I am a researcher combining economics and machine learning, on the job market 2025–26. My focus is on developing a Transformer framework for macroeconomic analysis.

My job market paper proposes a Transformer architecture for estimating nonlinear dynamic factors from time series data under flexible identification assumption. Performance on small datasets is improved substantially by using a conventional factor model as prior information via a regularization term in the training objective. The results are interpreted with Attention matrices that quantify the relative importance of variables and their lags for the factor estimate. Time variation in Attention patterns can help detect regime switches and evaluate narratives. Monte Carlo experiments suggest that the Transformer is more accurate than the linear factor model, when the data deviate from linear-Gaussian assumptions. An empirical application uses the Transformer to construct a coincident index of U.S. real economic activity.