Estimation uncertainty in repeated finite populations
Selected for the REStud North America Tour 2025
Abstract
Draft
Often, datasets cover much of the population under study — think of censuses
of firms or workers, or state-level panels. Yet, standard practice remains to treat the
sample as drawn from a hypothetical superpopulation, and classical finite-population
adjustments are of limited use since they rule out unobserved heterogeneity. In this
paper, I study settings where interest is in population averages over a latent characteristic, and the data only provides noisy, repeated measurements. I show that
conventional standard errors are generally too large, and propose Finite Population
Corrections (FPCs) that guarantee non-conservative inference. FPCs are very simple
to implement via covariance restrictions. I apply these to (i) predicting lethal police
encounters using data from all U.S. police departments and (ii) studying labor misallocation from a census of Indonesian firms. FPCs yield standard errors that properly
combine uncertainty from measurement and from sampling — and lead to confidence
intervals that are up to 50% shorter in these applications.
Micro responses to macro shocks
w/
Martín Almuzara
Reject and Resubmit at AER
Abstract
Draft
Supplement
Slides
Code
We study panel data regression models when the shocks of interest are aggregate
and there are omitted macro and micro-level shocks of any relative size. This speaks to
a large empirical literature that targets impulse responses via panel local projections.
We show how to interpret the estimated coefficients when responses are heterogeneous
and that a simple recipe leads to uniformly valid inference over the macro–micro composition
of the errors: including lags as controls and then clustering at the time level.
Finally, we use our methods to reassess the role of firm financial frictions in shaping
the transmission of monetary policy.
Estimating flexible income processes from subjective expectations data: evidence from India and Colombia
w/
Manuel Arellano,
Orazio Attanasio and
Sam Crossman
Revise and Resubmit at JPE: Micro
Abstract
Draft
NBER version
We develop a methodology for modeling household income processes when subjective probabilistic assessments of future income are available. This allows us to flexibly estimate conditional cdfs directly using elicited individual subjective probabilities,
and to obtain empirical measurements of subjective risk and persistence. We then use two longitudinal surveys collected in rural India and rural Colombia to explore the nature of income dynamics in those contexts. Our results suggest linear income processes
are rejected in favor of more flexible versions in both cases; subjective income distributions feature heteroskedasticity, conditional skewness and nonlinear persistence.
