About
Fernando Rios-Avila is a PhD economist with over a decade of experience in applied econometrics, specializing in areas like poverty and inequality analysis, labor economics, and data-driven policy evaluation.
His research interests include causal inference, decomposition methods, and understanding labor market dynamics. Currently, he is exploring the potential of Generative AI and its applications within data science, economic research, and public health.
Current projects include expanding the analysis of measurement errors by comparing data for Austria and the United Kingdom. Also working on expanding the quantile regression via models using multiple fixed effects, as well as expanding the application of Correlated Random Effect models with multiple fixed effects, on non-linear models.
Publications
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