Essays in Non- and Semiparametric Econometrics PDF Download
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Author: Christoph Rothe
Publisher:
ISBN:
Category :
Languages : en
Pages : 102
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Author: Christoph Rothe
Publisher:
ISBN:
Category :
Languages : en
Pages : 102
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Author:
Publisher:
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: 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: Sungwon Lee
Publisher:
ISBN:
Category :
Languages : en
Pages : 416
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My dissertation contains three chapters focusing on semi-/non-parametric models in econometrics. The first chapter, which is a joint work with Sukjin Han, considers parametric/semiparametric estimation and inference in a class of bivariate threshold crossing models with dummy endogenous variables. We investigate the consequences of common practices employed by empirical researchers using this class of models, such as the specification of the joint distribution of the unobservables to be a bivariate normal distribution, resulting in a bivariate probit model. To address the problem of misspecification, we propose a semiparametric estimation framework with parametric copula and nonparametric marginal distributions. This specification is an attempt to ensure robustness while achieving point identification and efficient estimation. We establish asymptotic theory for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effects. Numerical studies suggest the sensitivity of parametric specification and the robustness of semiparametric estimation. This paper also shows that the absence of excluded instruments may result in the failure of identification, unlike what some practitioners believe. The second chapter develops nonparametric significance tests for quantile regression models with duration outcomes. It is common for empirical studies to specify models with many covariates to eliminate the omitted variable bias, even if some of them are potentially irrelevant. In the case where models are nonparametrically specified, such a practice results in the curse of dimensionality. I adopt the integrated conditional moment (ICM) approach, which was developed by Bierens (1982) and Bierens (1990) to construct test statistics. The proposed test statistics are functionals of a stochastic process which converges weakly to a centered Gaussian process. The test has non-trivial power against local alternatives at the parametric rate. A subsampling procedure is proposed to obtain critical values. The third chapter considers identification of treatment effect and its distribution under some distributional assumptions. I assume that a binary treatment is endogenously determined. The main identification objects are the quantile treatment effect and the distribution of the treatment effect. I construct a counterfactual model and apply Manski's approach (Manski (1990)) to find the quantile treatment effects. For the distribution of the treatment effect, I adapt the approach proposed by Fan and Park (2010). Some distributional assumptions called stochastic dominance are imposed on the model to tighten the bounds on the parameters of interest. It also provides confidence regions for identified sets that are pointwise consistent in level. An empirical study on the return to college confirms that the stochastic dominance assumptions improve the bounds on the distribution of the treatment effect.
Author: Olga Voyteshenko Livingston
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 180
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Two essays are focused on semiparametric econometric methods. The first essay investigates applicability of the smooth back tting estimator (SBE) to statistical analysis of residential energy consumption. The second essay attempts to incorporate additivity restrictions into semiparametric stochastic frontier estimation. The procedure described in the first study is used to estimate the directional regressions for each of the additive components. These estimates are used as a pilot for stochastic frontier estimation. The essay contains an empirical study of power generating units in the US.
Author: Matias Damian Cattaneo
Publisher:
ISBN:
Category :
Languages : en
Pages : 268
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Author: Sokbae Lee
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 334
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Author: Tomás Andrés Rau Binder
Publisher:
ISBN: 9780549168775
Category :
Languages : en
Pages : 162
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This dissertation consists of two essays. In the first essay I propose a new class of flexible microeconometric models that incorporates endogenous regressors and sorting. Existing models, including Garen's (1984) correlated random coefficients model, and Newey, Powell, and Vella's (1999) fully-flexible model with an additive error, can be derived from a structural equation with unobserved heterogeneity by imposing homogeneity and constancy assumptions on the first and second derivatives. I consider a less restrictive model that imposes homogeneity assumptions on the second partial derivative of the structural equation. Assuming the existence of suitable instrumental variables, the model can be estimated using a generalized control function approach. I consider an application to the estimation of the returns to education in Chile, exploiting variation across regions and cohorts in educational infrastructure and compulsory schooling laws. Using penalized spline functions to approximate the components of the average structural response function, I find that the local average returns to schooling are typically under-estimated by flexible models that ignore the endogeneity of schooling. I also find limited evidence of comparative advantage bias in the returns to certain levels of education.
Author: Aman Ullah
Publisher: Springer Science & Business Media
ISBN: 3642518486
Category : Business & Economics
Languages : en
Pages : 180
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Book Description
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: Hongjun Li
Publisher:
ISBN:
Category :
Languages : en
Pages :
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This dissertation aims at investigating the theory and application of semiparametric econometrics. I first inspect the selection of optimal bandwidth using the cross-validation method for the kernel estimation of cumulative distribution/survivor functions. Then, I analyze the determination of the number of factors with the methods of principal component and information criteria. I also show the application of semiparametric methods to "purchasing power parity" puzzle. Firstly, I propose a data-driven least squares cross-validation method to optimally select smoothing parameters for the nonparametric estimation of cumulative distribution/ survivor functions. The general multivariate covariates can be continuous, discrete/ordered categorical or a mix of either. I establish the asymptotic optimality of least squares cross-validation method. Also, I show that the estimators of cumulative distribution/survivor functions using the smoothing parameters selected by the proposed method is asymptotically normally distributed. Monte Carlo simulation verifies the finite-sample properties of the least squares cross-validation method. Secondly, I provide some discussions on the econometric theory for factor models of large dimensions where the number of factors (r) is allowed to increase as the two dimensions, cross-sections (N) and time dimensions (T) increase. I mainly focus on the determination of the number of factors. I extend the existing panel criteria to high dimension case where r may be increasing with N or T. I show that the number of factors can be consistently estimated using the criteria. Also, Monte-Carlo simulation demonstrates the finite sample properties of the proposed estimating method. Lastly, I consider an empirical application of semiparametric econometrics to the problem of purchasing power parity (hereafter PPP) hypothesis test. Traditional linear cointegration tests of PPP hypothesis often lead to rejection of the PPP hypothesis. More recent studies allowing for some sort of nonlinearity in econometric modelings suggest mixed results and leave this problem as an unresolved issue. Therefore, I analyze PPP hypothesis within a semiparametric framework using the varying coefficient model with integrated variables, which can capture the nonlinearity of the economic structures. Applying the semiparametric functional cointegration test method, I conduct the cointegration test of PPP hypothesis between U.S. and Canada, U.S. and Japan, and U.S. and U.K., respectively to test the PPP hypothesis. In contrast to the usual findings based on linear model PPP hypothesis testing, the semiparametric model based tests provide supporting evidence of the PPP hypothesis. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/152605
