The course will introduce all the basic tools for numerical analysis of equilibria in dynamic economic models including conventional computational-economic methods and recent machine learning from the field of data science.

By the end of the course the participants will have acquired detailed knowledge of and hands-on experience in:

• Key concepts and notions of economic dynamics (Bellman equation, Euler equation, Markov decision process, state and control variables, recursive equilibrium, non-stationary models, etc.).
• Conventional computational methods for analyzing dynamic economic models, including perturbation, projection and stochastic simulation approaches.
• Methods of computational economics for ameliorating the curse of dimensionality including sparse grids, ergodic set methods, non-product integration, quasi-Monte Carlo methods, endogenous grid method, parallelization, etc.
• Machine learning tools from the fields of supervised, unsupervised and reinforcement learning, including linear and logistic regression, cluster analysis, dimensionality reduction, decision trees, principal component analysis, stochastic optimization, etc.
• Applications of neural networks and deep learning methods for analyzing high dimensional economic models including heterogeneous-agent new Keynesian model with incomplete markets, large scale models of central banking.
• Matlab and python computer codes for analyzing dynamic economic models including deep learning libraries such as TensorFlow.