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help for ^clad^                                                   (STB-58: sg153)
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CLAD estimation with bootstrap standard errors
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       ^clad^ varlist [^if^ exp] [^in^ range] [^, r^eps^(^#^)^ ^psu(^varname^)^ ^ll^[^(^#^)^] ^ul^[^(^#^)^]
                    ^d^ots ^sa^ving^(^filename^)^ ^replace l^evel^(^#^)^
                    ^qu^antile^(^#^) it^erate^(^#^) wlsi^ter^(^#^)^]


Description
-----------

^clad^ calculates Powell's (1984) censored least absolute deviations
estimator (CLAD) and bootstrap estimates of its sampling variance.
The CLAD estimator is a generalization of the least absolute
deviations (LAD) estimator, which is implemented in Stata by @qreg@.
Unlike the standard estimators of the censored regression model such
as tobit or other maximum likelihood approaches, the CLAD estimator
is robust to heteroscedasticity and is consistent and asymptotically
normal for a wide class of error distributions. (See Arabmazar and
Schmidt, 1981, and Vijverberg, 1987, for empirical examples of the
magnitude of the bias resulting from the tobit in the presence of
non-normal error distributions.)

This program sidesteps the issue of programming analytical standard errors
and provides instead bootstrapped estimates of the sampling variance.
Rogers (1993) shows that the standard errors reported by Stata for @qreg@
are not robust to violations of homoscedasticity or independence of the
residuals and proposes a bootstrap alternative. We follow Rogers for the
CLAD estimator and propose two bootstrap estimates of the standard errors.
The first is the standard bootstrap which assumes that the sample was
selected using a simple random design. The second is a bootstrap estimate
which assumes that the sample was selected in two-stages and which replicates
the design by bootstrapping in two stages.

An advantage of the two-stage bootstrap estimates available in ^clad^ is that
if the sample was collected using a two-stage process, then the estimated
standard errors will be robust to this design effect. Kish (1995) and Cochran
(1997) show the importance of correcting mean values for design effects.
Scott and Holt (1982) show that the magnitude of the bias for the estimated
variance-covariance matrix for ordinary least squares estimates can be quite
large when it is erroneously assumed that the data were collected using a
simple random sample, if in fact a two-stage design had been used.


Options
-------

^reps(^#^)^ specifies the number of bootstrap replications to be performed. The
    default value is 100.

^psu(^varname^)^ specifies the variable identifying the primary sampling unit. If
    no variable is specified, then the bootstrap replication is a single-stage,
    simple random draw on the sample. If specified, the bootstrap replication
    is two-stage simple random draw on the sample.

^ll(^#^)^ and ^ul(^#^)^ are as in Stata's @tobit@ command and indicate the
    censoring point.  ^ll()^ indicates left censoring and ^ul()^ indicates
    right-censoring. If ^ll^ or ^ul^ is specified without a
    specific censoring value, then ^clad^ assumes that the lower limit is
    the minimum observed in the data (if ^ll^ is specified) and the upper
    limit is the maximum (if ^ul^ is specified). If nothing is specified
    for a lower or upper bound, ^clad^ assumes that the lower limit is zero.
    ^clad^ only functions with lower or upper censoring; you cannot specify
    censoring at both the lower and upper bound.

^dots^ prints a dot to the screen for each bootstrap replication,
    thereby allowing the user to estimate, after a few replications,
    the time to completion.

^saving(^filename^)^ creates a Stata data file (^.dta^ file) containing the
    bootstrap distribution for each statistic. Then, ^bstat^ can be run on the
    data in filename to view the bootstrap estimates again.

^replace^ overwrites the Stata data file specified in ^saving()^,
    if it already exists.

All other options are as specified in Stata's @qreg@ command.


Remarks
-------

The program does not use in calculations observations with missing values of
dependent variable or psu-variable. However, the user might want to take care
of missing values in exogenous variables to increase effectiveness of the
bootstrap procedure.


Examples
--------

	. ^clad lwage exp school^

	. ^clad lwage exp school, reps(500) psu(cluster)^

	. ^clad lwage exp school if rural==1, reps(500) psu(cluster) ll(1)^
          ^level(90) dots saving(c:\stata\data\boot1)^


Authors
-------

          Dean Jolliffe
          Economic Research Service, U.S. Department of Agriculture
          jolliffe@@ers.usda.gov

          Bohdan Krushelnytskyy 
          Czech Republic

          Anastassia Semykina 
          Czech Republic


Also see
--------

 Manual:  ^[R] qreg^, ^bstat^
    STB:  STB-58: sg153
On-line:  help for @qreg@, @bstat@