* 00000009
*! version 1.0.0
* Do not erase or edit this file
* It is used by Stata to track the ado and help
* files you have installed.

S http://fmwww.bc.edu/repec/bocode/e
N estout.pkg
D  8 Nov 2019
U 1
d 'ESTOUT': module to make regression tables
d 
d  estout produces a table of regression results from one or
d several models for use with spreadsheets, LaTeX, HTML, or a
d word-processor table. eststo stores a quick copy of the active
d estimation results for later tabulation. esttab is a wrapper for
d estout. It displays a pretty looking publication-style regression
d table without much typing. estadd adds additional results to the
d e()-returns for one or several models previously fitted and
d stored. This package subsumes the previously circulated esto,
d esta, estadd,  and estadd_plus. An earlier version of estout is
d available  as estout1.
d 
d KW: estimates
d KW: LaTeX
d KW: HTML
d KW: word processor
d KW: output
d 
d Requires: Stata version 8.2
d 
d Distribution-Date: 20190613
d 
d Author: Ben Jann, University of Bern
d Support: email jann@@soz.unibe.ch
d 
f _\_eststo.ado
f _\_eststo.hlp
f e\estadd.ado
f e\estadd.hlp
f e\estout.ado
f e\estout.hlp
f e\eststo.ado
f e\eststo.hlp
f e\estpost.ado
f e\estpost.hlp
f e\esttab.ado
f e\esttab.hlp
e
S http://fmwww.bc.edu/repec/bocode/m
N mediation.pkg
D  8 Nov 2019
U 2
d 'MEDIATION': module for causal mediation analysis and sensitivity analysis
d 
d  mediation estimates the role of particular causal mechanisms
d that mediate a relationship between treatment and outcome
d variables. Calculates causal mediation effects and direct effects
d for models with continuous or binary dependent variables using
d methods presented in Imai et al 2010. Also calculates sensitivity
d analyses for mediation effects that are necessary due to
d non-random assignment of mediating variable. Package replaces
d earlier approaches like the "Baron-Kenny" method and "Sobel test"
d for the case of continuous mediator and outcome variables,
d producing identical results as these earlier methods but not put
d in a causal inference framework and with sensitivity analyses to
d the key identification assumption. For models with binary
d mediators or outcomes, correct calculation of mediation effects
d are implemented that take into account the use of non-linear
d models such as probit.
d 
d KW: causal mediation effects
d KW: binary outcome
d KW: continuous outcome
d 
d Requires: Stata version 10 and moremata (q.v.)
d 
d Distribution-Date: 20121119
d 
d Author: Raymond Hicks, Niehaus Center for Globalization and Governance, Princeton University
d Support: email rhicks@@princeton.edu
d 
d Author: Dustin Tingley, Harvard University
d Support: email dtingley@@gov.harvard.edu
d 
f m\medeff.ado
f m\medeff.sthlp
f m\medsens.ado
f m\medsens.sthlp
e
S http://fmwww.bc.edu/repec/bocode/m
N moremata.pkg
D  8 Nov 2019
U 3
d 'MOREMATA': module (Mata) to provide various functions
d 
d  This package includes various Mata functions. kern(): various
d kernel functions; kint(): kernel integral functions; kdel0():
d canonical bandwidth of kernel; quantile(): quantile function;
d median(): median; iqrange(): inter-quartile range; ecdf():
d cumulative distribution function; relrank(): grade
d transformation; ranks(): ranks/cumulative frequencies; freq():
d compute frequency counts; histogram(): produce histogram data;
d mgof(): multinomial goodness-of-fit tests; collapse(): summary
d statistics by subgroups; _collapse(): summary statistics by
d subgroups; gini(): Gini coefficient; sample(): draw random
d sample; srswr(): SRS with replacement; srswor(): SRS without
d replacement; upswr(): UPS with replacement; upswor(): UPS without
d replacement; bs(): bootstrap estimation; bs2(): bootstrap
d estimation; bs_report(): report bootstrap results; jk():
d jackknife estimation; jk_report(): report jackknife results;
d subset(): obtain subsets, one at a time; composition(): obtain
d compositions, one by one; ncompositions(): determine number of
d compositions; partition(): obtain partitions, one at a time;
d npartitionss(): determine number of partitions; rsubset(): draw
d random subset; rcomposition(): draw random composition; greedy():
d one-to-one and one-to-many matching; ebal(): entropy balancing
d (Stata 11 required); colvar(): variance, by column; meancolvar():
d mean and variance, by column; variance0(): population variance;
d meanvariance0(): mean and population variance; mse(): mean
d squared error; colmse(): mean squared error, by column; sse():
d sum of squared errors; colsse(): sum of squared errors, by
d column; benford(): Benford distribution; cauchy(): cumulative
d Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density;
d cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy():
d inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial
d random numbers; cebinomial(): cond. expect. of binomial r.v.;
d root(): Brent's univariate zero finder; nrroot(): Newton-Raphson
d zero finder; finvert(): univariate function inverter;
d integrate_sr(): univariate function integration (Simpson's rule);
d integrate_38(): univariate function integration (Simpson's 3/8
d rule); ipolate(): linear interpolation; polint(): polynomial
d inter-/extrapolation; sqrt(): square root of a symmetric positive
d definite matrix; plot(): Draw twoway plot; _plot(): Draw twoway
d plot; panels(): identify nested panel structure; _panels():
d identify panel sizes; npanels(): identify number of panels;
d nunique(): count number of distinct values; nuniqrows(): count
d number of unique rows; isconstant(): whether matrix is constant;
d nobs(): number of observations; colrunsum(): running sum of each
d column; linbin(): linear binning; fastlinbin(): fast linear
d binning; exactbin(): exact binning; makegrid(): equally spaced
d grid points; cut(): categorize data vector; posof(): find element
d in vector; which(): positions of nonzero elements; locate():
d search an ordered vector; hunt(): consecutive search; cond():
d matrix conditional operator; expand(): duplicate single
d rows/columns; _expand(): duplicate rows/columns in place;
d repeat(): duplicate contents as a whole; _repeat(): duplicate
d contents in place; unorder2(): stable version of unorder();
d jumble2(): stable version of jumble(); _jumble2(): stable version
d of _jumble(); pieces(): break string into pieces; npieces():
d count number of pieces; _npieces(): count number of pieces;
d regexr(): regular expression replace; invtokens(): reverse of
d tokens(); realofstr(): convert string into real; strexpand():
d expand string argument; matlist(): display a (real) matrix;
d insheet(): read spreadsheet file; infile(): read free-format
d file; outsheet(): write spreadsheet file; callf(): pass optional
d args to function; callf_setup(): setup for mm_callf().
d 
d KW: Mata
d KW:  quantile
d KW: median
d KW: inter-quartile range
d KW: integration
d KW: cumulative distribution
d KW: Cauchy distribution
d KW: Cauchy-Lorentz distribution
d KW: relative ranks
d KW: running sum
d KW: linear interpolation
d KW: insheet
d KW: infile
d KW: outsheet
d KW: spreadsheet
d KW: tokens
d KW: rowvector
d KW: string
d KW: real
d KW: grid
d KW: linear binning
d KW: histogram
d 
d Requires: Stata version 9
d 
d Distribution-Date: 20190821
d 
d Author: Ben Jann, University of Bern
d Support: email jann@@soz.unibe.ch
d 
f l\lmoremata.mlib
f l\lmoremata10.mlib
f l\lmoremata11.mlib
f l\lmoremata14.mlib
f m\mf_mm_sample.hlp
f m\moremata.hlp
f m\moremata_source.hlp
f m\moremata10_source.hlp
f m\moremata11_source.hlp
f m\moremata14_source.hlp
f m\mf_mm_unorder2.hlp
f m\mf_mm_panels.hlp
f m\mf_mm_quantile.hlp
f m\mf_mm_strexpand.hlp
f m\mf_mm_realofstr.hlp
f m\mf_mm_posof.hlp
f m\mf_mm_ipolate.hlp
f m\mf_mm_invtokens.hlp
f m\mf_mm_linbin.hlp
f m\mf_mm_histogram.hlp
f m\mf_mm_ecdf.hlp
f m\mf_mm_colrunsum.hlp
f m\mf_mm_rbinomial.hlp
f m\mf_mm_insheet.hlp
f m\mf_mm_cut.hlp
f m\mf_mm_freq.hlp
f m\mf_mm_colvar.hlp
f m\mf_mm_gini.hlp
f m\mf_mm_nunique.hlp
f m\mf_mm_bs.hlp
f m\mf_mm_callf.hlp
f m\mf_mm_expand.hlp
f m\mf_mm_finvert.hlp
f m\mf_mm_greedy.hlp
f m\mf_mm_jk.hlp
f m\mf_mm_kern.hlp
f m\mf_mm_locate.hlp
f m\mf_mm_nobs.hlp
f m\mf_mm_nrroot.hlp
f m\mf_mm_polint.hlp
f m\mf_mm_root.hlp
f m\mf_mm_which.hlp
f m\mf_mm_plot.hlp
f m\mf_mm_pieces.hlp
f m\mf_mm_benford.hlp
f m\mf_mm_isconstant.hlp
f m\mf_mm_mgof.hlp
f m\mf_mm_subset.hlp
f m\mf_mm_cauchy.hlp
f m\mf_mm_matlist.hlp
f m\mf_mm_cond.hlp
f m\mf_mm_collapse.hlp
f m\mf_mm_integrate.hlp
f m\mf_mm_regexr.hlp
f m\mf_mm_sqrt.hlp
f m\mf_mm_ebal.sthlp
e
S http://fmwww.bc.edu/repec/bocode/p
N psmatch2.pkg
D  8 Nov 2019
U 4
d 'PSMATCH2': module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing
d 
d psmatch2 implements full Mahalanobis and propensity score
d matching, common support graphing, and covariate imbalance
d testing. This routine supersedes the previous 'psmatch' routine
d of B. Sianesi. The April 2012 revision of pstest changes the
d syntax of that command.
d 
d KW: propensity score
d KW: matching
d KW: treatment
d 
d Requires: Stata version 11
d 
d 
d Author: Edwin Leuven, Ecole Nationale de la Statistique et Administration Economique (ENSAE)
d Support: email edwin.leuven@@ensae.fr
d 
d Author: Barbara Sianesi, Institute for Fiscal Studies
d Support: email barbara_s@@ifs.org.uk
d 
d Distribution-Date: 20180201
d 
f p\psmatch2.ado
f p\psmatch2.sthlp
f p\psmatch2.dlg
f p\pstest.ado
f p\pstest2.ado
f p\pstest.sthlp
f d\density2.ado
f p\psgraph.ado
f p\psgraph.sthlp
e
S http://fmwww.bc.edu/repec/bocode/i
N ivreg2.pkg
D 15 Nov 2019
U 5
d 'IVREG2': module for extended instrumental variables/2SLS and GMM estimation
d 
d ivreg2 provides extensions to Stata's official ivregress and
d newey. Its main capabilities: two-step feasible GMM estimation;
d continuously updated GMM estimation (CUE); LIML and k-class
d estimation; automatic output of the Hansen-Sargan or
d Anderson-Rubin statistic for overidentifying restrictions; C
d statistic test of exogeneity of subsets of instruments (orthog()
d option); kernel-based autocorrelation-consistent (AC) and
d heteroskedastic and autocorrelation-consistent (HAC) estimation,
d with user-specified choice of kernel; Cragg's "heteroskedastic
d OLS" (HOLS) estimator; default reporting of large-sample
d statistics (z and chi-squared rather than t and F); small option
d to report small-sample statistics; first-stage regression
d reported with F-test of excluded instruments and R-squared with
d included instruments "partialled-out"; enhanced Kleibergen-Paap
d and Cragg-Donald tests for weak instruments, redundancy of
d instruments, significance of endogenous regressors; two-way
d clustering of standard errors;  Kiefer and Driscoll-Kraay
d standard errors. ivreg2 can also be used for ordinary least
d squares (OLS) estimation using the same command syntax as Stata's
d official regress and newey. New in this version: ivreg2 now
d supports factor variables. This is version 4.1.10 of ivreg2,
d updated from that published in Stata Journal, 5(4), requiring
d Stata 11.2 or better. Stata 8.2/9.2/10.2 users may use this
d routine, which will  automatically call ivreg28, ivreg29, or
d ivreg210, respectively. These versions are now included in the
d ivreg2 package. Stata 7 users may use the Stata Journal version
d of ivreg2, accessible via net search ivreg2.
d 
d KW: instrumental variables
d KW: Sargan test
d KW: robust estimation
d KW: orthogonality
d KW: GMM
d KW: Hansen's J
d KW: heteroskedastic OLS,
d 
d Requires: Stata version 11.2 and ranktest from SSC
d 
d 
d Author: Christopher F Baum, Boston College
d Support: email baum@@bc.edu
d 
d Author: Mark E Schaffer, Heriot-Watt University
d Support: email m.e.schaffer@@hw.ac.uk
d 
d Author: Steven Stillman, Free University of Bozen-Bolzano
d Support: email  stillman@@motu.org.nz
d 
d Distribution-Date: 20180419
d 
f i\ivreg2.ado
f i\ivreg2.sthlp
f i\ivreg2_p.ado
f l\livreg2.mlib
f i\ivreg210.ado
f i\ivreg210.sthlp
f i\ivreg210_p.ado
f i\ivreg29.ado
f i\ivreg29.hlp
f i\ivreg29_p.ado
f i\ivreg29_cue.ado
f i\ivreg28.ado
f i\ivreg28.hlp
f i\ivreg28_p.ado
f i\ivreg28_cue.ado
e
S http://fmwww.bc.edu/repec/bocode/r
N ranktest.pkg
D 15 Nov 2019
U 6
d 'RANKTEST': module to test the rank of a matrix using the Kleibergen-Paap rk statistic
d 
d  ranktest implements the Kleibergen-Paap (2006) rk test for the
d rank of a matrix.  Tests of the rank of a matrix have many
d practical applications.  For example, in econometrics the
d requirement for identification is the rank condition, which
d states that a particular matrix must be of full column rank.
d Another example from econometrics concerns cointegration in
d vector autoregressive (VAR) models; the Johansen trace test is a
d test of a rank of a particular matrix.  The traditional test of
d the rank of a matrix for the standard (stationary) case is the
d Anderson (1951) canonical correlations test. If we denote one
d list of variables as Y and a second as Z, and we calculate the
d squared canonical correlations between Y and Z, the LM form of
d the Anderson test, where the null hypothesis is that the matrix
d of correlations or regression parameters B between Y and Z has
d rank(B)=r, is N times the sum of the r+1 largest squared
d canonical correlations.  A large test statistic and rejection of
d the null indicates that the matrix has rank at least r+1.  The
d Cragg-Donald (1993) statistic is a closely related Wald test for
d the rank of a matrix.  Both the Anderson and Cragg-Donald tests
d require the assumption that the covariance matrix has a Kronecker
d form; when this is not so, e.g., when disturbances are
d heteroskedastic or autocorrelated, the test statistics are no
d longer valid. The Kleibergen-Paap (2006) rk statistic is a
d generalization of the Anderson canonical correlation rank test to
d the case of a non-Kronecker covariance matrix.  The
d implementation in ranktest will calculate rk statistics that are
d robust to various forms of heteroskedasticity, autocorrelation,
d and clustering.
d 
d KW: matrix
d KW: rank
d KW: collinearity
d KW: cointegration
d 
d Requires: Stata version 11.2 (version 9.2 for ranktest9)
d 
d Distribution-Date: 20150124
d 
d Author: Frank Kleibergen, Brown University
d Support: email  Frank_Kleibergen@@brown.edu
d 
d Author: Mark E Schaffer, Heriot-Watt University
d Support: email m.e.schaffer@@hw.ac.uk
d 
f r\ranktest.ado
f r\ranktest9.ado
f r\ranktest.hlp
f l\livreg2.mlib
e
S http://www.stata.com/users/vwiggins
N grc1leg.pkg
D  4 Dec 2019
U 7
d grc1leg.  Combine graphs into one graph with a common legend.
d Program by Vince Wiggins, StataCorp <vwiggins@@stata.com>.
d Statalist distribution, 16 June 2003.
d 
d Exactly like -graph combine- but shows a single common legend for all
d combined graphs.
d 
d Distribution-Date: 02jun2010
f g\grc1leg.ado
f g\grc1leg.hlp
e
S http://www.stata-journal.com/software/sj15-1
N st0369.pkg
D  5 Dec 2019
U 8
d SJ15-1 st0369. Compute two-sided bound...
d Compute two-sided bound estimates using
d   Bonferroni's inequality
d by Victor Chernozhukov, Massachusetts Institute
d      of Technology, Cambridge, MA
d    Wooyoung Kim, University of Wisconsin-
d      Madison, Madison, WI
d    Sokbae Lee, Institute for Fiscal Studies and
d      Seoul National University, Seoul, Korea
d    Adam M. Rosen, University College London and
d      Centre for Microdata Methods and Practice,
d      London, UK
d Support:  vchern@@mit.edu, wkim68@@wisc.edu,
d           sokbae@@snu.ac.kr, adam.rosen@@ucl.ac.uk
d After installation, type help ^clr2bound^,
d   ^clr3bound^, ^clrbound^, and ^clrtest^
f c\clr2.ado
f c\clr2b.ado
f c\clr2bo.ado
f c\clr2bou.ado
f c\clr2boun.ado
f c\clr2bound.ado
f c\clr3.ado
f c\clr3b.ado
f c\clr3bo.ado
f c\clr3bou.ado
f c\clr3boun.ado
f c\clr3bound.ado
f c\clr.ado
f c\clrb.ado
f c\clrbo.ado
f c\clrbou.ado
f c\clrboun.ado
f c\clrbound.ado
f c\clrtest.ado
f c\clr_k.ado
f c\clr_kb.ado
f c\clr_p.ado
f c\clr_pb.ado
f c\clr_b.ado
f c\clr_s.ado
f c\clr2bound.sthlp
f c\clr3bound.sthlp
f c\clrbound.sthlp
f c\clrtest.sthlp
e
S http://fmwww.bc.edu/repec/bocode/x
N xtivreg2.pkg
D  5 Dec 2019
U 9
d 'XTIVREG2': module to perform extended IV/2SLS, GMM and AC/HAC, LIML and k-class regression for panel data models
d 
d  xtivreg2 implements IV/GMM estimation of the fixed-effects and
d first-differences panel data models with possibly endogenous
d regressors.  It is essentially a wrapper for ivreg2, which must
d be installed for xtivreg2 to run: ssc install ivreg2, replace).
d xtivreg2 supports all the estimation and reporting options of
d ivreg2; see help ivreg2 for full descriptions and examples.  In
d particular, all the statistics available with ivreg2
d (heteroskedastic, cluster- and autocorrelation-robust covariance
d matrix and standard errors, overidentification and orthogonality
d tests, first-stage and weak/underidentification statistics, etc.)
d are also supported by xtivreg2 and will be reported with any
d degrees-of-freedom adjustments required for a panel data
d estimation.
d 
d KW: instrumental variables
d KW: panel data
d KW: fixed effects
d KW: first differences
d KW: Sargan test
d KW: robust estimation
d KW: orthogonality
d KW: GMM
d KW: Hansen's J
d KW: heteroskedastic OLS,
d 
d Requires: Stata version 8.2 and ivreg2, ranktest from SSC
d 
d Distribution-Date: 20150222
d 
d Author: Mark E Schaffer, Heriot-Watt University
d Support: email m.e.schaffer@@hw.ac.uk
d 
f x\xtivreg2.ado
f x\xtivreg2.hlp
f x\xtivreg2_p.ado
e
