RQ: RATS module to generate regression quantile

Eric Blankmeyer ()
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Eric Blankmeyer: Texas State University

Statistical Software Components from Boston College Department of Economics

Abstract: rq.src computes a regression quantile [R. Koenker and G. Bassett (1978) "regression quantiles" in Econometrica, 46, 33-50]. Just as least-squares estimates the average value of the dependent variable for specified values of the independent variables, the 0.5 quantile (the fiftieth percentile) estimates the median of the dependent variable for specified values of the independent variables. in general, the user chooses "quant" (0 < quant < 1) to estimate any desired quantile of the dependent variable. The procedure is designed for linear models; it cannot handle models which are intrinsically nonlinear in the unknown parameters. rq.src uses the rats function "find minimum" to approximate a regression quantile. Yo obtain good initial values for this function, rq.src first does a search over many "elemental subsets" of the sample; each subset contains as many observations as there are unknown parameters. the option "iterations" specifies how many subsets are to be examined. the default, 3000 subsets, should be adequate for most regression models.

Language: RATS
Keywords: quantile; regression (search for similar items in EconPapers)
Date: 1999-04-20

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http://fmwww.bc.edu/repec/bocode/r/rq.src program code (text/plain)

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Persistent link: http://EconPapers.repec.org/RePEc:boc:bocode:r942001

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