Francisco-Fernández, MarioVilar, Juan M.2007-06-252007-06-252001Communications in statistics, theory and methods, vol. 30, n. 7, pp. 1271-1293.0361-0926http://hdl.handle.net/2183/856In this paper, we study the nonparametric estimation of the regression function and its derivatives using weighted local polynomial fitting. Consider the fixed regression model and suppose that the random observation error is coming from a strictly stationary stochastic process. Expressions for the bias and the variance array of the estimators of the regression function and its derivatives are obtained and joint asymptotic normality is established. The influence of the dependence of the data is observed in the expression of the variance. We also propose a variable bandwidth selection procedure. A simulation study and an analysis with real economic data illustrate the proposed selection method.application/pdfengThis is a preprint of an article submitted for consideration in the Communications in statistics, theory and methods © 2001 copyright Taylor & Francis; Communications in statistics, theory and methods is available online at: http://www.informaworld.com/Nonparametric estimatorsLocal polynomial fittingAutoregressive processpreprintopen access