Grüß Gott, Welcome, Benvenuti, Bem-vindo!


Hi, I'm Max, a PhD Candidate in Economics.

Currently visiting UQAM - Université du Québec à Montréal


My research interests are:

climate econometrics, machine learning, networks in macroeconomics, macroeconomics and many more!





Related Topics

machine learning, neural networks, forecasting, deep learning

The inversion of the yield-curve has long held up as the single most prominent early-warning indicator of a looming recession in the United States.


Yet, recessions are arguably the result of a complex convolution of many macroeconomic aggregates. Such a setting strains the capabilities of orthodox models. Still, the combination of machine learning (ML) and a plethora of macroeconomic indicators have a hard time outperforming classical models.


Beyond providing a plain point-forecast and quantifying its uncertainty, I address the criticism of ML's limited interpretability. The results corroborate the standing of the yield-curve as the principal predictor of U.S. recessions, followed by labor- and stock-market indicators.


Bundling predictive ability and interpretability within a single package, I propose YSNet, a structural neural network architecture that leverages the predictive power of the yield-curve while conditioning its time-varying relation with the probability of a recession on the state of the economy. At the one-year ahead forecasting horizon, it outperforms all other competitors in terms of the AUC score. The results advocate for carefully designed ML to be valuable addition to the econometric toolbox.