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t and over time statanow 19 5 will get additional features as soon as they are ready from the stata elves we are excited to be able to give you the new features we add to stata on a continuous basis getting them into your hands sooner categories new products stata products tags biostatistics econometrics mata new release python statanow statistics time series a stata command to run chatgpt 25 july 2023 chuck huber director of statistical outreach no comments tweet artificial intelligence ai is a popular topic in the media these days and chatgpt is perhaps the most well known ai tool i recently tweeted that i had written a stata command called chatgpt for myself that runs chatgpt i promised to explain how i did it so here is the explanation read more categories programming tags ado artificial intelligence chatgpt programming pystata python stata stata 17 released 20 april 2021 alan riley president no comments tweet we just announced stata 17 visit stata com new in stata to read all about its 29 major new features they are tables bayesian econometrics faster stata difference in differences did and ddd models interval censored cox model multivariate meta analysis bayesian var models treatment effects lasso estimation bayesian multilevel models nonlinear joint sem like and more galbraith plots leave one out meta analysis bayesian longitudinal panel data models panel data multinomial logit model zero inflated ordered logit model nonparametric tests for trend bayesian dynamic forecasting bayesian irf and fevd analysis bic for lasso penalty selection lasso with clustered data bayesian linear and nonlinear dsge models do file editor enhancements new functions for dates and times intel math kernel library mkl stata on apple silicon jdbc java integration h2o integration pystata jupyter notebook with stata looking over this list of features someone suggested that a potential marketing tagline for stata 17 could be better faster stronger i thought daft punk might not like it if we used that so we aren t but really it is a great overall description of the new version i ll share my thoughts on some of the new features below customizable tables there has been a long tradition in the stata user community of commands that build various tables these are among some of the most used community contributed commands there has been an equally long tradition of the stata user community asking us to provide more official features to assist with flexible table creation and export stata s table command has been completely revamped and a new collect command allows you to gather and manage results from multiple commands which can then be shown in tabular form excel html latex markdown pdf stata smcl word and plain text are supported as export formats i suspect almost all users will be adding this to their stata repertoire bayesian econometrics in stata 17 we have added many features for bayesian econometrics including bayesian var models bayesian irf and fevd analysis bayesian dynamic forecasting bayesian longitudinal panel data models bayesian linear and nonlinear dsge models many of you have been asking us for bayesian var models that s not surprising var models have many parameters but often not enough data to estimate them reliably the bayesian approach provides a solution by incorporating specialized priors to allow you to obtain more stable parameter estimates as with classical var models you can perform irf analysis and obtain dynamic forecasts but now within the bayesian paradigm bayesian panel data models are appealing when you have few panels or when you would like to study and compare panel specific effects with bayesian dsge models prior distributions give you a natural way to incorporate knowledge about model parameters that is motivated by the economic theory as with stata s other bayesian features our aim is to make specification of these models as intuitive as possible and as similar to the specifications of the frequentist counterparts as possible all the above is in addition to other existing bayesian features of interest to econometricians such as bayesian generalized linear models and bayesian sample selection models bayesian multilevel models stata users span many disciplines in addition to the new bayesian features above that will be of most interest to econometricians stata 17 also adds bayesian multilevel modeling with support for nonlinear joint sem like and even more models one notable feature is the ability to fit multivariate nonlinear models containing random effects such as multivariate nonlinear growth models give me more speed as computer capabilities have grown so have dataset sizes with larger datasets and more computationally intensive methods comes the above request give me more speed sometimes our developers say i m giving her all she s got captain but for stata 17 they gave us all more we achieved speed gains in part through careful algorithm selection and implementation and in part through integration of the intel math kernel library mkl to underpin many of mata s linear algebra functions and operators read the details including tables of specific speed gains difference in differences did models did and difference in difference in differences ddd models are appealing to many disciplines including econometrics epidemiology political science public policy and many more if you are studying the effect of a treatment such as a drug regimen or policy in observational data and are concerned that the effect may be influenced by time or by some other group effects did and ddd models provide intuitive methods to control for such unobserved effects new meta analysis features stata 17 adds the following to the excellent imho meta analysis suite introduced in stata 16 multivariate meta analysis galbraith plots leave one out meta analysis if you recognize the above you know you want them and we have made these new features just as easy to use as the rest of the meta suite interval censored cox model the cox proportional hazards model is used routinely by researchers from many disciplines to analyze right censored event time data where time to an event of interest is observed exactly in stata 17 you can use it with interval censored event time data too with interval censored event time data we only know that the time to an event of interest lies in an interval for instance think of time to cancer recurrence or to an infection or for that matter to any asymptomatic disease that can be detected only through periodic examinations these are all interval censored data and you now have a new powerful tool to analyze them new lasso features stata 17 adds the following to the popular lasso features first introduced in stata 16 treatment effects lasso estimation bic for lasso penalty selection lasso with clustered data in fact treatment effects lasso combines two popular features treatment effects and lasso you can now incorporate many hundreds thousands and more covariates in your treatment effects models you can account for clustered observations in your lasso analysis and you can use the bic criterion to select lasso penalty parameters integration with other software and languages two of stata s great features are its extensibility and reproducibility stata 17 builds on that tradition by greatly enhancing its interoperability with python and java adding support for jupyter notebook adding jdbc support and giving you experimental access to the h2o platform you could already call python code from stata code now you can call stata from any stand alone python environment do you write python code in jupyter notebook spyder ide or pycharm ide now you can call stata directly from those environments passing data metadata and results between stata and python seamlessly even your stata graphs will show up directly in jupyter notebook our nickname for all the ways you can connect python and stata is pystata for java you could already compile java code into jar files and call those as plugins from stata now you can embed java code directly in your stata do files and ado files just like mata code and python code stata will compile and execute your java code on the fly you can interchange data metadata and results at will one of the first steps of any analysis is importing your data stata 17 supports jdbc for importing data from and writing data to databases that provide jdbc drivers an important advantage jdbc has over odbc is that jdbc drivers are platform independent so if a database vendor provides a jdbc driver it will work seamlessly on windows mac and linux finally some of our developers have been experimenting with connecting stata to h2o a scalable and distributed open source machine learning and predictive analytics platform we decided to release our experiment to you is this something you d like us to do more with we look forward to your feedback new date and time functions stata 17 adds a plethora of date and time convenience functions in three main areas datetime durations such as ages relative dates such as the next birthday relative to a reference date datetime components functions that extract various components from datetime values you ll undoubtedly find these make your life easier when working with date and time values new do file editor features don t miss the enhancements in the do file editor including persistent bookmarks that are saved with your code a navigation control providing quick access to those bookmarks and defined programs syntax highlighting support for java and xml in addition to the existing support for stata ado python and markdown and autocompletion of quotes parentheses and brackets around a selection and there s more health scientists who deal with ordinal outcomes with an overabundance of values in the lowest category will want to try the new ziologit command those of you interested in panel data and categorical outcomes will be pleased to know that you can now analyze both together easily with the new xtmlogit command and for those of you interested in nonparametric tests of trend we have added three new tests to the existing nptrend command finally stata 17 runs fully natively on apple s new m1 macs known as apple silicon stata ships as a universal application that has everything necessary to run natively on both m1 macs and intel based macs for the best performance no matter your choice of hardware platform it has been a lot of fun to see this release come together at statacorp and it is a tremendous pleasure to be able to release it to you categories new products tags bayesian biostatistics data science econometrics java jupyter notebook lasso meta analysis new release python stata 17 statistics stata python integration part 9 using the stata function interface to copy data from python to stata 19 november 2020 chuck huber director of statistical outreach no comments tweet in my previous post we learned how to use the stata function interface sfi module to copy data from stata to python in this post i will show you how to use the sfi module to copy data from python to stata we will be using the yfinance module to download financial data from the yahoo finance website you can install this module in your python environment by typing pip install yfinance our goal is to use python to download historical data for the dow jones industrial average djia and use stata to create the following graph read more categories programming tags python stocks yahoo finance stata python integration part 8 using the stata function interface to copy data from stata to python 5 november 2020 chuck huber director of statistical outreach no comments tweet in my previous posts i used the read_stata method to read stata datasets into pandas data frame s this works well when you want to read an entire stata dataset into python but sometimes we wish to read a subset of the variables or observations or both from a stata dataset into python in this post i will introduce you to the stata function interface sfi module and show you how to use it to read partial datasets into a pandas data frame read more categories programming tags python stocks yahoo finance stata python integration part 7 machine learning with support vector machines 13 october 2020 chuck huber director of statistical outreach no comments tweet machine learning deep learning and artificial intelligence are a collection of algorithms used to identify patterns in data these algorithms have exotic sounding names like random forests neural networks and spectral clustering in this post i will show you how to use one of these algorithms called a support vector machines svm i don t have space to explain an svm in detail but i will provide some references for further reading at the end i am going to give you a brief introduction and show you how to implement an svm with python our goal is to use an svm to differentiate between people who are likely to have diabetes and those who are not we will use age and hba1c level to differentiate between people with and without diabetes age is measured in years and hba1c is a blood test that measures glucose control the graph below displays diabetics with red dots and nondiabetics with blue dots an svm model predicts that older people with higher levels of hba1c in the red shaded area of the graph are more likely to have diabetes younger people with lower hba1c levels in the blue shaded area are less likely to have diabetes read more categories programming tags artificial intelligence cross validation machine learning python support vector machines stata python integration part 6 working with apis and json data 29 september 2020 chuck huber director of statistical outreach no comments tweet data are everywhere many government agencies financial institutions universities and social media platforms provide access to their data through an application programming interface api apis often return the requested data in a javascript object notation json file in this post i will show you how to use python to request data with api calls and how to work with the resulting json data read more categories programming tags api data science json openfda python stata python integration part 5 three dimensional surface plots of marginal predictions 14 september 2020 chuck huber director of statistical outreach no comments tweet in my first four posts about stata and python i showed you how to set up stata to use python three ways to use python in stata how to install python packages and how to use python packages it might be helpful to read those posts before you continue with this post if you are not familiar with python now i d like to shift our focus to some practical uses of python within stata this post will demonstrate how to use stata to estimate marginal predictions from a logistic regression model and use python to create a three dimensional surface plot of those predictions read more categories programming tags margins marginsplot pro...