Professor Áureo de Paula

We study consumer spending dynamics during one such time, the first infection wave of the COVID-19 pandemic, using household scanner data covering fast-moving consumer goods in the United Kingdom.

This paper first reviews some of the approaches that have been taken to estimate the common parameters of binary outcome models with fixed effects.

We study testable implications of multiple equilibria in discrete games with incomplete information. Unlike de Paula and Tang (2012), we allow the players’ private signals to be correlated.

We study consumer spending dynamics during the first infection wave of the COVID-19 pandemic using household scanner data covering fast-moving consumer goods in the United Kingdom.

Nearly one-quarter of married, fertile-age women in Sub-Saharan Africa say that they want to avoid pregnancy but are not using contraceptives.

This article provides a selective review on the recent literature on econometric models of network formation.

This paper introduces measures for how each moment contributes to the precision of parameter estimates in GMM settings.

We present results on the identification of social networks from observational panel data that contains no information on social ties between agents.

This paper introduces measures for how each moment contributes to the precision of the parameter estimates in GMM settings.

It is almost self-evident that social interactions can determine economic behavior and outcomes. Yet, information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents.

High‐risk sexual behaviours are generally unobserved and difficult to identify. In this paper, we investigate the accuracy of two risky‐behaviour measures: biomarkers for sexually transmitted infections (STIs) and self‐reported data. We build an epidemiological model to assess the relative performance of biomarkers versus self‐reported data.

The negative consequences of long-term exposure to particulate pollution are well established but a number of studies find no effect of short-term exposure on health outcomes. The high correlation of industrial pollutants complicates the estimation of the impact of individual pollutants on health.

It is almost self-evident that social interactions can determine economic behavior and outcomes. Yet, information on social ties does not exist in most publicly available and widely used datasets. We present methods to recover information on the entire structure of social networks from observational panel data that contains no information on social ties between individuals.