search for: kleinspady

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 fine so far, but if I want to estimate the bootstrapped standard errors on estimates by se(KSi) then the res...

...instance, #Simulation n <- 200 x1 <- runif(n, min=-1, max=1) x2 <- runif(n, min=-1, max=1) y <- ifelse(x1 + x2 + rnorm(n) > 0, 1, 0) #Klein and Spady estimation, letting np compute the bandwidth npindexbw(bws=c(1,1,.5),xdat=cbind(x1,x2),ydat=y,bandwidth.compute=TRUE,method="kleinspady") #Works fine! #Klein and Spady estimation, attempting to constrain the bandwidth to .5 npindexbw(bws=c(1,1,.5),xdat=cbind(x1,x2),ydat=y,bandwidth.compute=FALSE,method="kleinspady") #Returns a result with the starting values: no estimation has been done... Do you know how (or wheth...

...uot; mode, R is not optimizing anything. Here is the code I am using: rp_ksbw<-npindexbw(bws=c(1, -0.1, -0.08, 0.06, -0.14, -0.02, 0.3), formula=(rp$participation ~ (AGE + rp$CHILD + rp$YCHILD + rp$HW + rp$EDU + AGE2)), bandwidth.compute=FALSE, optim.method="Nelder-Mead", method="kleinspady") and it is giving me exactly the same values I specified. Here is the result I got: Single Index Model Regression data (2339 observations, 6 variable(s)): AGE rp$CHILD rp$YCHILD rp$HW rp$EDU AGE2 Beta: 1 -0.1 -0.08 0.06 -0.14 -0.02 Bandwidth: 0.3 Optimisation Method: N...

...39;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", ckertype="epanechnikov" ) KSi <- npindex(KS, errors=TRUE) se(KSi) But then I get as a result a vector Nx1, which I do not understand what it is, and if I let errors=FALSE then I get a NA as a result. So, how can I get the standard error of the estimated coefficient? Thank yo...

...ods) The code that I run is: library(np) N<-250 q<-2 BETA<-matrix(1,3,1) X<-matrix(c(rnorm(N,0,1), rnorm(N,1,1)), ncol=q) X<-cbind(X,1) 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", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2) Thank you Dimitris -- View this message in context: http://r.789695.n4.nabble.com/error-in-optimization-when-I-include-constant-term-in-Klein-and-Spady-np-package-tp3692728p3692728.html Sent from the R help mailing list ar...

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 = 50) x_fit = predict(model, se.fit = TRUE) x_fit_bi= x_fit$fit x_fit_bi_se = x_fit$se.fit However, I also would like to obtain an estimate of G(X?b). For example, after estimating a probit model, it would simpl...