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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.

conditional dependence index --- Kendall’s tau --- leverage effect --- nonparametric copula --- tail dependence index --- volatility feedback effect --- TFP growth --- emissions --- materials balance condition --- semiparametric estimation --- nonparametric method --- conditional quantile function --- panel data --- financial development --- production efficiency --- nonparametric frontiers --- generalized additive models --- tensor products --- cubic spline penalty --- unit root testing --- wavelet --- GLS detrending --- difference kernel estimator --- integrated difference kernel estimator --- M-estimation --- Monte Carlo --- nonparametric threshold regression --- dependent Bayesian nonparametrics --- Dirichlet process prior --- slice sampling --- realised volatility --- heterogeneous autoregressive model --- purified implied volatility --- classification --- random forests --- machine learning --- functional coefficients --- local linear regression --- nonparametric 2SLS estimator --- series estimator --- Solow economic growth convergence model --- maximum score estimator --- discrete duration models --- efficient semiparamteric estimation --- competitiveness --- country competitiveness index --- DEA --- efficiency --- European Union --- factors --- indicators --- Malmquist productivity index