Today's speaker: Neil Shepard (Harvard Departments of Statistics and Economics), "When do common time series estimands have nonparametric causal meaning"
The nonparametric potential outcome system provides a foundational framework for giving conditions under which common predictive time series statistical estimands, such as the impulse response function, generalized impulse response function, local projection and local projection instrument variables, have a nonparametric causal interpretation in terms of dynamic causal effects.
This is joint work with Ashesh Rambachan.
The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.
More information is available at the Gov 3009 website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009