Econometrics seminars

Susan Athey examines how machine learning and digital interventions can help explain and improve workers’ transitions between jobs. One project uses Swedish administrative data to identify groups whose earnings and employment are less resilient after layoffs, revealing substantial differences among workers within the same firms and labour markets. A second line of work uses transformer models and large language models to represent careers and analyse gender wage gaps, identifying settings in which large unexplained differences persist. Two further projects develop and evaluate digital interventions intended to help disadvantaged workers enter expanding occupations in information technology and data science. The lecture connects new methods for measuring labour-market disadvantage with evidence about practical interventions.

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.

Recent economics literature has seen a revival of interest to psychologically-grounded theories of decision under risk. We review the recent proposals in this direction, compare it to classical estimations based on utility functions, and discuss their appropriateness using some original experimental data.

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.

Framing effects in individual decision-making have puzzled economists for decades because they are hard, if at all, to explain with rational choice theories. Why should mere changes in the description of a choice problem affect decision-making? Here, we examine the hypothesis that changes in framing cause changes in the allocation of attention to the different options – measured via eye-tracking – and give rise to changes in decision-making. We document that the framing of a sure alternative as a gain – as opposed to a loss – in a risk-taking task increases the attentional advantage of the sure option and induces a higher choice frequency of that option – a finding that is predicted by the attentional drift-diffusion model (aDDM). The model also correctly predicts other key findings such as that the increased attentional advantage of the sure option in the gain frame should also lead quicker decisions in this frame. In addition, the data reveal that increasing risk aversion at higher stake sizes may also be driven by attentional processes because the sure option receives significantly more attention – regardless of frame – at higher stakes. We also corroborate the causal impact of framing-induced changes of attention on choice with an additional experiment that manipulates attention exogenously. Finally, to study the precise mechanisms underlying the framing effect we structurally estimate an aDDM that allows for frame and option-dependent parameters. The estimation results indicate that – in addition to the direct effects of framing-induced changes in attention on choice – the gain frame also causes (i) an increase in the attentional discount of the gamble and (ii) an increased concavity of utility. Our findings suggest that the traditional explanation of framing effects in risky choice in terms of a more concave value function in the gain domain is seriously incomplete and that attentional mechanisms as hypothesized in the aDDM play a key role.

Nassim Nicholas Taleb develops two approaches to reasoning about extreme risk. In the first part, on the law of large numbers in the real world, he defines fat-tailed distributions and examines why conventional statistical procedures can perform poorly for economic variables. He stresses the much larger data requirements and the choice of estimators when rare observations dominate outcomes. The second part considers how to detect fragility in portfolios. Taleb describes fragility as sensitivity to volatility and argues for analysing the shape of an exposure and its response to shocks when reliable numerical estimates of tail probabilities are unavailable. The lecture connects limitations of statistical estimation with practical heuristics for identifying vulnerability.

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.