Essays In Non And Semiparametric Econometrics
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Essays In Non And Semiparametric Econometrics
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Author :
language : en
Publisher:
Release Date : 2009
Essays In Non And Semiparametric Econometrics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
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.
Essays In Non And Semiparametric Econometrics
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Author : Christoph Rothe
language : en
Publisher:
Release Date : 2009
Essays In Non And Semiparametric Econometrics written by Christoph Rothe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Essays On Nonparametric And Semiparametric Econometrics
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Author : Eduardo García Echeverri
language : en
Publisher:
Release Date : 2022
Essays On Nonparametric And Semiparametric Econometrics written by Eduardo García Echeverri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Social mobility categories.
"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.
Essays In Semiparametric Econometrics
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Author : Olga Voyteshenko Livingston
language : en
Publisher:
Release Date : 2010
Essays In Semiparametric Econometrics written by Olga Voyteshenko Livingston and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Econometrics categories.
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.
Essays In Honor Of Aman Ullah
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Author : R. Carter Hill
language : en
Publisher: Emerald Group Publishing
Release Date : 2016-06-29
Essays In Honor Of Aman Ullah written by R. Carter Hill and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-29 with Business & Economics categories.
Volume 36 of Advances in Econometrics recognizes Aman Ullah's significant contributions in many areas of econometrics and celebrates his long productive career.
Essays In Econometrics
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Author : Vitaliy Oryshchenko
language : en
Publisher:
Release Date : 2011
Essays In Econometrics written by Vitaliy Oryshchenko and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
This dissertation contributes to the theoretical understanding and practical application of non- and semi-parametric methods in econometrics. It consists of three chapters. The first chapter advocates the use of unsupervised statistical learning (clustering) techniques to group observations from a series of repeated cross-sections to create a pseudo-panel of group averages. This clustering method is based on features of the data space and does not require external grouping variables unlike many other methods. Using a model of enterprise training as an example, fixed eff ects panel data model isestimated using a pseudo-panel of cluster centers. Chapters 2 and 3 extend univariate kernel methods to the estimation of time-varyingdistributions and densities subject to moment constraints. Chapter 2 proposes a weighted kernel density estimator for a time-varying probabilitydensity function and the corresponding cumulative distribution function. Time-varying quantiles are estimated by inverting an estimate of the cumulative distribution function. Weighting schemes are derived from those used in time series modelling. Parameters, including the bandwidth, may be estimated by maximum likelihood or cross-validation. Diagnostic checks are constructed based on residuals given by the predictive cumulativedistribution function. Chapter 3 considers a set-up where additional information concerning the distribution of random variables is available in the form of moment conditions. A weighted kernel density estimate reflecting the extra information is constructed by replacing the uniformweights associated with standard kernel density estimator by generalised empirical likelihood implied probabilities. This chapter shows that the resulting density estimator provides an improved approximation to the moment conditions. Moreover, a reduction in variance is achieved due to the systematic use of the extra moment information.
Essays On Semi Non Parametric Methods In Econometrics
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Author : Sungwon Lee
language : en
Publisher:
Release Date : 2018
Essays On Semi Non Parametric Methods In Econometrics written by Sungwon Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
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.
Essays In Econometrics
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Author : Jeonghwan Kim
language : en
Publisher:
Release Date : 2021
Essays In Econometrics written by Jeonghwan Kim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This dissertation studies a few econometric theories potentially useful for applied economists. In the first two chapters, I study estimation and inference in a semi-parametric model under a monotonicity restriction on the non-parametric component. I develop a new semi-parametric estimator that can be implemented without choosing any smoothing parameters and construct a confidence band for the non-parametric component under monotonicity. The finite dimensional parametric estimator satisfies asymptotic normality. The asymptotic distribution of $L_{\infty}$ - distance for the non-parametric component is Gumbel with the rate of convergence $O_p((\frac{\log n}{n})^{1/3})$. I apply the estimator to estimate the returns to schooling under the restriction that age has monotonic effect on wages. The confidence interval of the returns to schooling and the confidence band of the age effect on the log of wage under an assumed monotonic relationship are reported. I illustrate the confidence intervals of the semi-parametric estimator and the confidence band of the semi-nonparametric estimator using Monte Carlo simulations.\\On the last chapter, my coauthors and I propose a pragmatic approach to the errors-in-variables and nonlinear panel models. These models are often deemed impossible to estimate in their most general forms. For example, the higher order moments approach to errors-in-variables model fails when there is conditional heteroscedasticity. Similarly, nonlinear panel models with fixed effects and small T are known to be problematic to estimate. We propose estimating these models using approximate moments, using a Taylor series approximation applied to Kadane's (1971) small sigma approach. Simulation results suggest that the approximation leads to reasonable sampling properties. Our proposal complements the newly resurgent literature on sensitivity analysis.
Semiparametric And Nonparametric Econometrics
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Author : Aman Ullah
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Semiparametric And Nonparametric Econometrics written by Aman Ullah and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
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).
Essays On Identification And Semiparametric Econometrics
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Author : Paul Schrimpf
language : en
Publisher:
Release Date : 2011
Essays On Identification And Semiparametric Econometrics written by Paul Schrimpf and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
This dissertation is a collection of three independent essays in theoretical and applied econometrics. The first chapter analyzes dynamic games with continuous states and controls. There are two main contributions. First, we give conditions under which the payoff function is nonparametrically identified by the observed distribution of states and controls. The identification conditions are fairly general and can be expected to hold in many potential applications. The key identifying restrictions include that one of the partial derivatives of the payoff function is known and that there is some component of the state space that enters the policy function, but not the payoff function directly. The second contribution of the first chapter is to propose a two-step semiparametric estimator for the model. In the first step the transition densities and policy function are estimated nonparametrically. In the second step, the parameters of the payoff function are estimated from the optimality conditions of the model. We give high-level conditions on the first step nonparametric estimates for the parameter estimates to be consistent and parameters to be v/fn-asymptotically normal. Finally, we show that a kernel based estimator satisfies these conditions. The second chapter, which is coauthored with Liran Einav and Amy Finkelstein, analyzes the welfare cost of adverse selection in the U.K. annuity market. We develop a model of annuity contract choice and estimate it using data from the U.K. annuity market. The model allows for private information about mortality risk as well as heterogeneity in preferences over different contract options. We focus on the choice of length of guarantee among individuals who are required to buy annuities. The results suggest that asymmetric information along the guarantee margin reduces welfare relative to a first best symmetric information benchmark by about 2 percent of annuitized wealth. We also find that by requiring that individuals choose the longest guarantee period allowed, mandates could achieve the first-best allocation. The third chapter develops a test for the exogeneity assumptions of classical factor models based on the fixed interactive effects estimator of Bai (2005). The exact form of the test is given for simple linear models. Simulations are used to asses the test's performance. The application of the test to more complicated models is also considered. The test is applied to a model of education as an example.