Non-Parametric Econometrics

Hardcover | January 20, 2011

byIbrahim Ahamada, Emmanuel Flachaire

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This book allows those with a basic knowledge of econometrics to learn the main nonparametric and semiparametric techniques used in econometric modelling, and how to apply them correctly. It looks at kernel density estimation, kernel regression, splines, wavelets, and mixture models, andprovides useful empirical examples throughout. Using empirical application, several economic topics are addressed, including income distribution, wage equation, economic convergence, the Phillips curve, interest rate dynamics, returns volatility, and housing prices. A helpful appendix also explainshow to implement the methods using R. This useful book will appeal to practitioners and researchers who need an accessible introduction to nonparametric and semiparametric econometrics. The practical approach provides an overview of the main techniques without including too much focus on mathematical formulas. It also serves as anaccompanying textbook for a basic course, typically at undergraduate or graduate level.

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This book allows those with a basic knowledge of econometrics to learn the main nonparametric and semiparametric techniques used in econometric modelling, and how to apply them correctly. It looks at kernel density estimation, kernel regression, splines, wavelets, and mixture models, andprovides useful empirical examples throughout. Us...

Ibrahim Ahamada is Assistant Professor of Economics at the University Paris 1 Pantheon-Sorbonne and a member of the Paris School of Economics. Between 2002 and 2004, he held position at the Universite de la Reunion. He obtained his PhD in Economics from the Universite de la Mediterranee in 2002. Emmanuel Flachaire is Professor of Econ...
Format:HardcoverDimensions:176 pages, 9.21 × 6.14 × 0.1 inPublished:January 20, 2011Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199578001

ISBN - 13:9780199578009

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Table of Contents

1. Kernel Density Estimation2. Kernel Regression3. Spline Regression4. Wavelet Regression5. Semi-Parametric Regression Models6. Mixture ModelsAppendix: Implementation in R