gives partial identification bounds, under a conditional mean independence assumption. Using MTR and MTS assumptions, this bound is further tightened. These bounds are used on estimating labor market return of college degree in a long term, with data from NLSY79
Author: Kirill Evdokimov
Publisher:
ISBN:
Category :
Languages : en
Pages : 398
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