Download or read book Smooth Nonparametric Conditional Quantile Profit Function Estimation written by Anton Piskunov and published by . This book was released on 2009 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, in an attempt to produce robust production frontier estimators, Aragon et al. [2005, Nonparametric frontier estimation: a conditional quantile-based approach. Econometric Theory 21, 358-389] and Martins-Filho and Yao [2008, A smooth nonparametric conditional quantile frontier estimator. Journal of Econometrics 143, 317-333] considered the estimation of nonparametric [alpha]- frontier models based on conditional quantiles with [alpha][element of] (0,1). There exist, however, a large and growing literature in economics devoted to the estimation of profit functions. In this paper, we first define an [alpha]-profit function based on the quantile of the suitably defined conditional distribution for profits. Second we propose a smooth nonparametric conditional quantile estimator for the [alpha]-profit function model. Our estimator is computationally simple, resistant to outliers and extreme values, and smooth. In addition, the estimator is shown to be consistent and asymptotically normal under mild regularity conditions. A small simulation study provides evidence of the finite sample properties for the estimator.
Download or read book Nonparametric Conditional Quantile Estimation for Profit Frontier Analysis written by Shan Zhou and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Estimation of Conditional Quantile Function written by Ashis Kumar Gangopadhyay and published by . This book was released on 1987 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Estimation of Conditional Quantiles written by Alexander Kukush and published by . This book was released on 2005 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Simple Nonparametric Approach for Estimation and Inference of Conditional Quantile Functions written by Zheng Fang and published by . This book was released on 2018 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we present a new nonparametric method for estimating a conditional quantile function and develop its weak convergence theory. The proposed estimator is computationally easy-to-implement, and automatically ensures quantile monotonicity by construction. For inference, we propose to use a residual bootstrap method. The Monte Carlo simulations show that our new estimator compares well with the checkfunction-based estimator in terms of estimation mean squared error (MSE), and the bootstrap confidence bands give adequate coverage probabilities. An empirical example considering a dataset from Canadian high school graduate earnings illustrates that the proposed method delivers a more reasonable quantile estimate than the check-function counterpart.
Download or read book Selected topics on nonparametric conditional quantiles and risk theory written by Yebin Cheng and published by Rozenberg Publishers. This book was released on 2007 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Essays on High dimensional Nonparametric Smoothing and Its Applications to Asset Pricing written by Chaojiang Wu and published by . This book was released on 2013 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric smoothing, a method of estimating smooth functions, has gained increasing popularity in statistics and application literature during the last few decades. This dissertation has focused primarily on the nonparametric estimation in quantile regression (Chapter 1) and an application of nonparametric estimation to financial asset pricing (Chapter 2). In the first essay (Chapter 1), we consider the estimation problem of conditional quantile when multi-dimensional covariates are involved. To overcome the "curse of dimensionality" yet retain model flexibility, we propose two partially linear models for conditional quantiles: partially linear single-index models (QPLSIM) and partially linear additive models (QPLAM). The unknown univariate functions are estimated by penalized splines. An approximate iteratively reweighted penalized least square algorithm is developed. To facilitate model comparisons, we develop effective model degrees of freedom for penalized spline conditional quantiles. Two smoothing parameter selection criteria, Generalized Approximate Cross-validation (GACV) and Schwartz-type Information Criterion (SIC) are studied. Some asymptotic properties are established. Finite sample properties are investigated through simulation studies. Application to the Boston Housing data demonstrates the success of proposed approach. Both simulations and real applications show encouraging results of the proposed estimators. In the second essay (Chapter 2), we investigate whether the conditional CAPM helps explain the value premium using the single-index varying-coefficient model. Our empirical specification has two novel advantages relative to those commonly used in the previous studies. First, it not only allows for a flexible dependence of conditional beta on state variables but also modeling heteroskedasticity. Second, from a large set of candidate state variables, we identify the most influential ones through an exhaustive variable selection method. We have also developed statistics to test the functional form of conditional beta and alpha, which provides justifications for or against the practices of letting conditional beta depend linearly on state variables and assuming constant alpha. Consistent with the notion that the value premium tends to be riskier during business recessions than during business expansions, we find that its conditional beta co-moves with unemployment and inflation, the two most closely watched gauges of aggregate economy by the Federal Reserve, and the price-earnings ratio. Realized beta does not subsume all the other explanatory variables when we include the realized beta as a state variable. The alpha is smaller for the conditional CAPM than for the unconditional CAPM; nevertheless, neither model fully explains the value premium.
Download or read book Smooth Nonparametric Quantile Estimation from Right censored Data written by Yuhlong Lio and published by . This book was released on 1987 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Introduction to the Advanced Theory of Nonparametric Econometrics written by Jeffrey S. Racine and published by Cambridge University Press. This book was released on 2019-06-27 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides theory, open source R implementations, and the latest tools for reproducible nonparametric econometric research.
Download or read book Nonparametric Estimation of Quantiles and of Density Functions Under Censoring Discrete Failure Models and Multiple Comparisons written by W. J. Padgett and published by . This book was released on 1985 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: Major results have been obtained in the areas of nonparametric estimation of quantiles and of density functions under censoring, discrete failure models, and multiple comparisons. In particular, smooth nonparametric estimators of quantile functions from censored data were developed which give better estimates of percentiles of the lifetime distribution than the usual product-limit quantile function. Also, smooth density estimators from censored data were investigated using maximum penalized likelihood procedures. Several parametric models were proposed for the case of discrete failure data. These models provide a better fit to such data than some previously used discrete models. Finally, new methods of constructing simultaneous confidence intervals for pairwise differences of means of normal populations were developed, and the problem of selecting an asymptotically optimal design for comparing several new treatments with a control was solved. Work is continuing on the study of properties of kernel type quantile function estimators and development of goodness-of-fit tests for the model assumptions in accelerated life testing. Keywords: Nonparametric quantile estimation; Density estimation; Right-censored data; Discrete failure models; Multiple comparisons; Accelerated life testing.
Download or read book A Study of a Smooth Nonparametric Estimator of Quantile Residual Life Function written by Zhoujun Feng and published by . This book was released on 1989 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Econometric Methods written by Qi Li and published by Emerald Group Publishing. This book was released on 2009-12-04 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.
Download or read book Nonparametric Econometrics written by Jeffrey Scott Racine and published by Now Publishers Inc. This book was released on 2008 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonparametric Econometrics is a primer for those who wish to familiarize themselves with nonparametric econometrics. While the underlying theory for many of these methods can be daunting for practitioners, this monograph presents a range of nonparametric methods that can be deployed in a fairly straightforward manner. Nonparametric methods are statistical techniques that do not require a researcher to specify functional forms for objects being estimated. The methods surveyed are known as kernel methods, which are becoming increasingly popular for applied data analysis. The appeal of nonparametric methods stems from the fact that they relax the parametric assumptions imposed on the data generating process and let the data determine an appropriate model. Nonparametric Econometrics focuses on a set of touchstone topics while making liberal use of examples for illustrative purposes. The author provides settings in which the user may wish to model a dataset comprised of continuous, discrete, or categorical data (nominal or ordinal), or any combination thereof. Recent developments are considered, including some where the variables involved may in fact be irrelevant, which alters the behavior of the estimators and optimal bandwidths in a manner that deviates substantially from conventional approaches.
Download or read book Smooth Nonparametric Quantile Estimation Under Censoring Simulations and Bootstrap Methods written by W. J. Padgett and published by . This book was released on 1986 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objectives of this paper are two-fold. One is to report results of extensive Monte Carlo simulations which demonstrate the behavior of the mean squared error of the kernel estimator with respect to bandwidth. These simulations provide a method of choosing an optimal bandwidth when the form of the lifetime and censoring distributions are known. Also, they compare the kernel-type estimator with the product-limit qauntile estimator. Five commonly used parametric lifetime distributions, two censoring mechanisms, and four different kernel functions are considered in this study, which is an extension of the brief simulations for exponential distributions reported by Padgett (1986). The second objective is to present a nonparametric method for bandwidth selection based on the bootstrap for right-censored data. This data-based procedure used the bootstrap to estimate mean squared error, and is both an extension and modification of the methods proposed by Padgett. Bandwidth selection using the bootstrap is important for small and moderately large samples since no exact expressions exist for the mean squared error of the kernel-type quantile estimator.
Download or read book Semiparametric and Nonparametric Econometrics written by Aman Ullah and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 180 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).
Download or read book Smooth Nonparametric Function Estimation from Record breaking Data written by Sneh Gulati and published by . This book was released on 1991 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:
