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We know only that cancer recurred sometime between the previous and current examinations.
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For example, the recurrence of cancer can be detected between periodic examinations, but the exact time of recurrence cannot be observed. Interval-censoring occurs when the time to an event of interest, such as recurrence of cancer, is not directly observed but is known to lie within an interval. The new estimation command stintcox fits the Cox model to interval-censored event-time data. DDD analysis controls for additional group effects and their interactions with time-you can specify up to three group variables or two group variables and a time variable.Ī semiparametric Cox proportional hazards regression model is commonly used to analyze uncensored and right-censored event-time data. Unlike with the standard cross-sectional analysis, available with the existing teffects command, DID analysis controls for group and time effects when estimating the ATET, where groups identify repeated measures. A treatment effect can be an effect of a drug regimen on blood pressure or an effect of a training program on employment. DID and DDD models are used to estimate the average treatment effect on the treated (ATET) with repeated-measures data. didregress works with repeated-cross-sectional data, and xtdidregress works with longitudinal/panel data. New estimation commands didregress and xtdidregress fit difference-in-differences (DID) and difference-in-difference-in-differences or triple-differences (DDD) models with repeated-measures data. We also attained speed improvements for some estimation commands such as mixed, which fits multilevel mixed-effects models. In Stata 17, we updated the algorithms behind sort and collapse to make these commands faster. There is often a tradeoff between the two, but Stata strives to give users the best of both worlds. Stata values accuracy and it values speed. Stata 17 offers several new features in the area of Bayesian econometrics: Bayesian VAR models Bayesian IRF and FEVD analysis Bayesian dynamic forecasting Bayesian longitudinal/panel-data models Bayesian linear and nonlinear DSGE models. Either way, a Bayesian approach allows us to combine that external information with what we observe in the current data to form a more realistic view of the economic process of interest. This information may come from historical data, or it may come naturally from the knowledge of an economic process. And more! One of the appeals for using Bayesian methods in econometric modeling is to incorporate the external information about model parameters often available in practice. Fit many Bayesian models such as cross-sectional, panel-data, multilevel, and time-series models. Want to use probabilistic statements to answer economic questions, for example, Are those who participate in a job-training program more likely to stay employed for the next five years? Want to incorporate prior knowledge of an economic process? Stata’s new Bayesian econometrics features can help. You can also point-and-click to create tables using the new Tables Builder. The new collect prefix collects as many results from as many commands as you want, builds tables, exports them to many formats, and more. You can easily create tables that compare regression results or summary statistics, you can create styles and apply them to any table you build, and you can export your tables to MS Word®, PDF, HTML, LaTeX, MS Excel®, or Markdown and include them in reports. Users have been asking us for better tables. Bayesian linear and nonlinear DSGE models Bayesian longitudinal/panel-data modelsĢ0. Difference-in-differences (DID) and DDD modelsġ2. Below, we list some highlights of the release, and we tell you a little more about the first 13 of them.Ĥ. We are excited to introduce you to the new features in Stata 17.