fv.yx: fv.yx

In bcallaway11/qrme: Quantile Regression with Measurement Error

Description

Return the density of the measurement error conditional on y and x; this takes as given some QR parameters from Y* (the true outcome) conditional on X. Here, we also presume that the distribution of the measurement error is a mixture of normal distributions

Usage

fv.yx(v, betmat, m, pi, mu, sig, Y, X, tau)

Arguments

v	A particular value of the measurement error to estimate f(v|y,x)
betmat	LxK matrix of parameter values with L the number of quantiles and K the dimension of the covariates
m	The dimension of the measurement error
pi	The probability of each mixture component (should have length equal to m)
mu	The mean of each mixture component (should have length equal to m)
sig	The standard deviation of each mixture component (should have length equal to m)
Y	An nx1 vector of outcomes
X	An nxK matrix of covariates
tau	an L-vector of all the quantiles where betas were estimated

Value

n x 1 vector of f(v|Y,X)

bcallaway11/qrme documentation built on June 30, 2021, 12:52 p.m.