*! version 1.0.1 14apr2020

Title

plotprocess -- Visualizing sequences of quantile regressions or distribution regression coefficients

Syntax

plotprocess [namelist] [, intervaltype level(#) graph_options ]

where namelist is a list of variables that appear in the current estimation results.

intervaltype

pointwise requires that only the pointwise confidence intervals are added to the plot, the default if the functional bands have not been estimated.

uniform requires that only the uniform confidence bands are added to the plot.

both requires that only the uniform confidence bands are added to the plot, the defulat if the functional bands have been estimated.

none requires that only the uniform confidence bands are added to the plot.

level

level(#) specifies the confidence level, as a percentage, for confidence intervals.

graph_options

lcolor(colorstyle) color of the line showing the point estimates.

pcolor(colorstyle) color of the pointwise confidence interval.

ucolor(colorstyle) color of the uniform confidence bands.

legend(legend_options) legend for the uniform bands, pointwise intervals and coefficients; "off" to suppress the legend.

title(title) title(s); the titles for different coefficients can be separated by ||; "off" to suppress titles.

xtitle(axis_title) specify x axis title(s); the titles for different coefficients can be separated by ||; "off" to suppress titles.

ytitle(axis_title) specify y axis title(s); the titles for different coefficients should be separated by ||; "off" to suppress titles.

other_graph_options:(options) options that will be passed directly to twoway.

combine_options:(options) options that will be passed directly to graph combine.

Description

plotprocess conveniently plots the
estimated quantile regression or distribution regression coefficients with their confidence intervals and uniform
bands. The display of the results in form of graphs is more informative than
tabulation when a large number of QR or DR has been estimated. A single call
of this function will automatically combine the values saved by the last
call of qrprocess or drprocess and produce a graphic that
visualizes the coefficients and allows for testing directly hypotheses. Many
options can be used to customize the figures.

For more detailed information about the Stata commands, please refer to Chernozhukov, Fernández-Val and Melly (2020b).

Examples

Use the cps91 dataset

. use http://www.stata.com/data/jwooldridge/eacsap/cps91

Estimate the discretized quantile regression process for the 0.1,0.02,...,0.9 quantiles, do not print the results

. qrprocess lwage c.age##c.age i.black i.hispanic educ, quantile(0.1(0.01)0.9) noprint

Plot all the coefficients

. plotprocess

Plot the coefficient on education, change the title, suppress the legend, change the colors

. plotprocess educ, title("Education (in years)") legend(off) pcolor(midblue) lcolor(edkblue)

Plot the coefficient on education, change the axis options by passing them to the option other_graph_options

. plotprocess educ, title("Education") other(xlabel(0.1(0.1)0.9))

Plot the coefficients on age and age squared, change the way both graphs are displayed by passing them to the option combine

. plotprocess age c.age#c.age, title("Age (in years)"||"Age squared") combine(rows(2) iscale(0.6))

Estimate the same process, activate functional inference, use the multiplier bootstrap with 500 replications

. qrprocess lwage c.age##c.age i.black i.hispanic educ, quantile(0.1(0.01)0.9) functional vce(multiplier, reps(500)) noprint

Plot the coefficient for education with uniform and pointwise confidence bands

. plotprocess educ, ytitle("QR coefficent") title("Years of education")

Plot the coefficient for education with only the uniform confidence bands

. plotprocess educ, uniform ytitle("QR coefficent") title("Years of education")

Estimate 100 distribution regression with the logit one-step estimator with functional inference based on the multiplier bootstrap and 500 replications

. drprocess lwage c.age##c.age i.black i.hispanic educ, functional method(logit, onestep) vce(multiplier, reps(500))

Plot all the coefficients

. plotprocess

Plot the coefficient on education

. plotprocess educ, ytitle("DR coefficent") title("Years of education")

References

Chernozhukov, V., I. Fernández-Val, and B. Melly. 2020b. Quantile and distribution regression in Stata: algorithms, pointwise and functional inference. Working paper.

Remarks

This is a preliminary version. Please feel free to share your comments, reports of bugs and
propositions for extensions.

If you use this command in your work, please cite Chernozhukov, Fernández-Val and Melly (2020).

Authors

Victor Chernozhukov, Iván Fernández-Val and Blaise Melly

MIT, Boston University and University of Bern

mellyblaise@gmail.com