Vasilis Sarafidis

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December 2025: It was a real honour to take part in a tribute session for Hashem Pesaran at CFE 2025, hosted at Birkbeck, University of London, and contribute a paper, especially with Hashem present. His landmark work across time series, panel data, macroeconometric modelling and forecasting has opened new literatures, created space for hundreds of others to follow, and shaped my own thinking through his writings and later conversations. This is new work on model selection in high-dimensional regression, joint with George Kapetanios and Alexia Ventouri. Slides from my presentation are here.  

November 2025: I am grateful to Enrique Pinzon, Stata Corporation and everyone who joined the 2025 Stata Economics Virtual Symposium. My talk on “Spatial dynamic panel data models with interactive effects,” drew more than 200 attendees and generated a lively online discussion at the end. My presentation slides are available in two different formats: static, and dynamic.

October 2025: I am delighted to be an invited speaker at the 2025 Stata Economics Virtual Symposium. I’ll present ‘Spatial dynamic panel data models with interactive effects’ on Thursday, 6 November 2025, 08:30 CST (14:30 GMT/UK). The event will be conducted online at no cost. As capacity is limited, please register via: 

https://www.stata.com/symposiums/economics25/ 

July 2025: Our paper on "Estimating Spatial Dynamic Panel Data Models with Unobserved Common Factors in Stata" is now available online. We introduce the spxtivdfreg package in Stata, which implements a general instrumental variables (IV) approach for estimating dynamic spatial panel data models with unobserved common factors or interactive effects, when the number of both cross-sectional and time series observations is large. The spxtivdfreg package allows for both homogeneous and heterogeneous slope coefficients. In addition, the spxtivdfreg package allows estimation of short-run and long-run direct and indirect effects.

June 2025: New research on "Heterogeneous Exposures to Systematic and Idiosyncratic Risk across Crypto Assets: A Divide-and-Conquer Approach". We  analyse realized return behavior across a broad set of crypto assets by estimating heterogeneous exposures to idiosyncratic and systematic risk. A key challenge arises from the latent nature of broader economy-wide risk sources: macro-financial proxies are unavailable at high-frequencies, while the abundance of low-frequency candidates offers limited guidance on empirical relevance. To address this, we develop a novel two-stage ``divide-and-conquer'' approach. Details are available at the link provided.

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