wnw_pdf: Weighted Nadaraya Watson Estimator of Coditional PDF In ygeunkim/ceshat: Nonparametric Estimation of Conditional Expected Shortfall

Description

This function estimates conditional pdf using WDKLL method.

Usage

  1 2 3 4 5 6 7 8 9 10 11 wnw_pdf( formula, data, wt, nw_kernel = c("Gaussian", "Epanechinikov", "Tricube", "Boxcar"), nw_h, h0, init = 0, eps = 1e-05, iter = 1000 ) 

Arguments

 formula an object class formula. data an optional data to be used. wt weights for WNW. Computing in prediction step will help efficiency. nw_kernel Kernel for weighted nadaraya watson nw_h Bandwidth for WNW h0 Binwidth init initial value for finding lambda eps small value iter maximum iteration when finding lambda

Details

Since standalone LL or WNW does not fully satisfy the conditions of cdf, Cai et al (2008) proposed to use WNW in LL scheme.

\hat{f}_c(y \mid x) = ∑_{t = 1}^n W_{c,t}(w, h) K_{h_0}(y - Y_t)

Value

Conditional pdf function of (y, x). y can be a numeric vector.

References

Cai, Z., & Wang, X. (2008). Nonparametric estimation of conditional VaR and expected shortfall. Journal of Econometrics, 147(1), 120-130.

ygeunkim/ceshat documentation built on Dec. 16, 2019, 12:39 p.m.