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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 have yet to see a mention of standard error choice.

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.

The summary.rq function from the quantreg vignette provides a multitude of choices for standard error estimates of quantile regression. 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 have yet to see a mention of standard error choice.

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 have yet to see a mention of standard error choice.

The summary.rq function from the quantreg vignette provides a multitude of choices for standard error estimates of quantile regression. 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.

The summary.rq function from the quantreg vignette provides a multitude of choices for standard error estimates of quantile regression. 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 have yet to see a mention of standard error choice.