Nonparametric Econometrics Themes in Modern Econometrics 9780511612503 Economics Books
Download As PDF : Nonparametric Econometrics Themes in Modern Econometrics 9780511612503 Economics Books 
This book systematically and thoroughly covers a vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the past five decades. Within this framework, this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g., regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of modern nonparametric approach. The book should provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.
Nonparametric Econometrics Themes in Modern Econometrics 9780511612503 Economics Books
Nonparametrics seems to be one of the most promising fields in econometrics. All econometricians should be aware of that and try to learn the basic tools. This book is a great beginning (perhaps you should read the chapter of nonparametrics in Johnston and Dinardo's "Econometric Methods" to get used to the very basic concepts). The manual contains practically all the stuff that has been done in the field. It begins pretty fast with the kernel estimation method and, by page 19, you will be face to nonparametric derivatives estimation equations. In the introduction there is a clear explanation of the difference between parametrics and nonparametrics; you will also learn the main basic methods and concepts, such as the nearest Neighborhood Estimator and the window's size problem. After that, you'll have to read about the statistical properties (finite sample and asymptotics) of the estimators. There is also a lot of stuff of semiparametric methods. You shouldn't expect an extremely easy-to-read manual, because nonparametrics is a pretty complex subject. The first 50 pages are easy and fun to read. You'll get excited by learning such interesting theory. But then, the hard topics begin and if you want to understand them all, you'll have to make a big effort. Not overwhelmingly complicated, neither elementary, this book is an excellent reference in the field, but I advice you to have two or three more books of the same subject (Hardle, for example) so you can understand faster some of the developments presented. A fairly good mathematical and probability knowledge is required.Product details
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Tags : Nonparametric Econometrics (Themes in Modern Econometrics): 9780511612503: Economics Books @ Amazon.com,Adrian Pagan, Aman Ullah,Nonparametric Econometrics (Themes in Modern Econometrics),Cambridge University Press,0511612508,BUSINESS & ECONOMICS Econometrics,Econometrics
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Nonparametric Econometrics Themes in Modern Econometrics 9780511612503 Economics Books Reviews
I just started reading it, and I love the clear exposition of the book. Its a very fast-growing field, so don't expect this book to be the last word on the subject. Still, it's a must for an advanced graduate student in econometrics in need of a good introduction to non-parametric estimation.
I was compelled to buy this book because I am a practitioner who needed to know what it means to use a kernel regression to separate the trend from the cycle in macroeconomic statistics. I have been out of graduate school for a while now, and I am not up to speed on non-parametric techniques in econometrics.
The authors are adept at introducing the subject, and they use the natural logical progression first they show how to estimate a density; and then they lead the reader to think of a kernel regression as the conditional moment of a bivariate density.
The author's notation confused me because to estimate a density is really to estimate a function at each of the points in its domain. Sometimes the authors made this point clear and sometimes their arguments obfuscated this subtlety.
A strength seems to be the breadth of the bibliographic references. Another strength seems to be the empirical applications that follow the cursory theoretical discussions.
The book will appeal to a middle-brow reader like myself, but mathematicians and good statisticians may find the analysis a trifle loose.
Nonparametrics seems to be one of the most promising fields in econometrics. All econometricians should be aware of that and try to learn the basic tools. This book is a great beginning (perhaps you should read the chapter of nonparametrics in Johnston and Dinardo's "Econometric Methods" to get used to the very basic concepts). The manual contains practically all the stuff that has been done in the field. It begins pretty fast with the kernel estimation method and, by page 19, you will be face to nonparametric derivatives estimation equations. In the introduction there is a clear explanation of the difference between parametrics and nonparametrics; you will also learn the main basic methods and concepts, such as the nearest Neighborhood Estimator and the window's size problem. After that, you'll have to read about the statistical properties (finite sample and asymptotics) of the estimators. There is also a lot of stuff of semiparametric methods. You shouldn't expect an extremely easy-to-read manual, because nonparametrics is a pretty complex subject. The first 50 pages are easy and fun to read. You'll get excited by learning such interesting theory. But then, the hard topics begin and if you want to understand them all, you'll have to make a big effort. Not overwhelmingly complicated, neither elementary, this book is an excellent reference in the field, but I advice you to have two or three more books of the same subject (Hardle, for example) so you can understand faster some of the developments presented. A fairly good mathematical and probability knowledge is required.
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