For meeting our customers geospatial needs, we have partnered with Esri India Technologies Ltd., the leading Geographic Information Systems (GIS) software and solutions provider.
We are authorised Business Partners of Esri India for providing our customers
advanced machine-learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications. Its ease of use, flexibility and scalability make IBM SPSS accessible to users with all skill levels and outfits projects of all sizes and complexity to help you and your organization to find new opportunities, improve efficiency and minimize risk.
We are authorised Business Partners of Esri India for providing our customers
advanced machine-learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications. Its ease of use, flexibility and scalability make IBM SPSS accessible to users with all skill levels and outfits projects of all sizes and complexity to help you and your organization to find new opportunities, improve efficiency and minimize risk.
We are authorised Business Partners of Esri India for providing our customers
advanced machine-learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications. Its ease of use, flexibility and scalability make IBM SPSS accessible to users with all skill levels and outfits projects of all sizes and complexity to help you and your organization to find new opportunities, improve efficiency and minimize risk.
We are authorised Business Partners of Esri India for providing our customers
advanced machine-learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications. Its ease of use, flexibility and scalability make IBM SPSS accessible to users with all skill levels and outfits projects of all sizes and complexity to help you and your organization to find new opportunities, improve efficiency and minimize risk.
Live Stata webinar
Webinar: Heterogenous Difference in Differences
About the Webinar
The last years have seen an explosion in the difference-in-differences (DID) literature. We have moved from assuming treatment effects did not change over group or time to assuming treatment effects change over group and time. We have embraced heterogeneity.
Stata 18 introduced two commands (each with four estimators) to fit heterogeneous (DID) models: hdidregress for repeated cross-sectional data and xthdidregress for panel/longitudinal data. In this webinar, we briefly introduce the theory behind both estimators and then show how to fit heterogeneous DID models using the new commands. We also demonstrate postestimation tools to aggregate and visualise heterogeneous treatment effects and perform diagnostic tests.
Join us for this one-hour webinar, and learn how you can make your interactions with Stata more efficient and more effective.
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How to Join
The webinar is free, but you must register to attend. Registrations are limited so register soon, by later today.
You will receive an email prior to the start with instructions on how to access the webinar.
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Webinar: Heterogenous Difference in Differences
Tomorrow, Tuesday, September 12, 2023
9.30pm to 10.30pm IST
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Di Liu is a Principal Econometrician in the econometric development team at StataCorp LLC. Di is fascinated by writing statistical software for researchers and doing research in both theoretical and applied econometrics. He is the primary developer of some Stata features, including heterogeneous DID, instrumental variable quantile regression, treatment effects estimation using lasso, lasso for prediction, lasso for inference, spatial autoregressive models, heckpoisson, and betareg. He also published research articles in Canadian Journal of Economics, Econometrics Reviews, Empirical Economics, Econometrics and Statistics, and the Stata journal. Di has a PhD degree in economics from Concordia University in Montreal, Canada; an engineer's degree in software engineering and statistics from Polytech'Lille in Lille, France; and master's and bachelor's degrees in computer science from Hohai University in Nanjing, China.
