He folks,
Thank you very much so far. Of course this forum is not intended to be a
substitute for reading the literature, maybe I just posed the question in a
way to general. I understand that rq.anovar produces the wald-test proposed
in
Tests of Linear Hypotheses and L1 Estimation (Koenker,
Dear all,
I know that my question is somewhat special but I tried several times to
solve the problems on my own but I am unfortunately not able to compute the
following test statistic using the quantreg package. Well, here we go, I
appreciate every little comment or help as I really do not know
Dear all,
I am searching for a way to compute a test comparable to Chuang et al.
(Causality in Quantiles and Dynamic Stock
Return-Volume Relations). The aim of this test is to check wheter the
coefficient of a quantile regression granger-causes Y in a quantile range. I
have nearly computed
He folks,
I want to use quantile regression for doing a test of symmetrie of a
distribution. Following Buchinsky I want to test, whether the square of \tau
= \beta(p)+\beta(1-p)-2*\beta(0.5) (\beta(\tau) is the estimated slope
parameter for quantile \tau).Unfortunately I do not know how to
He folks=)
I am trying to compute a supWald Test in R, trying to show that a subset of
my regressors is significantly different from 0 but I am not able to compute
this test. My sample looks as follows: I am regressing fit1 -
(Y~X1+X2+X3,tau=tau). I know that if I want to show that e.g. X2 is
He folks=)
I want to check whether a coefficient has an impact on a quantile regression
(by applying the sup-wald test for a given quantile range [0.05,0.95].
Therefore I am doing the following calculations:
a=0;
for (i in 5:95/100){
fitrestricted=rq(Y~X1+X2,tau=i)
Hi Folks,
I am currently trying to present some results I obtained by using the
quantreg package developed by Roger Koenker. After calculating
fit-summary(rq(Y~X1+X2, tau=2:98/100) ) the function plot(fit) presents a
really nice the results by showing the values for all regressors in the
given
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