Applied nonparametric econometrics:
The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2015
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| Subjects: | |
| Online Access: | BSB01 FHN01 UBG01 Volltext |
| Summary: | The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls |
| Item Description: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Physical Description: | 1 online resource (xii, 367 pages) |
| ISBN: | 9780511845765 |
| DOI: | 10.1017/CBO9780511845765 |
Staff View
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| 520 | |a The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls | ||
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| contents | Machine generated contents note: 1. Introduction; 2. Univariate density estimation; 3. Multivariate density estimation; 4. Testing; 5. Regression; 6. Testing; 7. Smoothing discrete variables; 8. Regression with discrete covariates; 9. Semiparametric methods; 10. Instrumental variables; 11. Panel data; 12. Constrained estimation and inference |
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| id | DE-604.BV043695111 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:32:43Z |
| institution | BVB |
| isbn | 9780511845765 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029107681 |
| oclc_num | 930540734 |
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| owner_facet | DE-473 DE-BY-UBG DE-12 DE-92 |
| physical | 1 online resource (xii, 367 pages) |
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| publishDate | 2015 |
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| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Henderson, Daniel J. Verfasser aut Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami Cambridge Cambridge University Press 2015 1 online resource (xii, 367 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) Machine generated contents note: 1. Introduction; 2. Univariate density estimation; 3. Multivariate density estimation; 4. Testing; 5. Regression; 6. Testing; 7. Smoothing discrete variables; 8. Regression with discrete covariates; 9. Semiparametric methods; 10. Instrumental variables; 11. Panel data; 12. Constrained estimation and inference The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls Econometrics Nonparametric statistics Nichtparametrisches Verfahren (DE-588)4339273-8 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Nichtparametrisches Verfahren (DE-588)4339273-8 s Ökonometrie (DE-588)4132280-0 s 1\p DE-604 Parmeter, Christopher F. Sonstige oth Erscheint auch als Druckausgabe 978-0-521-27968-0 Erscheint auch als Druckausgabe 978-1-107-01025-3 https://doi.org/10.1017/CBO9780511845765 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Henderson, Daniel J. Applied nonparametric econometrics Machine generated contents note: 1. Introduction; 2. Univariate density estimation; 3. Multivariate density estimation; 4. Testing; 5. Regression; 6. Testing; 7. Smoothing discrete variables; 8. Regression with discrete covariates; 9. Semiparametric methods; 10. Instrumental variables; 11. Panel data; 12. Constrained estimation and inference Econometrics Nonparametric statistics Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4339273-8 (DE-588)4132280-0 |
| title | Applied nonparametric econometrics |
| title_auth | Applied nonparametric econometrics |
| title_exact_search | Applied nonparametric econometrics |
| title_full | Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami |
| title_fullStr | Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami |
| title_full_unstemmed | Applied nonparametric econometrics Daniel J. Henderson, University of Alabama, Christopher F. Parmeter, University of Miami |
| title_short | Applied nonparametric econometrics |
| title_sort | applied nonparametric econometrics |
| topic | Econometrics Nonparametric statistics Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometrics Nonparametric statistics Nichtparametrisches Verfahren Ökonometrie |
| url | https://doi.org/10.1017/CBO9780511845765 |
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