Anna Simoni is a Research Director at the CNRS, affiliated with CREST, and Professor of Statistics and Econometrics at ENSAE Paris. She is also a Hi! PARIS Fellow for the 2021 – 2025 period.

She obtained her PhD and Habilitation à Diriger des Recherches (HDR) from the Toulouse School of Economics. She serves as Associate Editor of the Journal of Econometrics and has been a Fellow of the Journal of Econometrics since 2024 and a Fellow of the Institut Louis Bachelier since 2021. Since 2025, she has also been a founding member of the Economic Data Science Society. Her research lies at the intersection of statistics, econometrics, machine learning, and data science, with a focus on developing methodological tools for the analysis of complex economic and financial data.

Her Hi! PARIS fellowship project focuses on Macroeconomic nowcasting with high-dimensional Google Search data: theory and practice.

The project develops machine learning tools tailored for a time-series environment with the aim of nowcasting macroeconomic and financial aggregates by using Google Search data together with official data. Nowcasting aims at providing an evaluation of the macroeconomic and financial activity in real time, which is crucial for the policy-maker to promptly react to changes in the economy with appropriate counter-cyclical economic policies. Indeed, the problem that forecasters face is that official series are often published with a delay. The project is interested in exploiting in the best possible way the economic informational content of Google Search data and in seeing how the combination of these data with the official series (when available) can improve prediction accuracy. Because Google Search data contains an ultra-high number of variables (categories) compared with the time dimension, the first goal is to shape pretesting procedures that retain only the more important Google Search variables to improve nowcasting accuracy.