Download or read book A Derivative Based Estimator for Semiparametric Index Models written by Albertus Christianus Dorothea Donkers and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a semiparametric estimator for single- and multiple index models. It provides an extension of the average derivative estimator to the multiple index model setting. The estimator uses the average of the outer product of derivatives and is shown to be root-N consistent and asymptotically normal. Unlike the average derivative estimator, our estimator still works in the single-index setting when the expected derivative is zero (symmetry). Compared to other estimators for multiple index models, the proposed estimator has the advantage of ease of computation. While many econometric models can be regarded as multiple index models with known number of indices, our estimator in addition provides for a natural framework within which to test for the number of indices required.
Download or read book A Method of Moments Estimator for Semiparametric Index Models written by Bas Donkers and published by . This book was released on 2008 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose an easy to use derivative based two-step estimation procedure for semi-parametric index models. In the first step various functionals involving the derivatives of the unknown function are estimated using nonparametric kernel estimators. The functionals used provide moment conditions for the parameters of interest, which are used in the second step within a method-of-moments framework to estimate the parameters of interest. The estimator is shown to be root N consistent and asymptotically normal. We extend the procedure to multiple equation models. Our identification conditions and estimation framework provide natural tests for the number of indices in the model. In addition we discuss tests of separability, additivity, and linearity of the influence of the indices.
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.
Download or read book Nonparametric and Semiparametric Models written by Wolfgang Karl Härdle and published by Springer Science & Business Media. This book was released on 2012-08-27 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.
Download or read book Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models written by Myoung-jae Lee and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book the author surveys new techniques in econometrics which may be used to analyse semiparametric models. As well as covering topics such as instrumental variable estimation, nonparametric density and regression function estimation and semiparametric limited dependent variable models, the book provides details of how these methods may be implemented using software.
Download or read book Semiparametric Methods in Econometrics written by Joel L. Horowitz and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many econometric models contain unknown functions as well as finite- dimensional parameters. Examples of such unknown functions are the distribution function of an unobserved random variable or a transformation of an observed variable. Econometric methods for estimating population parameters in the presence of unknown functions are called "semiparametric." During the past 15 years, much research has been carried out on semiparametric econometric models that are relevant to empirical economics. This book synthesizes the results that have been achieved for five important classes of models. The book is aimed at graduate students in econometrics and statistics as well as professionals who are not experts in semiparametic methods. The usefulness of the methods will be illustrated with applications that use real data.
Download or read book Simple Edgeworth Approximations for Semiparametric Averaged Derivatives written by Chuan Goh and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note proposes a computationally simple empirical Edgeworth expansion for the limiting distribution of a Studentized estimator of a semiparametric single index model. The estimator in question is the density-weighted averaged derivative estimator implemented according to the method of Powell, Stock and Stoker (1989). The coefficients of the expansion are derived from the cumulants of a bootstrapped estimate of the distribution of the Studentized estimator. Monte Carlo evidence indicates finite-sample performance comparable to that of the empirical Edgeworth expansions proposed by Nishiyama and Robinson (2000).
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.
Download or read book Nonparametric Econometrics written by Qi Li and published by Princeton University Press. This book was released on 2023-07-18 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
Download or read book Semiparametric Regression for the Applied Econometrician written by Adonis Yatchew and published by Cambridge University Press. This book was released on 2003-06-02 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an accessible collection of techniques for analyzing nonparametric and semiparametric regression models. Worked examples include estimation of Engel curves and equivalence scales, scale economies, semiparametric Cobb-Douglas, translog and CES cost functions, household gasoline consumption, hedonic housing prices, option prices and state price density estimation. The book should be of interest to a broad range of economists including those working in industrial organization, labor, development, urban, energy and financial economics. A variety of testing procedures are covered including simple goodness of fit tests and residual regression tests. These procedures can be used to test hypotheses such as parametric and semiparametric specifications, significance, monotonicity and additive separability. Other topics include endogeneity of parametric and nonparametric effects, as well as heteroskedasticity and autocorrelation in the residuals. Bootstrap procedures are provided.
Download or read book Series Estimation for Single Index Models Under Constraints written by Chaohua Dong and published by . This book was released on 2018 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, a semiparametric single-index model is investigated. The link function is allowed to be unbounded and has unbounded support that answers a pending issue in the literature. Meanwhile, the link function is treated as a point in an infinitely many dimensional function space which enables us to derive the estimates for the index parameter and the link function simultaneously. This approach is different from the profile method commonly used in the literature. The estimator is derived from an optimization with the constraint of an identification condition for the index parameter, which addresses an important problem in the literature of single-index models.In addition, making the best use of a property of Hermite orthogonal polynomials, an explicit estimator for the index parameter is obtained. Asymptotic properties for the two estimators of the index parameter are established. Their efficiency is discussed in some special cases as well. The finite sample properties of the two estimators are demonstrated through an extensive Monte Carlo study and an empirical example.
Download or read book Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models written by Myoung-jae Lee and published by Springer Verlag. This book was released on 1996-01-01 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classical econometric approach to modelling has been to specify a model up to a finite-dimensional parameter vector, and estimation and testing techniques have been widely used on these finite-dimensional parameter spaces. In the last fifteen years or so however, new methods have been developed to allow more flexible models which utilise infinite-dimensional parameters. Simultaneously, methods of moments estimation have also become more widely used and applied. In this book, the author provides a survey of these modern techniques and how they are applied to limited dependent variable (LDV) models. As well as covering many classical approaches, the topics covered include: instrumental variable estimation, the generalized method of moments, extremum estimators, methods of simulated moments, minimum distance estimation, nonparametric density and regression function estimation, and semiparametric methods for LDV. As a result, many graduate students and research workers will appreciate this up-to-date account. An appendix describes the use of the software package GAUSS to implement these methods in conjunction with some real data sets.
Download or read book Density Estimation for Some Semiparametric Models written by Manuel Hernandez Bejarano and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classical nonparametric kernel density estimator has been widely used to estimate the marginal density of a variable of interest in fields such as finance and economics. However, it has several limitations, including a slow convergence rate, which becomes particularly problematic in small sample sizes. This dissertation document is concerned with studying more efficient density estimations for the marginal density of two important semiparametric models. We show that the proposed estimators exhibit appealing properties that are absent in the classical estimator. In the first project, chapter 2, motivated by the slow convergence rate of classical nonparametric kernel density estimator, we study more efficient density estimation and density derivative estimation for the marginal density of nonparametric regression models. We show that in the presence of unknown nonparametric regression function, the proposed density and density derivative estimators can achieve parametric convergence rate, √n, and possess several appealing properties which the classical estimator lacks. Also, in the absence of nonparametric regression function, when the noise is normally distributed, the proposed method performs as well as if we have known the model and estimated the density using the maximum likelihood method. Based on the proposed density estimator, we further propose a more powerful density-based specification test for the nonparametric regression function. Our extensive numerical studies show that the proposed density estimator, density derivative estimator, and specification test significantly outperform existing ones. In the second project, chapter 3, we study a more efficient density estimation for the stationary density of nonparametric autoregressive conditional heteroscedasticity (NARCH) models. These models are important tools in analyzing time series, specifically in economic and financial applications where the goal is modeling and understanding the volatility of the statistical data since this volatility appears to change over time and exhibit clustering. We demonstrate that in the presence of an unknown nonparametric variance structure, we can establish the root-n consistency of the proposed density estimator, improving this way the widely used nonparametric kernel density estimator whose rate of convergence is inferior. A numerical study confirms the results. The density estimator is applied to the S&P 500 Index data. Finally, we showcase a practical implementation of the proposed density estimator in quantile regression. Specifically, we propose to get a more accurate estimate of the limiting variance of the estimated coefficients in a quantile regression model whose errors follow a nonparametric autoregressive conditional heteroscedastic structure. We perform a simulation study, which shows that using the new density estimator leads to a more accurate estimation of this asymptotic variance compared to the results obtained using the classical density estimator. To illustrate the application of this methodology in estimating the asymptotic variance, we apply it to the monthly inflation rate of the United States. Finally, Chapter 4 summarizes the main conclusions of the projects outlined in this document, as well as two potential avenues for future research in density estimation in the context of time series.
Download or read book Microeconometrics written by A. Colin Cameron and published by Cambridge University Press. This book was released on 2005-05-09 with total page 1058 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.
Download or read book Optimal Semi parametric Estimation in Single index Models written by Peter Hall and published by . This book was released on 1991 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Recursive Differencing for Estimating Semiparametric Models written by Chan Shen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Controlling the bias is central to estimating semiparametric models. Many methods have been developed to control bias in estimating conditional expectations while maintaining a desirable variance order. However, these methods typically do not perform well at moderate sample sizes. Moreover, and perhaps related to their performance, non-optimal windows are selected with undersmoothing needed to ensure the appropriate bias order. In this paper, we propose a recursive di¤erencing estimator for conditional expectations. When this method is combined with a bias control targeting the derivative of the semiparametric expectation, we are able to obtain asymptotic normality under optimal windows. As suggested by the structure of the recursion, in a wide variety of triple index designs, the proposed bias control performs much better at moderate sample sizes than regular or higher order kernels and local polynomials.
Download or read book Estimation in Semiparametric Models written by Johann Pfanzagl and published by Springer. This book was released on 1990 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
