Health expenditure data almost always include extreme values, implying that the underlying distribution has heavy tails. This may result in infinite variances as well as higher-order moments and bias the commonly used least squares methods. To accommodate extreme values, we propose an estimation...

Building on automatic differentiation, I propose a solution method for heterogeneous agents models with many aggregate equations which allows to account even for strong nonlinearities. A powerful open source reference implementation is provided which typically solves the canonical HANK model...

Traditional asset pricing tests boil down to evaluating the maximum Sharpe ratio obtained from the factors in a given model. This implicitly assumes the linear stochastic discount factor (SDF) that prices the factors as the asset pricing model. We generalize this approach by considering a...

We use a Bayesian Threshold Vector Autoregression model identified through sign and narrative restrictions to uncover non-linearities in the propagation of energy supply shocks. We find that the transmission of energy supply shocks on consumer prices is stronger in high-inflation regimes,...

We present a pathwise deep Backward Stochastic Differential Equation (BSDE) method for Forward Backward Stochastic Differential Equations with terminal conditions that time-steps the BSDE backwards and apply it to the differential rates problem as a prototypical nonlinear problem of independent...

While demand models require a sound understanding of economic processes and should be flexible enough to capture nonlinearities, endogeneity can greatly hinder the identification of (nonlinear) causal relationships. To tackle these issues, we extend the instrument-free Gaussian copula approach...

Response functions that link regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is usually not further scrutinized. For...