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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. Joel L. Horowitz is the Charles E. and Emma H. Morrison Professor of Market Economics at Northwestern University. He is the author of over 100 journal articles and book chapters in econometrics and statistics, a winner of the Richard Stone prize in applied econometrics, a fellow of the Econometric Society and American Statistical Association, and a former co-editor of Econometrica.

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1. 2011-08-20Semiparametric and Nonparametric Methods in Econometrics
2. 2010-07-15Semiparametric and Nonparametric Methods in Econometrics

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1. Ebooks list page : 39034
2. 2011-08-20Semiparametric and Nonparametric Methods in Econometrics
3. 2011-08-20Semiparametric and Nonparametric Methods in Econometrics (repost)
4. 2010-12-17Semiparametric and Nonparametric Methods in Econometrics (repost))
5. 2010-11-30Semiparametric and Nonparametric Methods in Econometrics (ReUp)
6. 2010-07-15Semiparametric and Nonparametric Methods in Econometrics
7. 2017-11-21[PDF] Nonlinear Time Series: Semiparametric and Nonparametric Methods
8. 2009-08-01Nonlinear Time Series: Semiparametric and Nonparametric Methods (repost)
9. 2007-10-08Nonlinear Time Series: Semiparametric and Nonparametric Methods
10. 2017-10-20[PDF] Semiparametric and Nonparametric Econometrics
11. 2020-06-03Robust Rank-Based and Nonparametric Methods: Michigan, USA, April 2015: Selected, Revised, and Extended Contributions (Springer Proceedings in Mathematics & Statistics)
12. 2018-12-04Advanced Robust and Nonparametric Methods in Efficiency Analysis Methodology and Applications
13. 2017-10-09[PDF] Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications (Studies in Productivity and Efficiency) - Removed
14. 2017-02-09[PDF] Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications (Studies in Productivity and Efficiency)
15. 2013-11-12Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications [Repost]
16. 2009-02-15Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications
17. 2007-11-20Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications - Removed
18. 2007-11-12Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications - Removed
19. 2022-07-30Nonlinear Time Series Nonparametric and Parametric Methods - Removed
20. 2022-03-09Spatial Analysis Using Big Data Methods and Urban Applications (Spatial Econometrics and Spatial Statistics)

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