Essays On Nonparametric And Semiparametric Identification And Estimation
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Essays On Nonparametric And Semiparametric Identification And Estimation
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Author : Shenshen Yang
language : en
Publisher:
Release Date : 2021
Essays On Nonparametric And Semiparametric Identification And Estimation written by Shenshen Yang 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 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
Essays On Nonparametric Identification And Estimation Of All Pay Auctions And Contests
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Author : Ksenia Shakhgildyan
language : en
Publisher:
Release Date : 2019
Essays On Nonparametric Identification And Estimation Of All Pay Auctions And Contests written by Ksenia Shakhgildyan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
My dissertation contributes to the structural nonparametric econometrics of auctions and contests with incomplete information. It consists of three chapters. The first chapter investigates the identification and estimation of an all-pay auction where the object is allocated to the player with the highest bid, and every bidder pays his bid regardless of whether he wins or not. As a baseline model, I consider the setting, where one object is allocated among several risk-neutral participants with independent private values (IPV); however, I also show how the model can be extended to the multiunit case. Moreover, the model is not confined to the IPV paradigm, and I further consider the case where the bidders' private values are affiliated (APV). In both IPV and APV settings, I prove the identification and derive the consistent estimators of the distribution of the bidders' valuations using a structural approach similar to that of Guerre et al. (2000). Finally, I consider the model with risk-averse bidders. I prove that in general the model in this set-up is not identified even in the semi-parametric case where the utility function of the bidders is restricted to belong to the class of functions with constant absolute risk aversion (CARA). The second chapter proves the identification and derives the asymptotically normal estimator of a nonparametric contest of incomplete information with uncertainty. By uncertainty, I mean that the contest success function is not only determined by the bids of the players, but also by the variable, which I call uncertainty, with a nonparametric distribution, unknown to the researcher, but known to the bidders. This work is the first to consider the incomplete information contest with a nonparametric contest success function. The limiting case of the model when there is no uncertainty is an all-pay auction considered in the first chapter. The model with two asymmetric players is examined. First, I recover the distribution of uncertainty using the information on win outcomes and bids. Next, I adopt the structural approach of Guerre et al. (2000) to obtain the distribution of the bidders' valuations (or types). As an empirical application, I study the U.S. House of Representatives elections. The model provides a method to disentangle two sources of incumbency advantage: a better reputation, and better campaign financing. The former is characterized by the distribution of uncertainty and the latter by the difference in the distributions of candidates' types. Besides, two counterfactual analyses are performed: I show that the limiting expenditure dominates public campaign financing in terms of lowering total campaign spending as well as the incumbent's winning probability. The third chapter is a semiparametric version of the second chapter. In the case when the data is sparse, some restrictions on the nonparametric structure need to be put. In this work, I prove the identification and derive the consistent estimator of a contest of incomplete information, in which an object is allocated according to the serial contest success function. As in previous chapters, I recover the distribution of the bidders' valuations from the data on observed bids using a structural approach similar to that of Guerre et al. (2000) and He and Huang (2018). As a baseline model, I consider the symmetric contest. Further, the model is extended to account for the bidders' asymmetry.
Identification And Inference For Econometric Models
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Author : Donald W. K. Andrews
language : en
Publisher: Cambridge University Press
Release Date : 2005-07-04
Identification And Inference For Econometric Models written by Donald W. K. Andrews and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-04 with Business & Economics categories.
This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.
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.
Essays In Semiparametric And Nonparametric Microeconometrics
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Author : Matias Damian Cattaneo
language : en
Publisher:
Release Date : 2008
Essays In Semiparametric And Nonparametric Microeconometrics written by Matias Damian Cattaneo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
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 On Nonparametric Structural Econometrics
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Author : Zhutong Gu
language : en
Publisher:
Release Date : 2017
Essays On Nonparametric Structural Econometrics written by Zhutong Gu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Econometrics categories.
My dissertation contains three papers in the theory and applications of nonparametric structural econometrics. In chapter 1, I propose a nonparametric test for additive separability of unobservables of unrestricted dimensions with average structural functions. Chapter 2 considers identification and estimation of fully nonparametric production functions and empirically tests for the Hicks-neutral productivity shocks, a direct application of the test proposed in chapter 1. In chapter 3, my authors and I study the semiparametric ordered response models with correlated unobserved thresholds and investigate the issue of corporate bond rating biases due to the sharing of common investors between bond-issuing firms and credit rating agencies. Brief abstracts are presented in order below. Additive separability between observables and unobservables is one of the essential properties in structural modeling of heterogeneity in the presence of endogeneity. In this chapter, I propose an easy-to-compute test based on empirical quantile mean differences between the average structural functions (ASFs) generated by nonparametric nonseparable and separable models with unrestricted heterogeneity. Given identification, I establish conditions under which structural additivity can be linked to the equality of ASFs derived from the two commonly employed competing specifications. I estimate the reduced form regressions by Nadaraya-Watson estimators and control for the asymptotic bias. I show that the asymptotic test statistic follows a central Chi-squred distribution under the null hypothesis and has power against a sequence of root N-local alternatives. The proposed test statistic works well in a series of finite sample simulations with analytic variances, alleviating the computational burden often involved in bootstrapped inferences. I also show that the test can be straightforwardly extended to semiparametric models, panel data and triangular simultaneous equations frameworks. Hicks-neutral technology implies the substitution pattern of labor and capital in a production function is not affected by technological shocks, first put forth by John Hicks in 1932. In this chapter, I consider the identification and estimation of fully nonparametric firm-level production functions and empirically test the Hicks-neutral productivity in the U.S. manufacturing industry during the period from 1990 to 2011. Firstly, I extend the proxy variable approach to fully nonparametric settings and propose a robust estimator of average output elasticities in non-Hick-neutral scenarios. Secondly, I show that the Hicks-neutral restriction can be converted to the additive separability between inputs and unobservables in a monotonic transformed model for which the proposed testing procedure can be directly applied. It turns out that there is substantial heterogeneity in the nonparametric output elasticities over various counterfactual input amounts. I also find that there were periods in the 90s when the non-Hicks technological shocks occur which coincide with the mass adoption of computing technology. However, the productivity has thereafter become Hicks-neutral into the 2000s. Controlling for sector-specific effects mitigate the non-Hicks-neutrality to some extend. Previous literature on bond rating indicates that credit rating agencies (CRAs) may assign favorable ratings to bond-issuing firms that have a closer relationship. This not only implies the existence of firm-specific unobserved heterogeneity in the rating criteria but also makes some bond/firm characteristics endogenous, which is confirmed by our empirical results. In this chapter, my coauthors and I propose a semiparametric two-step index and location estimator of ordered response models that explicitly incorporates endogenous regressors and correlated random thresholds. We apply our model in the application of assessing bond rating bias of credit rating agencies. Methodologically, we first show that the heterogeneous relative thresholds can be identified using conditional shift restrictions in conjunction with the control variables for the firm-CRA liaison. Then, we illustrate the estimation strategy in a heuristic manner and derive the asymptotic properties of the suggested estimator. In the application, we find significant overrating bias through varying thresholds as the liaison strengthens and those biases display heterogeneous patterns with respect to rating categories.
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 On Econometrics And Its Application To Education
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Author : Xiaoting Sun
language : en
Publisher:
Release Date : 2019
Essays On Econometrics And Its Application To Education written by Xiaoting Sun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
This dissertation consists of three chapters that study econometrics questions and their applications to education. Chapter 1 studies a nonparametric two-sided many-to-one matching model, where many agents on one side match one institution on the other side. Classical examples include student-college matching and firm-worker matching. In this paper, I study nonparametric identification and estimation of many-to-one matching with non-transferable utility. The existing literature either assumes that the matching algorithm and reported preferences are observed or that preferences are homogeneous. This paper assumes heterogeneous preferences on the two sides and only requires data on who matches with whom in a single large market. Under mild restrictions, I prove that both the utility functions of the students and colleges and the joint distribution of unobserved heterogeneity from the two sides are nonparametrically identified. Based on my constructive identification results, I propose nonparametric and semiparametric estimators of the model and establish their consistency and asymptotic normality. The semiparametric estimator converges at a root-n rate. Chapter 2 analyzes the U.S. college admissions under a many-to-one matching framework. In recovering the parameters of the utility functions, I am able to demonstrate substantial welfare consequences for different groups of students, relative to a centralized matching mechanism. In estimating the model using data from High School Longitudinal Study of 2009 (HSLS:09) and the Integrated Postsecondary Education Data System (IPEDS), I show that the students who experience the largest losses are first-generation college students and low-ability students. This potential loss among these groups provides an opportunity for policy interventions to lead to substantial gains in welfare. Chapter 3 (joint with Kathleen McGarry) studies the three generations of changing gender patterns of schooling in China. The phenomenon of son preference in China and throughout much of Asia has been well documented. However, changing economic conditions, such as increases in educational attainment and employment opportunities for women and the rise in the prevalence of one child families, have likely changed the incentives for parents to invest in daughters. In this paper, we take advantage of data spanning three generations of Chinese families to examine the evolution of educational attainment for boys and girls and importantly the relative levels of schooling of each gender. We also use variation in the timing of compulsory schooling laws and the implementation of the one child policy to assess the effect of these policy measures on the relative educational levels. We find a substantial narrowing of the gap between the schooling of boys and girls, so much so that girls now have more schooling on average than boys. In addition, public policy initiatives had a larger effect in rural than urban areas.
Essays On Nonparametric And High Dimensional Econometrics
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Author : Jesper Riis-Vestergaard Soerensen
language : en
Publisher:
Release Date : 2018
Essays On Nonparametric And High Dimensional Econometrics written by Jesper Riis-Vestergaard Soerensen 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.
This dissertation studies questions related to identification, estimation, and specification testing of nonparametric and high-dimensional econometric models. The thesis is composed by two chapters. In Chapter 1, I propose specification tests for two formally distinct but related classes of econometric models: (1) semiparametric conditional moment restriction models dependent on conditional expectation functions, and (2) a class of high-dimensional unconditional moment restriction models dependent on high-dimensional best linear predictors. These classes may be motivated by economic models in which agents make choices under uncertainty and therefore have to predict payoff-relevant variables such as the behavior of other agents. The proposed tests are shown to be both asymptotically correctly sized and consistent. Moreover, I establish a bound on the rate of local alternatives for which the test for high-dimensional unconditional moment restriction models is consistent. These results allow researchers to test the specification of their models without introducing additional parametric, typically ad hoc, assumptions on expectations. In Chapter 2, I show that it is possible to identify and estimate a generalized panel regression model (GPRM) without imposing any parametric structure on (1) the function of observable explanatory variables, (2) the systematic function through which the function of observable explanatory variables, fixed effect, and disturbance term generate the outcome variable, or (3) the distribution of unobservables. I proceed with estimation using a series maximum rank correlation estimator (SMRCE) of the function of observable explanatory variables and provide conditions under which L2-consistency is achieved. I also provide conditions under which both L2 and uniform convergence rates of the SMRCE may be derived.