StataCorp Stata 17.0 | 890 MB
Fast. Accurate. Easy to use. Stata is a complete, integrated software package that provides all your data science needs—data manipulation, visualization, statistics, and automated reporting. The Team StataCorp is pleased to announce the availability of Stata 17 (Rev. 21 May 2024) is a complete, integrated statistical package that provides everything you need for data analysis, data management, and graphics. -------- update 21may2024 ------------1. The Java Development Kit (OpenJDK) that is redistributed with Stata is now updated to version 17.0.11+9-LTS acquired from Azul Systems.
2. collect did not follow the same factor-variable expansion rules as Stata's fvvarlist for dimensions across, coleq, colname, roweq, rowname, and var. This has been fixed.
For example,
. sysuse auto
. collect : regress mpg foreign#rep78 foreign
. collect layout (colname[foreign]) (result[_r_b])
Notice that in the model specification, foreign is present in two terms. The first term is the interaction foreign#rep78, and here foreign is treated as a factor variable. The second term is foreign, and without a factor-variable operator, it is treated as a continuous predictor. Before the fix, the layout was incorrectly translated to
. collect layout (colname[i.foreign]) (result[_r_b])
instead of keeping foreign as an unadorned variable name. Prior to the fix, the only work-around was to use c. notation, as in colname[c.foreign], to prevent translation to colname[i.foreign].
3. esize and esizei with option level() specified with a number less than 50 and greater than or equal to 10 erroneously exited with an error message. This has been fixed.
4. estat summarize sometimes incorrectly exited with an error message when time-series operators were used in estimation. This has been fixed.
5. gmm with option xtinstruments() has the following fixes and improvements:
a. gmm with option xtinstruments() now returns a warning when robust standard errors are used when there is only one panel.
b. gmm with option xtinstruments() specified with multiple lags when some panels had gaps at the beginning of the time series returned incorrect results. This has been fixed.
c. gmm with options xtinstruments() and vce(robust, independent) returned standard errors that were too small. This has been fixed.
d. gmm with options xtinstruments() and center exited with an uninformative error message. This has been fixed.
e. gmm with option xtinstruments(), when the time-variable or panel-variable values were missing in some observations, exited with an uninformative error message. This has been fixed.
6. gsem with options family(gaussian) and link(identity), when specifie with outcomes with paths from exogenous variables with missing values in the estimation sample, sometimes incorrectly exited with error message "initial values not feasible". This has been fixed.
7. margins with option pwcompare sometimes incorrectly exited with an unhelpful error message. This has been fixed.
8. predict with option latent(), which is used to compute empirical Bayes predictions of the latent endogenous variable, when used with gsem with option group() and estimation results from a model with a latent endogenous variable, incorrectly exited with an unhelpful error message. This has been fixed.
9. sureg and reg3 with option sure, ols, or mvreg returned the names of all dependent variables in e(exog), even though those variables were not exogenous. This has been fixed.
10. table, when specified with a numeric variable requiring more than 8 digits of precision in rowspec, colspec, or tabspec, incorrectly used scientific notation instead of the proper precision. This typically resulted in some categories not being properly accounted for. This has been fixed.
11. xtabond, xtdpd, and xtdpdsys, when gaps were present in a subset of panels, did not account for the gaps when constructing GMM-style lagged instruments in those panels for time periods up to and including the gap, leading to incorrect results. Note that because prior lagged values were substituted for the desired lagged values in these cases, results remained asymptotically valid under commonly used assumptions. This has been fixed.
These tools, together with the Stata Function Interface (sfi) module, allow users to easily integrate Stata's vast statistical and data management methods into any data science project using Python.
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