Professor Áureo de Paula

Implementing a range of production function estimators on UK data, we find our results are similar to or more credible than widely-used alternatives.

This paper investigates the intergenerational transmission of socio-emotional skills during childhood, using the 1970 British Cohort Study in the UK.

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.