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Applied Nonparametric Regression

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  • Härdle,Wolfgang

Abstract

Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs have made curve estimation possible. This volume focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds. Härdle argues that all smoothing methods are based on a local averaging mechanism and can be seen as essentially equivalent to kernel smoothing. To simplify the exposition, kernel smoothers are introduced and discussed in great detail. Building on this exposition, various other smoothing methods (among them splines and orthogonal polynomials) are presented and their merits discussed. All the methods presented can be understood on an intuitive level; however, exercises and supplemental materials are provided for those readers desiring a deeper understanding of the techniques. The methods covered in this text have numerous applications in many areas using statistical analysis. Examples are drawn from economics as well as from other disciplines including medicine and engineering.

Suggested Citation

  • Härdle,Wolfgang, 1992. "Applied Nonparametric Regression," Cambridge Books, Cambridge University Press, number 9780521429504, January.
    • Handle: RePEc:cup:cbooks:9780521429504
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    Cited by:

    1. Dorn, Sabrina & Egger, Peter, 2015. "On the distribution of exchange rate regime treatment effects on international trade," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 75-94.
    2. Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
    3. Cristian Aedo & Sergio Nuñez, 2004. "Efectos de las políticas de capacitación en América Latina y el Caribe: el caso del Programa Joven," Research Department Publications 3176, Inter-American Development Bank, Research Department.
    4. Ashis K. Gangopadhyay & Robert disario & Dipak K. Dey, 1997. "A nonparametric approach to k-sample inference based on entropy-super-," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 8(3), pages 237-252, September.
    5. Miles Corak & Andrew Heisz, 1999. "The Intergenerational Earnings and Income Mobility of Canadian Men: Evidence from Longitudinal Income Tax Data," Journal of Human Resources, University of Wisconsin Press, vol. 34(3), pages 504-533.
    6. Jan Eeckhout, 2004. "Gibrat's Law for (All) Cities," American Economic Review, American Economic Association, vol. 94(5), pages 1429-1451, December.
    7. Rama CONT, 1998. "Beyond implied volatility: extracting information from option prices," Finance 9804002, University Library of Munich, Germany.
    8. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    9. B. Wade Brorsen & John Coombs & Kim Anderson, 1995. "The cost of forward contracting wheat," Agribusiness, John Wiley & Sons, Ltd., vol. 11(4), pages 349-354.
    10. Aedo, Cristían & Nuñez, Sergio, 2004. "The Impact of Training Policies in Latin America and the Caribbean: The Case of Programa Joven," IDB Publications (Working Papers) 3287, Inter-American Development Bank.
    11. Cristian Aedo & Sergio Nuñez, 2004. "The Impact of Training Policies in Latin America and the Caribbean: The Case of Programa Joven," Research Department Publications 3175, Inter-American Development Bank, Research Department.
    12. Gabor Nagy & Gergo Barta & Tamas Henk, 2015. "Portfolio optimization using local linear regression ensembles in RapidMiner," Papers 1506.08690, arXiv.org.
    13. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2016. "Disentangling temporal patterns in elasticities: A functional coefficient panel analysis of electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 232-243.
    14. Dora L. Costa & Matthew E. Kahn, 2004. "Changes in the Value of Life, 1940--1980," Journal of Risk and Uncertainty, Springer, vol. 29(2), pages 159-180, September.
    15. Jaromír Kukal & Tran Van Quang, 2014. "Neparametrický heuristický přístup k odhadu modelu GARCH-M a jeho výhody [Estimating a GARCH-M Model by a Non-Parametric Heuristic Method and Its Advantages]," Politická ekonomie, Prague University of Economics and Business, vol. 2014(1), pages 100-116.
    16. Mangalova, Ekaterina & Shesterneva, Olesya, 2016. "Sequence of nonparametric models for GEFCom2014 probabilistic electric load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1023-1028.
    17. Lionel Page & Robert T. Clemen, 2013. "Do Prediction Markets Produce Well‐Calibrated Probability Forecasts?-super-," Economic Journal, Royal Economic Society, vol. 123(568), pages 491-513, May.
    18. D. K. Ginther, "undated". "A nonparametric analysis of the U.S. earnings distribution," Institute for Research on Poverty Discussion Papers 1067-95, University of Wisconsin Institute for Research on Poverty.
    19. Fortin, Nicole M., 1997. "L’impact des règles de prêts hypothécaires sur l’offre de travail des femmes au Canada : évidence paramétrique et non paramétrique," L'Actualité Economique, Société Canadienne de Science Economique, vol. 73(1), pages 129-159, mars-juin.
    20. Marco Modica, 2014. "Does the EU have homogeneous urban structure area? The role of agglomeration and the impact of shocks on urban structure," ERSA conference papers ersa14p229, European Regional Science Association.
    21. Čížek, Pavel, 2004. "(Non) Linear Regression Modeling," Papers 2004,11, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    22. Emre Barut & Warren Powell, 2014. "Optimal learning for sequential sampling with non-parametric beliefs," Journal of Global Optimization, Springer, vol. 58(3), pages 517-543, March.
    23. Dietmar P. J. Leisen, 2017. "The shape of small sample biases in pricing kernel estimations," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 943-958, June.
    24. Noyan Aydin Taner Akmercan, 2016. "Forecasting of Households Consumption Expenditure with Nonparametric Regression: The Case of Turkey," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 19(2), pages 19-32, November.
    25. Cristian Aedo, "undated". "The Impact of Training Policies in Latin America and the Caribbean: The Case of "Programa Joven"," ILADES-UAH Working Papers inv131, Universidad Alberto Hurtado/School of Economics and Business.

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