Stata Statistical Software Release 14
2021年2月15日Download here: http://gg.gg/ob1zv
Use the button(s) below to download the PASS 14 installation file. If PASS 14 is not yet installed on your computer, this will install the full bundle. If you already have PASS 14 installed, this will update your current installation to the newest version. Your personal files will not be affected.
*Stata Software For Free
*Stata Software Cost
*Stata Statistical Software Release 14 PdfPASS 14 Download
Download Now
The Stata News—a periodic publication containing articles on using Stata and tips on using the software, announcements of new releases and updates, feature highlights, and other announcements of interest to interest to Stata users—is sent to all Stata users and those who request information about Stata from us. Yes, please send me the News. Name: STATA SE 14 Description: Stata is a powerful statistical software that enables users to analyze, manage, and produce graphical visualizations of data. It is primarily used by researchers in the fields. Stata: Release 14. Statistical Software. College Station, TX: StataCorp LP. Contents Combined subject table of contents. StataCorp (2015) Stata Statistical Software: Release 14. StataCorp LP, College Station. Has been cited by the following article: TITLE: Evaluation of a Nutrition Intervention through a School-Based Food Garden to Improve Dietary Consumption, Habits and Practices in Children from the Third to Fifth Grade in Chile.
File: PASS14Setup_v14_0_15.exe
Size: 104.81 MB
Current Version: 14.0.15
Released: May 23, 2019
ROI-wise statistical analyses were performed using Stata 14 (Stata Statistical Software: Release 14, StataCorp LP, College Station, TX, USA). Similar Products.
Click here for minimum System RequirementsDocumentation
PDF documentation for PASS 14 is installed with the software and can be accessed through the Help System. The documentation is not available online at this time.Update Release Notes
This page lists the changes that have been made to PASS 14 since it was released.Version: 14.0.15
Released: May 23, 2019
1. Fixed a “Subscript out of range” error in the Tests for Two Proportions in a Repeated Measures Design procedure that occurred when Group Allocation was set to “Enter total sample size and percentage in Group 1” and Definitions were being output in the report.
2. This is the final update to PASS 14.Version: 14.0.14
Released: November 2, 2018
1. Fixed power calculation error in the Tests for Two Correlated Proportions in a Matched Case-Control Design procedure. The author of the paper on which this procedure is based determined that the correction made in PASS 14 update 14.0.10 was unnecessary. The calculations in PASS have been updated to reflect the author’s corrections. This correction returns the PASS calculation results for the Tests for Two Correlated Proportions in a Matched Case-Control Design procedure to the same results obtained prior to PASS 14 update 14.0.10.
2. Fixed power calculation error in the Tests for Two Variances procedure. The power was not being calculated correctly in some cases when the group sample sizes are unequal.
3. Fixed documentation typos in the Non-Central Chi-Square Distribution in the Probability Calculator documentation.Version: 14.0.13
Released: August 17, 2018
1. Corrected issues encountered in various procedures where the period is used in a way other than to represent a decimal symbol (e.g. in “D0.L”) when the computer language uses a comma as the decimal symbol. The period was incorrectly being replaced by a comma in non-numeric instances.Version: 14.0.12
Released: July 6, 2018
1. Corrected an error in the Multiple Contrasts (Simulation) procedure. When solving for Sample Size, the target power value was being set to 1-(User-Entered Value for Power). The correct input power value is now used.
2. Corrected an error that caused incorrect power and sample size calculations when entering “R1” in the Tests for Two Proportions in a Stratified Design (Cochran/Mantel-Haenszel Test) procedure when the computer’s system language setting was set to a South African language setting or any other language setting where R is a currency symbol.
3. Corrected a “Subscript out of Range” error in Hotelling’s One-Sample T2 procedure that occurred when the language setting on the machine uses a comma as the decimal.Version: 14.0.11
Released: February 6, 2018
1. Corrected summary statement errors in the Logrank Tests procedure output. The type of input was mislabeled in some cases.
2. Corrected mouseover message error in the Logrank Tests Accounting for Competing Risks procedure. The message for R (Accrual Time) indicated that 0 was a valid entry, but R must be greater than 0 in this procedure.Version: 14.0.10
Released: November 3, 2017
1. Fixed power calculation error in Tests for Two Correlated Proportions in a Matched Case-Control Design. The author of the paper on which this procedure is based discovered a calculation error in his software used to create the results in the paper. The calculations in PASS and the validation results have been updated to reflect the author’s corrections.
2. Corrected error in Normality Tests procedure. In some cases the power was being drastically underestimated for the Skewness test.Version: 14.0.9
Released: February 14, 2017
1. Fixed an error in the Logrank Tests (Input Proportion Surviving) and Logrank Tests (Input Mortality) procedures. The procedures were not calculating the hazard rates correctly when using spreadsheet entry for the survival proportions or the mortality rates. PASS was calculating hazard rates for each row as though all previous survival proportions (or mortalities) were equal. The detail reports for these two procedures were also corrected to display appropriate values.
2. Fixed a problem in the Survival Parameter Conversion Tool. Mortality 1 Until T0 was not updating when changing Median Survival Time 1 or Hazard Ratio 1.Version: 14.0.8
Released: November 3, 2016
1. Fixed the Tests for Two Proportions in a Repeated Measures Design procedure. When solving for OR1 & P1 and using Odds Ratios for the input type, the Y-axis values on the plots were labeled as Odds Ratios, but plotted as proportions. This has been corrected so that Odds Ratios are plotted.
2. Fixed exact binomial enumeration sample size calculations in One Proportion procedures. The sample size was being overestimated in some cases for very extreme proportions (e.g. 0.98 or higher or 0.02 or less).Version: 14.0.7
Released: July 1, 2016
1. In some instances, input text was incorrectly shown as red (indicating that the entry is not valid) when a valid value of “0” was entered for accrual time in various survival procedures. This has been corrected.
2. Removed extra text from various Logrank Test procedures’ Summary Statements
3. Corrected Tests for One ROC Curve procedure. If AUC0 = AUC1, the power should be undefined, but was being reported as 0.5.Version: 14.0.6
Released: May 6, 2016
1. Software Version added to the header at the top of each output page for better record keeping. The default color for the header was also set to gray for better report readability. You can change any of these settings using the System Options window.
2. Corrected documentation typos in the formula for the non-centrality parameter in Inequality, Non-Inferiority, Superiority by a Margin, and Equivalence Tests for Two Means in a Cluster-Randomized Design procedures. Specifically, the formula for σd was incorrect as stated.
3. Fixed Confidence Intervals for One Standard Deviation using Relative Error procedure. When solving for Relative Error, the search did not converge.
4. Fixed various minor typos in the Multiple Two-Sample T-Tests procedure documentation.Version: 14.0.5
Released: January 6, 2016
1. Corrected documentation and help messages relating to AUC0 and AUC1 in the Tests for One ROC Curve procedure.
2. Minor documentation corrections in the Equivalence Tests for Two Means using Differences procedure.
3. Fixed error in summary statements of the four exponential survival procedures. Some statements were populated with incorrect information.Version: 14.0.4
Released: October 29, 2015
1. Fixed problem for computers with language set to “Thai.” The software was not recognizing valid license keys with an expiration date.
2. Fixed Comparative Plot Labels in the Normality Tests (Simulation) procedure. Some labels were not appearing.
3. Fixed Normality Tests (Simulation) power calculations for cases where the simulated distribution had nearly all ties.
4. Fixed Power and Sample Size calculations in the Logrank Tests in a Cluster-Randomized Design procedure. The power was being overestimated resulting in drastically reduced sample sizes.Version: 14.0.3
Released: September 22, 2015
1. Fixed various typos in the documentation.
2. Fixed two sample survival routines based on the Lakatos method to correct for errors that occurred when the group sample sizes were unequal. These errors amounted to power values that were off by no more that 2 or 3 percentage points when the sample size of one of the groups was up to twice that of the other. Equal group size power values were not affected.
3. Improves overall appearance for High-DPI display on Windows 10 machines. The software was corrected so that text no longer appears fuzzy.Version: 14.0.2
Released: July 28, 2015
1. Fixed “Overflow” error in plots with extremely large axis values.
2. Fixed Anderson-Darling Normality Test and Range Normality Test calculation for large sample sizes. The power was incorrect for the Anderson-Darling Test when N > ~4000 (it was reported as 0.000 when it should have been 1.000 for very non-normal data). The Range Test is only available when N < 1000, but results were still appearing.
3. Fixed example templates for Confidence Intervals for One Proportion procedure. They were generating a ’Type mismatch’ error.
4. Fixed various minor documentation typos and omissions.
5. Fixed layout of Getting Started window. Items were not being re-scaled for some computer DPI settings.Version: 14.0.1
Released: July 21, 2015
1. Initial Release of PASS 14.
We just announced the release of Stata 16. It is now available. Click to visit stata.com/new-in-stata.
Stata 16 is a big release, which our releases usually are. This one is broader than usual. It ranges from lasso to Python and from multiple datasets in memory to multiple chains in Bayesian analysis.
The highlights are listed below. If you click on a highlight, we will spirit you away to our website, where we will describe the feature in a dry but information-dense way. Or you can scroll down and read my comments, which I hope are more entertaining even if they are less informative.
The big features of Stata 16 are
*Importing of SAS and SPSS datasets
*Set matsize obviated
Number 22 is not a link because it’s not a highlight. I added it because I suspect it will affect the most Stata users. It may not be enough to make you buy the release, but it will half tempt you. Buy the update, and you will never again have to type
And if you do type it, you will be ignored. Stata just works, and it uses less memory.
Oh, and in Stata/MP, Stata matrices can now be up to 65,534 x 65,534, meaning you can fit models with over 65,000 right-hand-side variables. Meanwhile, Mata matrices remain limited only by memory.
Here are my comments on the highlights.
1. Lasso, both for prediction and for inference
There are two parts to our implementation of lasso: prediction and inference. I suspect inference will be of more interest to our users, but we needed prediction to implement inference. By the way, when I say lasso, I mean lasso, elastic net, and square-root lasso, but if you want a features list, click the title.
Let’s start with lasso for prediction. If you type
lasso will select the covariates from the x‘s specified and fit the model on them. lasso will be unlikely to choose the covariates that belong in the true model, but it will choose covariates that are collinear with them, and that works a treat for prediction. If English is not your first language, by “works a treat”, I mean great. Anyway, the lasso command is for prediction, and standard errors for the covariates it selects are not reported because they would be misleading.
Concerning inference, we provide four lasso-based methods: double selection, cross-fit partialing out, and two more. If you type
then, conceptually but not actually, y will be fit on x1 and the variables lasso selects from x2-x999. That’s not how the calculation is made because the variables lasso selects are not identical to the true variables that belong in the model. I said earlier that they are correlated with the true variables, and they are. Another way to think about selection is that lassoestimates the variables to be selected and, as with all estimation, that is subject to error. Anyway, the inference calculations are robust to those errors. Reported will be the coefficient and its standard error for x1. I specified one variable of special interest in the example, but you can specify however many you wish.
2. Reproducible and automatically updating reports Tinmouse ii boeing 737-200.
The inelegant title above is trying to say (1) reports that reproduce themselves just as they were originally and (2) reports that, when run again, update themselves by running the analysis on the latest data. Stata has always been strong on both, and we have added more features. I don’t want to downplay the additions, but neither do I want to discuss them. Click the title to learn about them.
I think what’s important is another aspect of what we did. The real problem was that we never told you how to use the reporting features. Now we do in an all-new manual. We tell you and we show you, with examples and workflows. Here’s a link to the manual so you can judge for yourself.
3. New meta-analysis suite
Stata is known for its community-contributed meta-analysis. Now there is an official StataCorp suite as well. It’s complete and easy to use. And yes, it has funnel plots and forest plots, and bubble plots and L’Abbé plots.
4. Revamped and expanded choice modeling (margins works everywhere)
Choice modeling is jargon for conditional logit, mixed logit, multinomial probit, and other procedures that model the probability of individuals making a particular choice from the alternatives available to each of them.
We added a new command to fit mixed logit models, and we rewrote all the rest. The new commands are easier to use and have new features. Old commands continue to work under version control.
margins can now be used after fitting any choice model. margins answers questions about counterfactuals and can even answer them for any one of the alternatives. You can finally obtain answers to questions like, “How would a $10,000 increase in income affect the probability people take public transportation to work?”
The new commands are easier to use because you must first cmset your data. That may not sound like a simplification, but it simplifies the syntax of the remaining commands because it gets details out of the way. And it has another advantage. It tells Stata what your data should look like so Stata can run consistency checks and flag potential problems.
Finally, we created a new [CM] Choice Modeling Manual. Everything you need to know about choice modeling can now be found in one place.
5. Integration of Python with Stata
If you don’t know what Python is, put down your quill pen, dig out your acoustic modem and plug it in, push your telephone handset firmly into the coupler, and visit Wikipedia. Python has become an exceedingly popular programming language with extensive libraries for writing numerical, machine learning, and web scraping routines.
Stata’s new relationship with Python is the same as its relationship with Mata. You can use it interactively from the Stata prompt, in do-files, and in ado-files. You can even put Python subroutines at the bottom of ado-files, just as you do Mata subroutines. Or put both. Stata’s flexible.
Python can access Stata results and post results back to Stata using the Stata Function Interface (sfi), the Python module that we provide.
6. Bayesian predictions, multiple chains, and more
We have lots of new Bayesian features.
We now have multiple chains. Has the MCMC converged? Estimate models using multiple chains, and reported will be the maximum of Gelman-Rubin convergence diagnostic. If it has not yet converged, do more simulations. Still hasn’t converged? Now you can obtain the Gelman-Rubin convergence diagnostic for each parameter. If the same parameter turns up again and again as the culprit, you know where the problem lies.
We now provide Bayesian predictions for outcomes and functions of them. Bayesian predictions are calculated from the simulations that were run to fit your model, so there are a lot of them. The predictions will be saved in a separate dataset. Once you have the predictions, we provide commands so that you can graph summaries of them and perform hypothesis testing. And you can use them to obtain posterior predictive p-values to check the fit of your model.
There’s more. Click the title.
7. Extended regression models (ERMs) for panel data
ERMs fits models with problems. These problems can be any combination of (1) endogenous and exogenous sample selection, (2) endogenous covariates, also known as unobserved confounders, and (3) nonrandom treatment assignment.
What’s new is that ERMs can now be used to fit models with panel (2-level) data. Random effects are added to each equation. Correlations between the random effects are reported. You can test them, jointly or singly. And you can suppress them, jointly or singly.
Ermistatas got a fourth antenna.
8. Importing of SAS and SPSS datasets
New command import sas imports .sas7bdat data files and .sas7bcat value-label files.
New command import spss imports IBM SPSS version 16 or higher .sav and .zsav files.
I recommend using them from their dialog boxes. You can preview the data and select the variables and observations you want to import.
9. Flexible nonparametric series regression
New command npregress series fits models like
y = g(x1, x2, x3) + ε
No functional-form restrictions are placed on g(), but you can impose separability restrictions. The new command can fit
y = g1(x1) + g2(x2, x3) + ε
y = g1(x1, x2) + g3(x3) + ε
y = g1(x1, x3) + g2(x2) + ε
and even fit
y = b1x1 + g2(x2, x3) + ε
y = b1x1 + b2x2 + g3(x3) + ε
I mentioned that lasso can perform inference in models like
If you know that variables x12, x19, and x122 appear in the model, but do not know the functional form, you could use npregress series to obtain inference. The command
fits
y = b1x1 + g2(x12, x19, x122) + ε
and, among other statistics, reports the coefficient and standard error of b1.
10. Multiple datasets in memory, meaning frames
I’m a sucker for data management commands. Even so, I d
https://diarynote-jp.indered.space
Use the button(s) below to download the PASS 14 installation file. If PASS 14 is not yet installed on your computer, this will install the full bundle. If you already have PASS 14 installed, this will update your current installation to the newest version. Your personal files will not be affected.
*Stata Software For Free
*Stata Software Cost
*Stata Statistical Software Release 14 PdfPASS 14 Download
Download Now
The Stata News—a periodic publication containing articles on using Stata and tips on using the software, announcements of new releases and updates, feature highlights, and other announcements of interest to interest to Stata users—is sent to all Stata users and those who request information about Stata from us. Yes, please send me the News. Name: STATA SE 14 Description: Stata is a powerful statistical software that enables users to analyze, manage, and produce graphical visualizations of data. It is primarily used by researchers in the fields. Stata: Release 14. Statistical Software. College Station, TX: StataCorp LP. Contents Combined subject table of contents. StataCorp (2015) Stata Statistical Software: Release 14. StataCorp LP, College Station. Has been cited by the following article: TITLE: Evaluation of a Nutrition Intervention through a School-Based Food Garden to Improve Dietary Consumption, Habits and Practices in Children from the Third to Fifth Grade in Chile.
File: PASS14Setup_v14_0_15.exe
Size: 104.81 MB
Current Version: 14.0.15
Released: May 23, 2019
ROI-wise statistical analyses were performed using Stata 14 (Stata Statistical Software: Release 14, StataCorp LP, College Station, TX, USA). Similar Products.
Click here for minimum System RequirementsDocumentation
PDF documentation for PASS 14 is installed with the software and can be accessed through the Help System. The documentation is not available online at this time.Update Release Notes
This page lists the changes that have been made to PASS 14 since it was released.Version: 14.0.15
Released: May 23, 2019
1. Fixed a “Subscript out of range” error in the Tests for Two Proportions in a Repeated Measures Design procedure that occurred when Group Allocation was set to “Enter total sample size and percentage in Group 1” and Definitions were being output in the report.
2. This is the final update to PASS 14.Version: 14.0.14
Released: November 2, 2018
1. Fixed power calculation error in the Tests for Two Correlated Proportions in a Matched Case-Control Design procedure. The author of the paper on which this procedure is based determined that the correction made in PASS 14 update 14.0.10 was unnecessary. The calculations in PASS have been updated to reflect the author’s corrections. This correction returns the PASS calculation results for the Tests for Two Correlated Proportions in a Matched Case-Control Design procedure to the same results obtained prior to PASS 14 update 14.0.10.
2. Fixed power calculation error in the Tests for Two Variances procedure. The power was not being calculated correctly in some cases when the group sample sizes are unequal.
3. Fixed documentation typos in the Non-Central Chi-Square Distribution in the Probability Calculator documentation.Version: 14.0.13
Released: August 17, 2018
1. Corrected issues encountered in various procedures where the period is used in a way other than to represent a decimal symbol (e.g. in “D0.L”) when the computer language uses a comma as the decimal symbol. The period was incorrectly being replaced by a comma in non-numeric instances.Version: 14.0.12
Released: July 6, 2018
1. Corrected an error in the Multiple Contrasts (Simulation) procedure. When solving for Sample Size, the target power value was being set to 1-(User-Entered Value for Power). The correct input power value is now used.
2. Corrected an error that caused incorrect power and sample size calculations when entering “R1” in the Tests for Two Proportions in a Stratified Design (Cochran/Mantel-Haenszel Test) procedure when the computer’s system language setting was set to a South African language setting or any other language setting where R is a currency symbol.
3. Corrected a “Subscript out of Range” error in Hotelling’s One-Sample T2 procedure that occurred when the language setting on the machine uses a comma as the decimal.Version: 14.0.11
Released: February 6, 2018
1. Corrected summary statement errors in the Logrank Tests procedure output. The type of input was mislabeled in some cases.
2. Corrected mouseover message error in the Logrank Tests Accounting for Competing Risks procedure. The message for R (Accrual Time) indicated that 0 was a valid entry, but R must be greater than 0 in this procedure.Version: 14.0.10
Released: November 3, 2017
1. Fixed power calculation error in Tests for Two Correlated Proportions in a Matched Case-Control Design. The author of the paper on which this procedure is based discovered a calculation error in his software used to create the results in the paper. The calculations in PASS and the validation results have been updated to reflect the author’s corrections.
2. Corrected error in Normality Tests procedure. In some cases the power was being drastically underestimated for the Skewness test.Version: 14.0.9
Released: February 14, 2017
1. Fixed an error in the Logrank Tests (Input Proportion Surviving) and Logrank Tests (Input Mortality) procedures. The procedures were not calculating the hazard rates correctly when using spreadsheet entry for the survival proportions or the mortality rates. PASS was calculating hazard rates for each row as though all previous survival proportions (or mortalities) were equal. The detail reports for these two procedures were also corrected to display appropriate values.
2. Fixed a problem in the Survival Parameter Conversion Tool. Mortality 1 Until T0 was not updating when changing Median Survival Time 1 or Hazard Ratio 1.Version: 14.0.8
Released: November 3, 2016
1. Fixed the Tests for Two Proportions in a Repeated Measures Design procedure. When solving for OR1 & P1 and using Odds Ratios for the input type, the Y-axis values on the plots were labeled as Odds Ratios, but plotted as proportions. This has been corrected so that Odds Ratios are plotted.
2. Fixed exact binomial enumeration sample size calculations in One Proportion procedures. The sample size was being overestimated in some cases for very extreme proportions (e.g. 0.98 or higher or 0.02 or less).Version: 14.0.7
Released: July 1, 2016
1. In some instances, input text was incorrectly shown as red (indicating that the entry is not valid) when a valid value of “0” was entered for accrual time in various survival procedures. This has been corrected.
2. Removed extra text from various Logrank Test procedures’ Summary Statements
3. Corrected Tests for One ROC Curve procedure. If AUC0 = AUC1, the power should be undefined, but was being reported as 0.5.Version: 14.0.6
Released: May 6, 2016
1. Software Version added to the header at the top of each output page for better record keeping. The default color for the header was also set to gray for better report readability. You can change any of these settings using the System Options window.
2. Corrected documentation typos in the formula for the non-centrality parameter in Inequality, Non-Inferiority, Superiority by a Margin, and Equivalence Tests for Two Means in a Cluster-Randomized Design procedures. Specifically, the formula for σd was incorrect as stated.
3. Fixed Confidence Intervals for One Standard Deviation using Relative Error procedure. When solving for Relative Error, the search did not converge.
4. Fixed various minor typos in the Multiple Two-Sample T-Tests procedure documentation.Version: 14.0.5
Released: January 6, 2016
1. Corrected documentation and help messages relating to AUC0 and AUC1 in the Tests for One ROC Curve procedure.
2. Minor documentation corrections in the Equivalence Tests for Two Means using Differences procedure.
3. Fixed error in summary statements of the four exponential survival procedures. Some statements were populated with incorrect information.Version: 14.0.4
Released: October 29, 2015
1. Fixed problem for computers with language set to “Thai.” The software was not recognizing valid license keys with an expiration date.
2. Fixed Comparative Plot Labels in the Normality Tests (Simulation) procedure. Some labels were not appearing.
3. Fixed Normality Tests (Simulation) power calculations for cases where the simulated distribution had nearly all ties.
4. Fixed Power and Sample Size calculations in the Logrank Tests in a Cluster-Randomized Design procedure. The power was being overestimated resulting in drastically reduced sample sizes.Version: 14.0.3
Released: September 22, 2015
1. Fixed various typos in the documentation.
2. Fixed two sample survival routines based on the Lakatos method to correct for errors that occurred when the group sample sizes were unequal. These errors amounted to power values that were off by no more that 2 or 3 percentage points when the sample size of one of the groups was up to twice that of the other. Equal group size power values were not affected.
3. Improves overall appearance for High-DPI display on Windows 10 machines. The software was corrected so that text no longer appears fuzzy.Version: 14.0.2
Released: July 28, 2015
1. Fixed “Overflow” error in plots with extremely large axis values.
2. Fixed Anderson-Darling Normality Test and Range Normality Test calculation for large sample sizes. The power was incorrect for the Anderson-Darling Test when N > ~4000 (it was reported as 0.000 when it should have been 1.000 for very non-normal data). The Range Test is only available when N < 1000, but results were still appearing.
3. Fixed example templates for Confidence Intervals for One Proportion procedure. They were generating a ’Type mismatch’ error.
4. Fixed various minor documentation typos and omissions.
5. Fixed layout of Getting Started window. Items were not being re-scaled for some computer DPI settings.Version: 14.0.1
Released: July 21, 2015
1. Initial Release of PASS 14.
We just announced the release of Stata 16. It is now available. Click to visit stata.com/new-in-stata.
Stata 16 is a big release, which our releases usually are. This one is broader than usual. It ranges from lasso to Python and from multiple datasets in memory to multiple chains in Bayesian analysis.
The highlights are listed below. If you click on a highlight, we will spirit you away to our website, where we will describe the feature in a dry but information-dense way. Or you can scroll down and read my comments, which I hope are more entertaining even if they are less informative.
The big features of Stata 16 are
*Importing of SAS and SPSS datasets
*Set matsize obviated
Number 22 is not a link because it’s not a highlight. I added it because I suspect it will affect the most Stata users. It may not be enough to make you buy the release, but it will half tempt you. Buy the update, and you will never again have to type
And if you do type it, you will be ignored. Stata just works, and it uses less memory.
Oh, and in Stata/MP, Stata matrices can now be up to 65,534 x 65,534, meaning you can fit models with over 65,000 right-hand-side variables. Meanwhile, Mata matrices remain limited only by memory.
Here are my comments on the highlights.
1. Lasso, both for prediction and for inference
There are two parts to our implementation of lasso: prediction and inference. I suspect inference will be of more interest to our users, but we needed prediction to implement inference. By the way, when I say lasso, I mean lasso, elastic net, and square-root lasso, but if you want a features list, click the title.
Let’s start with lasso for prediction. If you type
lasso will select the covariates from the x‘s specified and fit the model on them. lasso will be unlikely to choose the covariates that belong in the true model, but it will choose covariates that are collinear with them, and that works a treat for prediction. If English is not your first language, by “works a treat”, I mean great. Anyway, the lasso command is for prediction, and standard errors for the covariates it selects are not reported because they would be misleading.
Concerning inference, we provide four lasso-based methods: double selection, cross-fit partialing out, and two more. If you type
then, conceptually but not actually, y will be fit on x1 and the variables lasso selects from x2-x999. That’s not how the calculation is made because the variables lasso selects are not identical to the true variables that belong in the model. I said earlier that they are correlated with the true variables, and they are. Another way to think about selection is that lassoestimates the variables to be selected and, as with all estimation, that is subject to error. Anyway, the inference calculations are robust to those errors. Reported will be the coefficient and its standard error for x1. I specified one variable of special interest in the example, but you can specify however many you wish.
2. Reproducible and automatically updating reports Tinmouse ii boeing 737-200.
The inelegant title above is trying to say (1) reports that reproduce themselves just as they were originally and (2) reports that, when run again, update themselves by running the analysis on the latest data. Stata has always been strong on both, and we have added more features. I don’t want to downplay the additions, but neither do I want to discuss them. Click the title to learn about them.
I think what’s important is another aspect of what we did. The real problem was that we never told you how to use the reporting features. Now we do in an all-new manual. We tell you and we show you, with examples and workflows. Here’s a link to the manual so you can judge for yourself.
3. New meta-analysis suite
Stata is known for its community-contributed meta-analysis. Now there is an official StataCorp suite as well. It’s complete and easy to use. And yes, it has funnel plots and forest plots, and bubble plots and L’Abbé plots.
4. Revamped and expanded choice modeling (margins works everywhere)
Choice modeling is jargon for conditional logit, mixed logit, multinomial probit, and other procedures that model the probability of individuals making a particular choice from the alternatives available to each of them.
We added a new command to fit mixed logit models, and we rewrote all the rest. The new commands are easier to use and have new features. Old commands continue to work under version control.
margins can now be used after fitting any choice model. margins answers questions about counterfactuals and can even answer them for any one of the alternatives. You can finally obtain answers to questions like, “How would a $10,000 increase in income affect the probability people take public transportation to work?”
The new commands are easier to use because you must first cmset your data. That may not sound like a simplification, but it simplifies the syntax of the remaining commands because it gets details out of the way. And it has another advantage. It tells Stata what your data should look like so Stata can run consistency checks and flag potential problems.
Finally, we created a new [CM] Choice Modeling Manual. Everything you need to know about choice modeling can now be found in one place.
5. Integration of Python with Stata
If you don’t know what Python is, put down your quill pen, dig out your acoustic modem and plug it in, push your telephone handset firmly into the coupler, and visit Wikipedia. Python has become an exceedingly popular programming language with extensive libraries for writing numerical, machine learning, and web scraping routines.
Stata’s new relationship with Python is the same as its relationship with Mata. You can use it interactively from the Stata prompt, in do-files, and in ado-files. You can even put Python subroutines at the bottom of ado-files, just as you do Mata subroutines. Or put both. Stata’s flexible.
Python can access Stata results and post results back to Stata using the Stata Function Interface (sfi), the Python module that we provide.
6. Bayesian predictions, multiple chains, and more
We have lots of new Bayesian features.
We now have multiple chains. Has the MCMC converged? Estimate models using multiple chains, and reported will be the maximum of Gelman-Rubin convergence diagnostic. If it has not yet converged, do more simulations. Still hasn’t converged? Now you can obtain the Gelman-Rubin convergence diagnostic for each parameter. If the same parameter turns up again and again as the culprit, you know where the problem lies.
We now provide Bayesian predictions for outcomes and functions of them. Bayesian predictions are calculated from the simulations that were run to fit your model, so there are a lot of them. The predictions will be saved in a separate dataset. Once you have the predictions, we provide commands so that you can graph summaries of them and perform hypothesis testing. And you can use them to obtain posterior predictive p-values to check the fit of your model.
There’s more. Click the title.
7. Extended regression models (ERMs) for panel data
ERMs fits models with problems. These problems can be any combination of (1) endogenous and exogenous sample selection, (2) endogenous covariates, also known as unobserved confounders, and (3) nonrandom treatment assignment.
What’s new is that ERMs can now be used to fit models with panel (2-level) data. Random effects are added to each equation. Correlations between the random effects are reported. You can test them, jointly or singly. And you can suppress them, jointly or singly.
Ermistatas got a fourth antenna.
8. Importing of SAS and SPSS datasets
New command import sas imports .sas7bdat data files and .sas7bcat value-label files.
New command import spss imports IBM SPSS version 16 or higher .sav and .zsav files.
I recommend using them from their dialog boxes. You can preview the data and select the variables and observations you want to import.
9. Flexible nonparametric series regression
New command npregress series fits models like
y = g(x1, x2, x3) + ε
No functional-form restrictions are placed on g(), but you can impose separability restrictions. The new command can fit
y = g1(x1) + g2(x2, x3) + ε
y = g1(x1, x2) + g3(x3) + ε
y = g1(x1, x3) + g2(x2) + ε
and even fit
y = b1x1 + g2(x2, x3) + ε
y = b1x1 + b2x2 + g3(x3) + ε
I mentioned that lasso can perform inference in models like
If you know that variables x12, x19, and x122 appear in the model, but do not know the functional form, you could use npregress series to obtain inference. The command
fits
y = b1x1 + g2(x12, x19, x122) + ε
and, among other statistics, reports the coefficient and standard error of b1.
10. Multiple datasets in memory, meaning frames
I’m a sucker for data management commands. Even so, I d
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