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Author : Hyungtaik Ahn
Publisher :
ISBN 13 :
Total Pages : 402 pages
Book Rating : 4.:/5 (89 download)
Download or read book Three Essays on Two-stage Estimation in Semiparametric and Nonparametric Econometrics written by Hyungtaik Ahn and published by . This book was released on 1991 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Shenshen Yang
Publisher :
ISBN 13 :
Total Pages : 432 pages
Book Rating : 4.:/5 (125 download)
Download or read book Essays on Nonparametric and Semiparametric Identification and Estimation written by Shenshen Yang and published by . This book was released on 2021 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Yi Zheng
Publisher :
ISBN 13 :
Total Pages : 98 pages
Book Rating : 4.:/5 (457 download)
Download or read book Essays on Nonparametric Econometrics with Applications to Consumer and Financial Economics written by Yi Zheng and published by . This book was released on 2008 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This dissertation is composed of three chapters centering on nonparametric econometrics with applications to consumer demand system analysis, value-at-risk analysis of commodity future prices, and credit risk analysis of home mortgage portfolios. The first chapter, based on my joint research with Abdoul Sam considers a semiparametric estimation model for a censored consumer demand system with micro data. A common attribute of disaggregated household data is the censoring of commodities. Maximum likelihood and existing two-step estimators of censored demand systems yield biased and inconsistent estimates when the assumed joint distribution of the disturbances is incorrect. This essay proposes a semiparametric estimator that retains the computational advantage of the two-step methods while circumventing their potential distributional misspecification. The key difference between the proposed estimator and existing two-step counterparts is that the parameters of the binary censoring equations are estimated using a distribution-free single-index model. We implement the proposed estimator using household-level data obtained from the Hainan province in China. Horrowitz and Härdle (1994)'s specification test lends support to our approach. The second chapter is an empirical application of a nonparametric estimator of Value-at-Risk on the cattle feeding margin. Value-at-Risk, known as VaR is a common measure of downside market risk associated with an asset or a portfolio of assets. It has been used as a standard tool of predicting potential portfolio losses for twenty years in the financial industry. Recently VaR has gained popularity in agricultural economics literature since the market price risks associated with agricultural commodities are under evaluation. As initial empirical findings suggest that the performance of any VaR estimation technique is sensitive to the types of data set (portfolio composition) used in developing and evaluating the estimates, agricultural data provides a unique laboratory to further explore VaR and its estimation approaches. This essay as a first attempt applies a distribution-free nonparametric kernel estimator of VaR in an agricultural context, the cattle feeding margin using futures data. The empirical results suggest that the nonparametric VaR estimates enjoy a significant efficiency gain without losing much accuracy compared to the parametric estimates. The third chapter measures credit risks associated with residential mortgage loans. Credit risk is the primary source of risk for real estate lenders. Recent advancements in the measurement and management of credit risk give lenders with sophisticated internal risk models a significant comparative advantage over other lenders in terms of capital optimization and risk controlling. This manuscript helps understand the determinants of credit risk and acquire perspectives on how it is distributed in the current or future loan portfolios. This essay contributes to the existing volume of literature as it incorporates the nonparametric estimation technique into default risk analysis. The CreditRisk model is modified and estimated using the consumer side of information. The model identifies the factors determining household default risks and generates a full loan loss distribution at the portfolio level using consumer finance survey data. In the end, portfolio management strategies are discussed.
Author : Meng-Shiuh Chang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (819 download)
Download or read book Essays on Nonparametric Series Estimation with Application to Financial Econometrics written by Meng-Shiuh Chang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation includes two essays. In the first essay, I proposed an alternative estimator for multivariate densities. This estimator can be characterized as a transformation based estimator. The first stage estimates each marginal density separately. In the second stage, the joint density of estimated marginal cumulative distribution functions (CDF) are approximated by the exponential series estimator. The final estimate is then obtained as the product of the marginal densities and the joint density estimated in the second stage. Extensive Monte Carlo studies show the proposed estimator outperforms kernel estimators in joint density and tail distribution estimation. An illustrative example on estimating the conditional copula density between S & P 500 and FTSE 100 given Hangseng and Nikkei 225 is also discussed. In the second essay, I extended the semiparametric model by Chen and Fan [X. Chen, Y. Fan, Estimation of copula-based semiparametric time series models, Journal of Econometrics 130 (2006) 307-335], and studied a class of univariate copula-based nonparametric stationary Markov models in which the copulas and the marginal distributions are estimated nonparametrically. In particular, I focused on the stationary Markov process of order 1 with continuous state space because it has the beta-mixing property for the analysis of weakly dependent processes. The copula density functions for time series models are approximated by the series estimate on sieve spaces. In this study, a finite dimensional linear space spanned by a sequence of power functions is treated as the sieve space where the estimation space of the copula density function is based. This sieve series estimator can be characterized as the exponential series estimator under mild smoothness conditions. By using the beta-mixing properties, I showed that the copula density function approximated by the exponential series estimator for stationary first-order Markov processes has the same convergence rate as the i.i.d. data. The Monte Carlo simulations show that the proposed estimator outperforms the kernel estimator in the conditional density estimation, except for the Frank copula-based Markov model. In addition, the proposed estimator considerably dominates the kernel estimator when used in the one-step-ahead forecast.
Author : Joel L. Horowitz
Publisher : Springer Science & Business Media
ISBN 13 : 0387928707
Total Pages : 278 pages
Book Rating : 4.3/5 (879 download)
Download or read book Semiparametric and Nonparametric Methods in Econometrics written by Joel L. Horowitz and published by Springer Science & Business Media. This book was released on 2010-07-10 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields, and they are being used in applied research with increasing frequency. The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods. It is accessible to graduate students and applied researchers who are familiar with econometric and statistical theory at the level taught in graduate-level courses in leading universities. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. Empirical examples illustrate the methods that are presented. This book updates and greatly expands the author’s previous book on semiparametric methods in econometrics. Nearly half of the material is new.
Author : Paul Schrimpf
Publisher :
ISBN 13 :
Total Pages : 146 pages
Book Rating : 4.:/5 (746 download)
Download or read book Essays on Identification and Semiparametric Econometrics written by Paul Schrimpf and published by . This book was released on 2011 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Hongjun Li
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (92 download)
Download or read book Three Essays on Semiparametric Econometrics written by Hongjun Li and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Author : Aman Ullah
Publisher :
ISBN 13 :
Total Pages : 192 pages
Book Rating : 4.:/5 (43 download)
Download or read book Semiparametric and Nonparametric Econometrics written by Aman Ullah and published by . This book was released on 1989 with total page 192 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).
Author : Matias Damian Cattaneo
Publisher :
ISBN 13 :
Total Pages : 268 pages
Book Rating : 4.:/5 (34 download)
Download or read book Essays in Semiparametric and Nonparametric Microeconometrics written by Matias Damian Cattaneo and published by . This book was released on 2008 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Mustafa Koroglu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (125 download)
Download or read book Semiparametric Applications in Economic Growth written by Mustafa Koroglu and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three essays that deals with estimation of semiparametric regression methods in macroeconomic context. Chapter 1 introduces the building-blocks of the non-/semiparametric regression methods. A literature review is provided to support the estimation methodologies employed in the subsequent chapters. I survey some nonparametric estimation techniques, including (i) the local least squares kernel estimator; (ii) nonparametric series estimator; (iii) estimation of nonparametric models with endogeneity; and (iv) nonparametric estimation of panel data models. I also survey different bootstrapping methods for nonparametric regression methods. In Chapter 2 we consider a spatial Durbin model with unknown functional-coefficients and nonparametric spatial weights. We apply series approximation method to estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS) estimation method. We illustrate proposed estimation method to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries' openness to trade, respectively. In Chapter 3 I re-investigate the relationship between public debt and economic growth and try to expose nonlinearity in this link through using an endogenous smooth coefficient approach. I find some evidence of parameter heterogeneity in the debt-growth link that may be governed by the institutional quality of countries. My results show a significant negative effect of public debt on economic growth for the countries with the lowest democracy score and high democracy score.
Author : William A. Barnett
Publisher : Cambridge University Press
ISBN 13 : 9780521424318
Total Pages : 512 pages
Book Rating : 4.4/5 (243 download)
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.
Author : Huansha Wang
Publisher :
ISBN 13 : 9781321088717
Total Pages : 133 pages
Book Rating : 4.0/5 (887 download)
Download or read book Essays on Semiparametric Ridge-type Shrinkage Estimation, Model Averaging and Nonparametric Panel Data Model Estimation written by Huansha Wang and published by . This book was released on 2014 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is composed with 4 essays. They explore modelling uncertainty following two major directions. The former 2 contains topics on ordinary and general ridge-type shrinkage estimation developed from model averaging and kernel density estimation. The third one critically reviews recent literature in the areas of model averaging and model selection both parametrically and nonparametrically and proposes topics for future work. The last one focuses on nonparametric panel data estimation with random effects. In chapter 2, ordinary ridge-type shrinkage estimation is extensively studied, where a class of well-behaved ordinary ridge-type semiparametric estimators is proposed. Monte Carlo simulations, theoretical derivations, as well as empirical out-of-sample forecasts are all investigated to prove their usefulness in reducing mean squared errors, i.e. risks. Chapter 3 develops the works in Chapter 2 to the general ridge regressions. By connecting general ridge regression with kernel density estimation, an asymptotically optimal semiparametric ridge-type estimator is built. By connecting general ridge regression with model averaging, a class of model averaging ridge-type estimators are obtained. These estimators are observed to have different improvements upon the feasible general ridge estimators when model uncertainties, i.e., the error variances are different. To encourage better understanding on model averaging and model selection, Chapter 4 gives a comprehensive literature review and analysis on these topics from a frequentist's point of view. Parametric and nonparametric procedures in the recent developments are explored. Chapter 5 starts investigating panel data estimation by introducing nonparametrics in the picture. The proposed two-stage estimator shows good behaviors in Monte Carlo simulation. In addition, illustrative empirical examples in health economics and environmental economics are also introduced.
Author : Guangyi Ma
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (819 download)
Download or read book Three Essays on Estimation and Testing of Nonparametric Models written by Guangyi Ma and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, I focus on the development and application of nonparametric methods in econometrics. First, a constrained nonparametric regression method is developed to estimate a function and its derivatives subject to shape restrictions implied by economic theory. The constrained estimators can be viewed as a set of empirical likelihood-based reweighted local polynomial estimators. They are shown to be weakly consistent and have the same first order asymptotic distribution as the unconstrained estimators. When the shape restrictions are correctly specified, the constrained estimators can achieve a large degree of finite sample bias reduction and thus outperform the unconstrained estimators. The constrained nonparametric regression method is applied on the estimation of daily option pricing function and state-price density function. Second, a modified Cumulative Sum of Squares (CUSQ) test is proposed to test structural changes in the unconditional volatility in a time-varying coefficient model. The proposed test is based on nonparametric residuals from local linear estimation of the time-varying coefficients. Asymptotic theory is provided to show that the new CUSQ test has standard null distribution and diverges at standard rate under the alternatives. Compared with a test based on least squares residuals, the new test enjoys correct size and good power properties. This is because, by estimating the model nonparametrically, one can circumvent the size distortion from potential structural changes in the mean. Empirical results from both simulation experiments and real data applications are presented to demonstrate the test's size and power properties. Third, an empirical study of testing the Purchasing Power Parity (PPP) hypothesis is conducted in a functional-coefficient cointegration model, which is consistent with equilibrium models of exchange rate determination with the presence of trans- actions costs in international trade. Supporting evidence of PPP is found in the recent float exchange rate era. The cointegration relation of nominal exchange rate and price levels varies conditioning on the real exchange rate volatility. The cointegration coefficients are more stable and numerically near the value implied by PPP theory when the real exchange rate volatility is relatively lower.
Author : Haitian Xie
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)
Download or read book Essays on Causal Inference and Econometrics written by Haitian Xie and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is a collection of three essays on the econometric analysis of causal inference methods. Chapter 1 examines the identification and estimation of the structural function in fuzzy RD designs with a continuous treatment variable. We show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff under shape restrictions, including monotonicity and smoothness conditions. Based on the nonparametric identification equation, we propose a three-step semiparametric estimation procedure and establish the asymptotic normality of the estimator. The semiparametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by using the discontinuity in natural light timing at time zone boundaries. Chapter 2 examines the local linear regression (LLR) estimate of the conditional distribution function F(y|x). We derive three uniform convergence results: the uniform bias expansion, the uniform convergence rate, and the uniform asymptotic linear representation. The uniformity in the above results is with respect to both x and y and therefore has not previously been addressed in the literature on local polynomial regression. Such uniform convergence results are especially useful when the conditional distribution estimator is the first stage of a semiparametric estimator. Chapter 3 studies the estimation of causal parameters in the generalized local average treatment effect model, a generalization of the classical LATE model encompassing multi-valued treatment and instrument. We derive the efficient influence function (EIF) and the semiparametric efficiency bound for two types of parameters: local average structural function (LASF) and local average structural function for the treated (LASF-T). The moment condition generated by the EIF satisfies two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment condition, we propose the double/debiased machine learning (DML) estimators for LASF and LASF-T. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application, we study the effects across different sources of health insurance by applying the developed methods to the Oregon Health Insurance Experiment.
Author : Sungwon Lee
Publisher :
ISBN 13 :
Total Pages : 416 pages
Book Rating : 4.:/5 (14 download)
Download or read book Essays on Semi-/non-parametric Methods in Econometrics written by Sungwon Lee and published by . This book was released on 2018 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Zhutong Gu
Publisher :
ISBN 13 :
Total Pages : 172 pages
Book Rating : 4.:/5 (13 download)
Download or read book Essays on Nonparametric Structural Econometrics written by Zhutong Gu and published by . This book was released on 2017 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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Author : Yanqin Fan
Publisher : National Library of Canada
ISBN 13 : 9780315552708
Total Pages : 274 pages
Book Rating : 4.5/5 (527 download)
Download or read book Seemingly Unrelated Essays in Econometrics [microform] : Functions of Mixing Processes, Nonparametric Estimation and Cointegration written by Yanqin Fan and published by National Library of Canada. This book was released on 1990 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three essays related to the estimation of econometric models with dependent heterogeneous observations. The first presents a unified overview of the properties of three typical classes of dependent heterogeneous processes. The next two investigate the properties of econometric estimators applied to data generated by such processes.
