Non-parametric econometrics
Author(s)
Bibliographic Information
Non-parametric econometrics
（Practical econometrics / series editors, Jurgen Doornik and Bronwyn Hall）
Oxford University Press, 2010
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Note
Includes bibliographical references (p. 148-157) and index
Description and Table of Contents
Description
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.
Table of Contents
- 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
by "Nielsen BookData"