Non-Parametric Econometrics [Hardcover]

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, and provides 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 explains how 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 an accompanying textbook for a basic course, typically at undergraduate or graduate level.

1. Kernel Density Estimation
2. Kernel Regression
3. Spline Regression
4. Wavelet Regression
5. Semi-Parametric Regression Models
6. Mixture Models
Appendix: Implementation in R

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 Economics at Aix-Marseille University and a member of the GREQAM (Groupement de Recherche en Economie Quantitative d'Aix Marseille). Between 2001 and 2008, he taught at the University Paris 1 Pantheon-Sorbonnne, and at the Paris School of Economics. After obtaining his PhD in Economics from the Universite de la Mediterranee in 1998, he has held short research positions at CORE, Universite Catholique de Louvain, and the LondonlS(