Weighted Semiparametric Estimator for Binary Response Models PDF Download
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Author: Anas Ramadan
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Anas Ramadan
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Roger Klein
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 52
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Author: Walter Belluzzo (Jr.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 152
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Author: Joel L. Horowitz
Publisher: Springer Science & Business Media
ISBN: 0387928707
Category : Business & Economics
Languages : en
Pages : 278
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Book Description
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: Margarida Genius
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 274
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Author: Ron Mittelhammer
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ISBN:
Category :
Languages : en
Pages : 60
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Author: Viktor Subbotin
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ISBN:
Category :
Languages : en
Pages : 0
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Rank-based estimators are important tools of robust estimation in popular semiparametric models under monotonicity constraints. Here we study weighted versions of such estimators. Optimally weighted monotone rank estimator (MR) of Cavanagh and Sherman (1998) attains the semiparametric efficiency bound in the nonlinear regression model and the binary choice model. Optimally weighted maximum rank correlation estimator (MRC) of Han (1987) has the asymptotic variance close to the semiparametric efficiency bound in single-index models under independence when the distribution of the errors is close to normal, and is consistent under deviations from the single index assumption. Under moderate nonlinearities and nonsmoothness in the data, the efficiency gains from weighting are likely to be small for MR and MRC in the binary choice model and for MRC in the transformation model, and can be large for MR and MRC in the monotone regression model.
Author: Jin-Young Choi
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ISBN:
Category :
Languages : en
Pages : 0
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A semiparametric estimator for binary-outcome sample-selection models is proposed that imposes only single index assumptions on the selection and outcome equations without specifying the error term distribution. I adopt the idea in Lewbel (2000) using a 'special regressor' to transform the binary response Y so that the transformed Y becomes linear in the latent index, which then makes it possible to remove the selection correction term by differencing the transformed Y equation. There are various versions of the estimator, which perform differently trading off bias and variance. A simulation study is conducted, and then I apply the estimators to US presidential election data in 2008 and 2012 to assess the impact of racial prejudice on the elections, as a black candidate was involved for the first time ever in the US history.
Author: Joel L. Horowitz
Publisher: Springer Science & Business Media
ISBN: 1461206219
Category : Mathematics
Languages : en
Pages : 211
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Book Description
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.
Author: Wolfgang Karl Härdle
Publisher: Springer Science & Business Media
ISBN: 364217146X
Category : Mathematics
Languages : en
Pages : 317
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Book Description
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.
