I am a doctoral researcher in economics, on the job market 2024-25. My focus is on developing a machine learning Transformer framework for macroeconometric analysis.

My job market paper proposes a new Transformer algorithm for estimating non-linear dynamic factors from time series data, requiring minimal identifying assumption. The results are improved substantially on small datasets by using conventional factor models, namely the Kalman filter, as prior information to guide the estimation. Prior information is supplied into the loss function through a new regularization term. The results can be interpreted with Attention matrices, which show the impact of each variable's every lag on the output.