DP14024 Will Artificial Intelligence Replace Computational Economists Any Time Soon?
|Author(s):||Lilia Maliar, Serguei Maliar, Pablo Winant|
|Publication Date:||September 2019|
|Date Revised:||September 2019|
|Keyword(s):||artificial intelligence, Bellman equation, deep learning, Dynamic Models, Dynamic programming, Euler Equation, Machine Learning, neural network, stochastic gradient, value function|
|Programme Areas:||Monetary Economics and Fluctuations|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=14024|
Artificial intelligence (AI) has impressive applications in many fields (speech recognition, computer vision, etc.). This paper demonstrates that AI can be also used to analyze complex and high-dimensional dynamic economic models. We show how to convert three fundamental objects of economic dynamics -- lifetime reward, Bellman equation and Euler equation -- into objective functions suitable for deep learning (DL). We introduce all-in-one integration technique that makes the stochastic gradient unbiased for the constructed objective functions. We show how to use neural networks to deal with multicollinearity and perform model reduction in Krusell and Smith's (1998) model in which decision functions depend on thousands of state variables -- we literally feed distributions into neural networks! In our examples, the DL method was reliable, accurate and linearly scalable. Our ubiquitous Python code, built with Dolo and Google TensorFlow platforms, is designed to accommodate a variety of models and applications.