Applying Kernel and Nonparametric Estimation to Economic Topics

Nonparametric estimation and inference is becoming increasingly popular in economics because of the advent of extensive computing power and the development of efficient computer algorithms. Papers in this volume present techniques that permit inference that is robust to deviations from conventional parametric assumptions. The volume is divided into two sections. The first section contains papers concerned with methodology while the second section contains papers that emphasize the application of nonparametric techniques to practical problems.

An out-of-sample, nonparametric test of the Martingale difference hypothesis (M.W. McCracken). On the finite-sample accuracy of nonparametric resampling algorithms for economic time series (J. Berkowitz, I. Birgean and L. Kilian). Semiparametric varying parameter panel data models: an application to estimation of speed of convergence (S. Kumar, A. Ullah). Specification testing and nonparametric estimation of the human capital model (J. Xu Zheng). A nonparametric approach to stochastic discount factor estimation (Fan Hu, A.R. Hall and D. Nychka). Applications. Nonparametric density estimation of the net benefits of Southern Illinois University on the State of Illinois by the human capital model (S. Grosskopf, B.W. Sloboda). The reaction of housing prices to information on superfund sites: a semiparametric analysis of the effects of neighborhood land uses on the house values by using kernel and spline regressions (S. Iwata, H. Murao and Q. Wang). Kernel density estimation and intergenerational transmission (D. O'Neill, O. Sweetman and D. Van de Gaer). Does job displacement explain wage inequality: a nonparametric examination (D.K. Ginther). Nonparametric analysis of growth in replenishable resource stocks (J.S. Racine, J.B. Smith). Nonparametric efficiency testing of Asian stock markets using weekly data (C. Los).

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