R: Summary for Quantile Regression Tranformation Models

summary.rqt {Qtools}

R Documentation

Summary for Quantile Regression Tranformation Models

Description

This functions gives a summary list for a quantile regression transformation model.

Usage

## S3 method for class 'rqt'
summary(object, alpha = 0.05, se = "boot", R = 50,
	sim = "ordinary", stype = "i", conditional = FALSE, ...)

Arguments

`object`	an object of `class` `rqt`.
`alpha`	numeric value to determine the confidence level `(1-alpha)` of the required interval.
`se`	specifies the method used to compute standard errors. For conditional inference (`conditional = TRUE`), see argument `se` in `summary.rq`. For unconditional inference (`conditional = FALSE`), see details below.
`R`	number of bootstrap replications.
`sim`	see argument `sim` in `boot`.
`stype`	see argument `stype` in `boot`.
`conditional`	logical flag. If `TRUE`, the transformation parameter is assumed to be known and conditional inference is carried out.
`...`	if `conditional = TRUE`, additional arguments for `summary.rq` in package `quantreg`. If `conditional = FALSE`, additional arguments for `boot` in package `boot`.

Details

If inference is carried out conditionally on the transformation parameter (ie, assuming this is known rather than estimated), any type of summary for regression quantiles can be used (see summary.rq).

For unconditional inference (conditional = FALSE), there are three methods available: boot for bootstrap; iid for large-n approximation of the standard errors under IID assumptions; nid for large-n approximation of the standard errors under NID assumptions. See Powell (1991), Chamberlain (1994) and Geraci and Jones (2015).

Author(s)

Marco Geraci

References

Canty A and Ripley B (2014). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-11.

Chamberlain G. Quantile regression, censoring, and the structure of wages. In: Sims C, editor. Advances in Econometrics: Sixth World Congress. 1. Cambridge, UK: Cambridge University Press; 1994.

Davison AC and Hinkley DV (1997). Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge.

Geraci M and Jones MC. Improved transformation-based quantile regression. Canadian Journal of Statistics 2015;43(1):118-132.

Mu YM, He XM. Power transformation toward a linear regression quantile. Journal of the American Statistical Association 2007;102(477):269-279.

Powell JL. Estimation of monotonic regression models under quantile restrictions. In: Barnett W, Powell J, Tauchen G, editors. Nonparametric and Semiparametric Methods in Econometrics and Statistics: Proceedings of the Fifth International Symposium on Economic Theory and Econometrics. New York, NY: Cambridge University Press 1991. p. 357-84.