Revision

**616fe292ea4c66f8db35d04c9fa045fea56eea02**authored by Roger Koenker on**04 September 2016, 13:02:15 UTC**, committed by cran-robot on**04 September 2016, 13:02:15 UTC****1 parent**db6345a

rq.fit.panel.R

```
rq.fit.panel <- function(X,y,s,w=c(.25,.5,.25),taus=(1:3)/4,lambda = 1){
# prototype function for panel data fitting of QR models
# the matrix X is assumed to contain an intercept
# the vector s is a strata indicator assumed (so far) to be a one-way layout
# NB:
# 0. This is an altered version from that originally posted -- the definition
# of the rhs vector now incorporates a factor of 1/2 for the penalty.
# 1. The value of the shrinkage parameter lambda is an open research problem in
# the simplest homogneous settings it should be the ratio of the scale parameters
# of the fixed effects and the idiocyncratic errors
# 2. On return the coefficient vector has m*p + n elements where m is the number
# quantiles being estimated, p is the number of colums of X, and n is the
# number of distinct values of s. The first m*p coefficients are the
# slope estimates, and the last n are the "fixed effects"
# 3. Like all shrinkage (regularization) estimators, asymptotic inference is somewhat
# problematic... so the bootstrap is the natural first resort.
require(SparseM)
require(quantreg)
K <- length(w)
if(K != length(taus))
stop("length of w and taus must match")
X <- as.matrix(X)
p <- ncol(X)
n <- length(levels(as.factor(s)))
N <- length(y)
if(N != length(s) || N != nrow(X))
stop("dimensions of y,X,s must match")
Z <- as.matrix.csr(model.matrix(~as.factor(s)-1))
Fidelity <- cbind(as(w,"matrix.diag.csr") %x% X,w %x% Z)
Penalty <- cbind(as.matrix.csr(0,n,K*p),lambda*as(n,"matrix.diag.csr"))
D <- rbind(Fidelity,Penalty)
y <- c(w %x% y,rep(0,n))
a <- c((w*(1-taus)) %x% (t(X)%*%rep(1,N)),
sum(w*(1-taus)) * (t(Z) %*% rep(1,N)) + lambda * rep(1/2,n))
rq.fit.sfn(D,y,rhs=a)
}
```

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