Working Paper, 2026
We propose a time-varying neural network framework for macroeconomic forecasting in which the weights and biases of a neural network evolve over time using a self-driving updating scheme
Recommended citation: Kooiker, S., van Brummelen, J. & Schaumburg, J. (2026). The Forecasting Grand Prix: Self-Driving Neural Networks for Inflation Prediction. Working Paper.
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Working Paper, 2026
We propose a factor model with time-varying loadings for term structure modeling, specifying the loadings as neural networks that evolve over time using a self-driving updating scheme based on past forecast errors.
Recommended citation: Kooiker, S., van Brummelen, J., Schaumburg, J., & Zamojski, M. (2026). Self-Driving Neural Networks for Term Structure Modeling. Working Paper.
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Working Paper, 2025
A procedure that integrates an exponentially weighted moving average version of the Diks-Panchenko test with EWMA local density estimators to assess Granger causality in dynamic environments.
Recommended citation: Kooiker, S. (2025). Nonparametric Time-Varying Granger Causality. Working Paper.
Working Paper, 2024
We propose to forecast multi-horizon Growth-at-Risk using flexible dynamic sequence models combining many-to-many recurrent neural networks with an objective function that guarantees non-crossing of quantile estimates.
Recommended citation: Kooiker, S., Hoesch, L., & Schaumburg, J. (2024). Multi-period Growth-at-Risk Forecasting with Recurrent Neural Network. Working Paper.