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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.

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