Dear R-users, I am fitting a kernel regression model of the form y ~ x1 + factor(x2) + factor(x3) and am using the function npregbw in the np-package to find the optimal bandwidths.
My dataset is relatively large and the optimization takes quite long. When testing different specifications I have noticed that the optimal bw for x3 is always very close to zero (around 10^-12 or so). I am wondering whether it is possible to hard code the bandwidth related to x3 to 0 and limit npregbw's choice of bw's those related to x1 and x2? My intuition suggests that this would reduce the number of parameters to be optimized from 3 to 2 and thus make the computations quicker. Furthermore the theoretical literature (e.g. [1]) seems to suggest that this might be a good idea with categorical variables and big datasets. Any comments? [1] Racine, J.S. and Q. Li (2004), "Nonparametric estimation of regression functions with both categorical and continuous Data," Journal of Econometrics, 119, 99-130. Best regards, ---- Otto Kassi University of Helsinki Dept. of Economics ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
