Machine Learning for Economics and Finance in TensorFlow 2 Deep Learning Models for Research and Industry

TensorFlow not only provides us with a means of training deep learning models but also offers a suite of tools that can be used to solve arbitrary mathematical models. This includes models that are commonly used in economics and finance. In this chapter, we examined how to do this using a toy model (the cake-eating model) and a common benchmark in the computational literature: the neoclassical business cycle model. Both models are trivial to solve using conventional methods in economics, but provide a simple means of demonstrating how TensorFlow can be used to solve theoretical models of relevance for economists. We also showed how deep reinforcement learning could be used as an alternative to standard methods in computational economics. In particular, using deep Q-learning networks (DQN) in TensorFlow may enable economists to solve higher-dimensional models in a non-linear setting without changing model assumptions or introducing a substantial amount of numerical error.