Estimation uncertainty in repeated finite populations

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

We study panel data regression models when the shocks of interest are aggregate and possibly small relative to idiosyncratic noise. This speaks to a large empirical literature that targets impulse responses via panel local projections. We show how to interpret the estimated coefficients when units have heterogeneous responses and how to obtain valid standard errors and confidence intervals. A simple recipe leads to robust inference: including lags as controls and then clustering at the time level. This strategy is valid under general error dynamics and uniformly over the degree of signal-to-noise of macro shocks.

Estimating flexible income processes from subjective expectations data: evidence from India and Colombia

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.

Heterogeneity in impulse response functions

The extent of heterogeneity in impulse responses is a key empirical target for many substantive questions. For instance, it is helpful to understand the transmission of monetary policy shocks to household- and firm-level outcomes. We propose a method to estimate the cross-sectional distribution of impulse responses in a panel data context. Our method combines the simplicity of estimating separate standard local projections for each horizon with additional common structure that is naturally present across units and across horizons. From a practical point of view, this structure facilitates documenting and summarizing heterogeneity; from a methodological one, it affords dimensionality reduction that we exploit in estimation. Preliminary simulation evidence supports these findings.

How fixed are fixed-effects? Evidence from the Spanish labor market

A popular approach to account for labor market heterogeneity views aggregate fluctuations as driven by a particular group of workers with low attachment. Behind this is an assumption that a worker's type is approximately constant over the relevant horizon. I propose an econometric framework to evaluate type stability that exploits the nature of longitudinal data and is based on comparing the posterior probability that a worker belongs to a particular unobserved type over short, consecutive panel segments. I find evidence of substantial type dynamics in an application to labor market transitions in Spain over 2016–2018, although preliminary evidence suggests that these are attributable to a relatively small fraction of units.

New measures for richer theories: some thoughts and an example

For a long time, the majority of economists doing empirical work relied on choice data, while data based on answers to hypothetical questions, stated preferences or measures of subjective beliefs were met with some skepticism. Although this has changed recently, much work needs to be done. In this paper, we emphasize the identifying content of new economic measures. In the first part of the paper, we discuss where the literature on measures in economics stands at the moment. We first consider how the design and use of new measures can help identify causal links and structural parameters under weaker assumptions than those required by approaches based exclusively on choice data. We then discuss how the availability of new measures can allow the study of richer models of human behavior that incorporate a wide set of factors. In the second part of the paper, we illustrate these issues with an application to the study of risk sharing and of deviations from perfect risk sharing.

• Econometrics (Teaching Assistant, CEMFI)  —  2022 (eval.), 2023 (eval.)
  Teaching Assistant of the Year (2023)
   An introduction to MCMC methods

• Econometrics (Bocconi, 30413)  —  2025

• Econometrics (Bocconi, 30462)  —  2025

• Microeconomics (Teaching Assistant, Universidad de Cantabria)  —  2015, 2016, 2017