Nonparametric Econometric Methods and Application

• Format: eBook
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Subjects: classification; competitiveness; conditional dependence index; conditional quantile function; country competitiveness index; cubic spline penalty; DEA; dependent Bayesian nonparametrics; difference kernel estimator; Dirichlet process prior; discrete duration models; efficiency; efficient semiparamteric estimation; emissions; European Union; factors; financial development; functional coefficients; generalized additive models; GLS detrending; heterogeneous autoregressive model; indicators; integrated difference kernel estimator; Kendall's tau; leverage effect; local linear regression; M-estimation; machine learning; Malmquist productivity index; materials balance condition; maximum score estimator; Monte Carlo; nonparametric 2SLS estimator; nonparametric copula; nonparametric frontiers; nonparametric method; nonparametric threshold regression; panel data; production efficiency; purified implied volatility; random forests; realised volatility; semiparametric estimation; series estimator; slice sampling; Solow economic growth convergence model; tail dependence index; tensor products; TFP growth; unit root testing; volatility feedback effect; wavelet
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication

 The present Special Issue collects a number of new contributions both at the theoretical level and in terms of applications in the areas of nonparametric and semiparametric econometric methods. In particular, this collection of papers that cover areas such as developments in local smoothing techniques, splines, series estimators, and wavelets will add to the existing rich literature on these subjects and enhance our ability to use data to test economic hypotheses in a variety of fields, such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth, to name a few.