We introduce deep equilibrium nets (DEQNs)—a deep learning‐based method to compute
approximate functional rational expectations equilibria of economic models featuring a …

We propose a novel structural estimation framework in which we train a surrogate of an
economic model with deep neural networks. Our methodology alleviates the curse of …

Solving dynamic economic models that capture salient real-world heterogeneity and non-
linearity requires the approximation of high-dimensional functions. As their dimensionality …

In this work, we develop a novel efficient quadrature and sparse grid based polynomial
interpolation method to price American options with multiple underlying assets. The …

We propose a scalable method for computing global solutions of nonlinear, high-
dimensional dynamic stochastic economic models. First, within a time iteration framework …

We introduce and deploy a generic, highly scalable computational method to solve high-
dimensional dynamic stochastic economic models on high-performance computing …

Maximum simulated likelihood estimation of mixed multinomial logit models requires
evaluation of a multidimensional integral. Quasi-Monte Carlo (QMC) methods such as …

We introduce a comprehensive computational framework for solving dynamic portfolio
choice problems with many risky assets, transaction costs, and borrowing and short-selling …

Abstract We introduce``deep surrogates''--high-precision approximations of structural
models based on deep neural networks, which speed up model evaluation and estimation …

I am thankful to many people for guiding me through my Ph. D. studies. First and foremost, I
would like to express my deepest gratitude to my advisor, Prof. Dr. Erich Walter Farkas, for …