search for: spadi

Dear all, I am trying to use the np package for the estimation of a model with Klein and Spady's semiparametric estimator. When though, I include a constant term ( a column with 1s in X) then the following message appear: Multistart 1 of 3...Error in optim(c(beta, h), fn = optim.fn, gr = NULL, method = optim.method, : non-finite value supplied by optim So, how can I include a constant

Dear all, I would like to estimate the standard errors of Klein and Spady's estimator for that I am using: library(np) N<-100 X<-matrix(c(rnorm(N,1,1), rnorm(N,0,1)), ncol=2) BETA <-matrix(1,2,1) Z<-X%*%BETA L<-rlogis(N,location=0, scale=1) Y <-as.vector(X%*%BETA+L>=0)*1 KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady",

Dear R-users, I am very enthusiastic about the capacities offered by the np package and pretty fond of it. Nevertheless, trying to estimate Ichimura and Klein and Spady models on my data, I would like to be able to provide the npindex function with my guesses for the bandwidth (eventually computed by other means). The fact that the option 'bandwidth.compute' in the function npindexbw

I have data that comes in every 7 seconds or so and I'd like to display it in a spreadsheet, and possibly take user input from the spreadsheet. I have installed RGnumeric and written an appropriate R function that reads, manipulates and displays the data by writing via RGnumeric to a spreadsheet. However, the results of this are not displayed until the R function returns, so refreshing by

Dear all, I would like to create a code for semiparametric Klein and Spady's estimator. For that I created a function that provides the log-likelihood function for each observation (so it is a function of betas and i, where i denotes the observation). Now, in order to maximize the log-likelihood function, I have to sum these log-likelihood functions for each i and so to get another function

Hello everyone, Recently, I have encountered a problem about estimation of binary response model,as I try to estimate the survival probability of firms. I do not want to use the traditional binary respose models, such as Probit model or Logit model, since they are too restricted in their function form. Therefore, I seek to adopt some semiparametric methods, precisely estimation methods of

Dear R-Help, I am Deniz. I am currently trying to replicate a semiparametric sample selection paper and I am working on Klein and Spady estimator. I am using the npindex() and npindexbw() functions. The problem is, I need results for single bandwidth and when I set bandwidth computation to "FALSE" mode, R is not optimizing anything. Here is the code I am using:

Dear all, I am using np package in order to estimate a model with Klein and Spady estimator. To estimate the model I use KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2) and to estimate beta hats standard errors I use KSi <- npindex(KS, gradients=T, boot.num=300) vcov(KSi) This is

Dear list, I am using the np package. With the npindex function I estimate a semiparametric single index model using the method of Klein-Spady. P(Z=1|X) = G(X?b) I don?t have any problems to calculated the fitted values and standard errors X?b: bw = npindexbw(xdat=x, ydat=y_bi, method="kleinspady", nmulti=2) model = npindex(bws= bw3, gradients= TRUE, residuals = TRUE, boot.num =

Dear R users, An updated version of the np package has recently been uploaded to CRAN (version 0.14-1). The package is briefly described in a recent issue of Rnews (October, 2007, http://cran.r-project.org/doc/Rnews/Rnews_2007-2.pdf) for those who might be interested. A somewhat more detailed paper that describes the np package is forthcoming in the Journal of Statistical Software

Dear R users, An updated version of the np package has recently been uploaded to CRAN (version 0.14-1). The package is briefly described in a recent issue of Rnews (October, 2007, http://cran.r-project.org/doc/Rnews/Rnews_2007-2.pdf) for those who might be interested. A somewhat more detailed paper that describes the np package is forthcoming in the Journal of Statistical Software