Semiparametric Regression For The Applied Econometrician


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Semiparametric Regression for the Applied Econometrician


Semiparametric Regression for the Applied Econometrician

Author: Adonis Yatchew

language: en

Publisher: Cambridge University Press

Release Date: 2003-06-02


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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.

Semiparametric Regression with R


Semiparametric Regression with R

Author: Jaroslaw Harezlak

language: en

Publisher: Springer

Release Date: 2018-12-12


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This easy-to-follow applied book on semiparametric regression methods using R is intended to close the gap between the available methodology and its use in practice. Semiparametric regression has a large literature but much of it is geared towards data analysts who have advanced knowledge of statistical methods. While R now has a great deal of semiparametric regression functionality, many of these developments have not trickled down to rank-and-file statistical analysts. The authors assemble a broad range of semiparametric regression R analyses and put them in a form that is useful for applied researchers. There are chapters devoted to penalized spines, generalized additive models, grouped data, bivariate extensions of penalized spines, and spatial semi-parametric regression models. Where feasible, the R code is provided in the text, however the book is also accompanied by an external website complete with datasets and R code. Because of its flexibility, semiparametric regression has proven to be of great value with many applications in fields as diverse as astronomy, biology, medicine, economics, and finance. This book is intended for applied statistical analysts who have some familiarity with R.

Applied Econometric Analysis Using Cross Section and Panel Data


Applied Econometric Analysis Using Cross Section and Panel Data

Author: Deep Mukherjee

language: en

Publisher: Springer Nature

Release Date: 2024-01-03


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This book is a collection of 20 chapters on chosen topics from cross-section and panel data econometrics. It explores both theoretical and practical aspects of selected cutting-edge techniques which are gaining popularity among applied econometricians, while following the motto of “keeping things simple”. Each chapter gives a basic introduction to one such method, directs readers to supplementary references, and shows an application. The book takes into account that—A: The field of econometrics is evolving very fast and leading textbooks are trying to cover some of the recent developments in revised editions. This book offers basic introduction to state-of-the-art techniques and recent advances in econometric models with detailed applications from various developing and developed countries. B: An applied researcher or practitioner may prefer reference books with a simple introduction to an advanced econometric method or model with no theorems but with a longer discussion on empirical application. Thus, an applied econometrics textbook covering these cutting-edge methods is highly warranted; a void this book attempts to fills.The book does not aim at providing a comprehensive coverage of econometric methods. The 20 chapters in this book represent only a sample of the important topics in modern econometrics, with special focus on econometrics of cross-section and panel data, while also recognizing that it is not possible to accommodate all types of models and methods even in these two categories. The book is unique as authors have also provided the theoretical background (if any) and brief literature review behind the empirical applications. It is a must-have resource for students and practitioners of modern econometrics.