Econometrics

Jin Zhu examines statistical approaches to identifying text produced by large language models. The increasing availability of fluent generated writing raises questions about misinformation, academic integrity and the authenticity of digital content. Although detection has attracted substantial machine-learning research, the talk argues that its statistical foundations remain underdeveloped. Zhu presents recent work on principled, computationally efficient detection methods, including approaches reported at NeurIPS and ICLR. An interactive demonstration accompanies the research, illustrating one of the proposed methods.

ISEC brings together ecologists, statisticians and quantitative researchers working on ecological inference and modelling. The main conference runs 10–15 January 2027 in Mérida, Mexico; separate pre-conference workshops run 8–9 January. Registration is open, with early rates through 30 September 2026 and reduced rates for eligible Global South participants.

Mathematics and statistics postdoctoral research combines institutional affiliation with scientific mentorship. Choose a research fellowship or research instructorship. Full proposals are due October 21, 2026, at 5 p.m. submitter local time.

Victor Chernozhukov develops sharp bounds on omitted-variable bias for a broad class of causal quantities. The framework covers averages of potential outcomes, average treatment effects, average derivatives, and policy effects generated by shifts in covariate distributions within general nonparametric causal models. Using the Riesz–Fréchet representation of the target quantity, the analysis expresses the bias bound through the additional variation that unobserved variables introduce into the outcome and the relevant Riesz representer. Debiased machine learning then provides flexible statistical inference for the components of these bounds that can be learned from observed data. The approach connects sensitivity analysis for unmeasured confounding with modern causal estimation.

This research panel explores how large and unconventional datasets can produce high-frequency inflation forecasts. Its starting point is renewed concern about inflation following substantial monetary and fiscal stimulus. Alberto Cavallo presents research on real-time inflation measurement. Yves Lemperiere, Adam Rej, José Scheinkman and Michael Woodford join the discussion of alternative data and its economic and financial applications. The panel connects timely measurement with the problem of assessing inflation while conventional economic statistics arrive with a delay.

Ariel Pakes introduces the foundations of demand estimation for differentiated products. Starting from individual preferences and choices, he explains how consumer heterogeneity can be aggregated into market demand and used to study prices, substitution and welfare. The lecture compares modelling products directly with representing them through characteristics, including observed and unobserved attributes. It examines how different assumptions about tastes generate substitution patterns, using examples involving cars and other differentiated goods. Pakes discusses logit models, vertical and horizontal differentiation, and the implications of demand assumptions for markups, competition and policy analysis.