  In this paper we consider the nonparametric estimation of density and
regression functions with non-negative support using a gamma kernel procedure
introduced by Chen (2000). Strong uniform consistency and asymptotic normality
of the corresponding estimators are established under a general ergodic
assumption on the data generation process. Our results generalize those of Shi
and Song (2016), obtained in the classic i.i.d. framework, and the works of
Bouezmarni and Rombouts (2008, 2010b) and Gospodinov and Hirukawa (2007) for
mixing time series.
