Calebhf

ado/_/_eststo.ado

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+*! version 1.0.4  09nov2007  Ben Jann
+
+program define _eststo, byable(onecall)
+    local caller : di _caller()
+    version 8.2
+    if "`_byvars'"!="" local by "by `_byvars'`_byrc0' : "
+    if inlist(`"`1'"',"clear","dir","drop") {
+        version `caller': `by'eststo `0'
+    }
+    else {
+        capt _on_colon_parse `0'
+        if !_rc {
+            local command `"`s(after)'"'
+            if `"`command'"'!="" {
+                local command `":`command'"'
+            }
+            local 0 `"`s(before)'"'
+        }
+        syntax [anything] [, Esample * ]
+        if `"`esample'"'=="" {
+            local options `"noesample `options'"'
+        }
+        if `"`options'"'!="" {
+            local options `", `options'"'
+        }
+        version `caller': `by'eststo `anything'`options' `command'
+    }
+end

ado/_/_eststo.hlp

@@ -0,0 +1 @@
+.h eststo

ado/backup.trk

@@ -0,0 +1,367 @@
+* 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://www.stata.com/users/vwiggins
+N grc1leg.pkg
+D 25 Sep 2024
+U 1
+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://fmwww.bc.edu/repec/bocode/c
+N cibar.pkg
+D 25 Sep 2024
+U 2
+d 'CIBAR': module to plot bar graphs and confidence intervals over groups
+d 
+d  cibar plots bars and confidence intervals of the mean of a
+d variable over at least one group.  cibar aims at drawing graphs
+d that resemble graphs created by graph bar varname, over(varname)
+d asyvar,  adding confidence intervals to the bars. In order to do
+d so, cibar uses Stata's twoway bar and twoway rcap. cibar shows
+d graphically the differences in the mean of a variable depending
+d on the categories of other variables.
+d 
+d KW: bar graph
+d KW: graphics
+d KW: confidence intervals
+d KW: means
+d 
+d Requires: Stata version 13
+d 
+d Distribution-Date: 20190624
+d 
+d Author: Alexander Staudt, N/A
+d Support: email staudtlex@@live.de
+d 
+f c/cibar.ado
+f c/cibar.sthlp
+e
+S http://fmwww.bc.edu/repec/bocode/c
+N coefplot.pkg
+D 25 Sep 2024
+U 3
+d 'COEFPLOT': module to plot regression coefficients and other results
+d 
+d   coefplot plots results from estimation commands or Stata
+d matrices. Results from multiple models or matrices can be
+d combined in a single graph. The default behavior of coefplot is
+d to draw markers for coefficients and horizontal spikes for
+d confidence intervals. However, coefplot can also produce various
+d other types of graphs.
+d 
+d KW: graphics
+d KW: coefficients
+d KW: estimation
+d 
+d Requires: Stata version 11
+d 
+d Distribution-Date: 20230225
+d 
+d Author: Ben Jann, University of Bern
+d Support: email jann@@soz.unibe.ch
+d 
+f c/coefplot.ado
+f c/coefplot.sthlp
+e
+S http://fmwww.bc.edu/repec/bocode/c
+N cmogram.pkg
+D 25 Sep 2024
+U 4
+d 'CMOGRAM': module to plot histogram-style conditional mean or median graphs
+d 
+d  cmogram graphs the means, medians, frequencies, or proportions
+d of one variable, conditional on another. Output can be further
+d conditioned on a series of control variables, in which case it is
+d the means or medians of the residual values that are plotted. For
+d regression discontinuity and similar applications, graphs can be
+d split at a particular cut point, and a by() option allows for
+d sub-groups to be plotted separately. While the default output is
+d similar to a histogram, there is the option of scatterplot-style
+d output.
+d 
+d KW: graphics
+d KW: descriptive statistics
+d KW: mean
+d KW: median
+d KW: frequencies
+d KW: proportions
+d 
+d Requires: Stata version 9.2
+d 
+d Distribution-Date: 20110911
+d 
+d Author: Christopher Robert, John F. Kennedy School of Government, Harvard University
+d Support: email chris_robert@@hksphd.harvard.edu
+d 
+f c/cmogram.ado
+f c/cmogram.hlp
+e
+S http://fmwww.bc.edu/repec/bocode/e
+N estout.pkg
+D 25 Sep 2024
+U 5
+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: 20230212
+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/o
+N outreg2.pkg
+D 25 Sep 2024
+U 6
+d 'OUTREG2': module to arrange regression outputs into an illustrative table
+d 
+d  outreg2 provides a fast and easy way to produce an illustrative
+d table  of regression outputs. The regression outputs are produced
+d piecemeal and are  difficult to compare without some type of
+d rearrangement. outreg2  automates this process by concatenating
+d the successive regression outputs in a  vertical format. The
+d resulting table is saved to the disk in ASCII  format, which can
+d be read by other programs. outreg2 thus facilitates  the
+d convertion of regression outputs to a standard format suitable
+d for  inclusion in a scholarly publication. The functionality of
+d outreg2 is based on the earlier package outreg,  by John Luke
+d Gallup. Unlike outreg, outreg2 is capable of writing LaTeX-format
+d tables, as well as ASCII, MS Word and MS Excel.
+d 
+d KW: regression
+d KW: output
+d KW: tables
+d KW: tab-delimited output
+d KW: LaTeX
+d KW: Word
+d KW: Excel
+d 
+d Requires: Stata version 7
+d 
+d Distribution-Date: 20140817
+d 
+d Author: Roy Wada
+d Support: email roywada@@hotmail.com
+d 
+f o/outreg2.ado
+f o/outreg2_prf.ado
+f o/outreg2.hlp
+f s/shellout.ado
+f s/shellout.hlp
+f s/seeout.ado
+f s/seeout.hlp
+e
+S http://fmwww.bc.edu/repec/bocode/x
+N xtivreg2.pkg
+D 25 Sep 2024
+U 7
+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: 20200626
+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
+S http://fmwww.bc.edu/repec/bocode/i
+N ivreg2.pkg
+D 25 Sep 2024
+U 8
+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 4.1.11 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 Distribution-Date: 20240814
+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  Steven.Stillman@@unibz.it
+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 25 Sep 2024
+U 9
+d 'RANKTEST': module to test the rank of a matrix
+d 
+d  ranktest implements various tests for the rank of a matrix.
+d Tests of the rank of a matrix have many practical applications.
+d For example, in econometrics the requirement for identification
+d is the rank condition, which states that a particular matrix must
+d be of full column rank. Another example from econometrics
+d concerns cointegration in vector autoregressive (VAR) models; the
+d Johansen trace test is a test of a rank of a particular matrix.
+d The traditional test of the rank of a matrix for the standard
+d (stationary) case is the Anderson (1951) canonical correlations
+d test. If we denote one list of variables as Y and a second as Z,
+d and we calculate the squared canonical correlations between Y and
+d Z, the LM form of the Anderson test, where the null hypothesis is
+d that the matrix of correlations or regression parameters B
+d between Y and Z has rank(B)=r, is N times the sum of the r+1
+d largest squared canonical correlations. A large test statistic
+d and rejection of the null indicates that the matrix has rank at
+d least r+1. The Cragg-Donald (1993) statistic is a closely related
+d Wald test for the rank of a matrix. The standard versions of
+d these tests require the assumption that the covariance matrix has
+d a Kronecker form; when this is not so, e.g., when disturbances
+d are heteroskedastic or autocorrelated, the test statistics are no
+d longer valid. ranktest implements various generalizations of
+d these tests - Kleibergen-Paap, Cragg-Donald, and J-type 2-step
+d GMM and CUE GMM tests - to the case of a non-Kronecker covariance
+d matrix. The implementation in ranktest will calculate test
+d statistics that are robust to various forms of
+d heteroskedasticity, autocorrelation, and clustering.
+d 
+d KW: matrix
+d KW: rank
+d KW: collinearity
+d KW: cointegration
+d 
+d Requires: Stata version 12 (version 9.2 for ranktest9, version 11 for ranktest11)
+d 
+d Distribution-Date: 20200929
+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 
+d Author: Frank Windmeijer, University of Oxford
+d Support: email frank.windmeijer@@stats.ox.ac.uk
+d 
+f r/ranktest.ado
+f r/ranktest9.ado
+f r/ranktest11.ado
+f r/ranktest.sthlp
+f r/ranktest11.sthlp
+f l/livreg2.mlib
+e