42
$\begingroup$

The summary.rq function from the quantreg vignette provides a multitude of choices for standard error estimates of quantile regression coefficients. What are the special scenarios where each of these becomes optimal/desirable?

  • "rank" which produces confidence intervals for the estimated parameters by inverting a rank test as described in Koenker (1994). The default option assumes that the errors are iid, while the option iid = FALSE implements the proposal of Koenker Machado (1999). See the documentation for rq.fit.br for additional arguments.

  • "iid" which presumes that the errors are iid and computes an estimate of the asymptotic covariance matrix as in KB(1978).

  • "nid" which presumes local (in tau) linearity (in x) of the the conditional quantile functions and computes a Huber sandwich estimate using a local estimate of the sparsity.

  • "ker" which uses a kernel estimate of the sandwich as proposed by Powell(1990).

  • "boot" which implements one of several possible bootstrapping alternatives for estimating standard errors.

I have read at least 20 empirical papers where this is applied either in the time-series or the cross-sectional dimension and haven't seen a mention of standard error choice.

Improve this question
$\endgroup$
5
  • 11
    $\begingroup$ I hope you get many responses to this excellent question. We need some guidance in this area. Another approach, made easy by the R rms package's bootcov function is to save the bootstrap replicate regression coefficients ($\beta$s) and to use the bootstrap nonparametric percentile confidence interval approach to get confidence intervals for any contrast (combination of $\beta$s) of interest. $\endgroup$
    – Frank Harrell
    Commented Dec 22, 2012 at 14:19
  • 1
    $\begingroup$ Excellent question, I was told in class "always use bootstrapping" but I'm not sure exactly why as I'm not familiar with the theory behind the other methods. $\endgroup$
    – Max Gordon
    Commented Dec 22, 2012 at 22:31
  • 4
    $\begingroup$ Did you go through the paper Koenker and Hallock(2000): Quantile Regression: An Introduction (econ.uiuc.edu/~roger/research/intro/rq.pdf)? Bootstrap is preferable because it makes no assumption about the distribution of response (p. 47, Quantile regressions, Hao and Naiman, 2007). Also, note that the "...assumptions for the asymptotic procedure usually do not hold, and even if these assumptions are satisfied, it is complicated to solve for the standard error of the constructed scale and skewness shifts (p. 43)..." $\endgroup$
    – Metrics
    Commented Dec 31, 2012 at 5:16
  • $\begingroup$ Doesn't bootstrap resampling assume that the uniform prior is non-informative? $\endgroup$
    – EngrStudent
    Commented Jun 29, 2013 at 14:16
  • $\begingroup$ @Metrics: Maybe you should post that as an answer? $\endgroup$
    – naught101
    Commented Sep 11, 2013 at 5:47

1 Answer 1

Reset to default
5
$\begingroup$

Did you go through the paper Koenker and Hallock(2000): Quantile Regression: An Introduction (econ.uiuc.edu/~roger/research/intro/rq.pdf)? Bootstrap is preferable because it makes no assumption about the distribution of response (p. 47, Quantile regressions, Hao and Naiman, 2007). Also, note that the "...assumptions for the asymptotic procedure usually do not hold, and even if these assumptions are satisfied, it is complicated to solve for the standard error of the constructed scale and skewness shifts (p. 43)..."

Improve this answer
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.