Serguei Maliar

New! Deep learning solution method using TensorFlow

    - Description of the DL method: solving Krusell and Smith's (1998) model by constructing decision functions with 2001 state variables

    - Download Python and TensorFlow code,

    - Video recording of 2020 two-day PhD minicourse at Columbia University "Artificial intelligence and deep learning solution methods for dynamic economic models"

    - Video recording of CEF-2018 conference presentation where the DL method is introduced


    1. Lilia Maliar, Serguei Maliar and Pablo Winant (2021). "Deep learning for solving dynamic economic models.", Journal of Monetary Economics 122.

      - Version 2018. Earlier version is titled "Will artificial intelligence replace computational economists any time soon?"

    2. Lilia Maliar, Serguei Maliar and Inna Tsener (2020). “Capital-Skill Complementarity and Inequality: Twenty Years After;, CEPR DP15228.

      - Data are available from Inna Tsener's webpage.

    3. Vadym Lepetyuk, Lilia Maliar and Serguei Maliar (2020). "When the U.S. catches a cold, Canada sneezes: a lower-bound tale told by Deep learning?" Journal of Economic Dynamics & Control 117 (2020), 103926.

    4. Lilia Maliar, Serguei Maliar, John Taylor and Inna Tsener (2019). “A Tractable Framework for Analyzing a Class of Nonstationary Markov Models”, Quantitative Economics 11/4, 1289-1323.

      - Version 2015. Earlier version with many applications, NBER21155.

    5. Chase Coleman, Spencer Lyon, Lilia Maliar and Serguei Maliar, (2018). "Matlab, Python, Julia: What to choose in economics?"Computational Economics, forthcoming, MATLAB, python and julia codes for neoclassical growth and new Keynesian models are available from QuantEcon site.

    6. Kenneth L. Judd, Lilia Maliar and Serguei Maliar, (2017). “Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models”, Econometrica 85(3), 991-1020.

    7. Kenneth L. Judd, Lilia Maliar, Serguei Maliar and Inna Tsener, (2016). “How to solve dynamic stochastic models computing expectations just once”, Quantitative Economics 8 (3), 851-893.

    8. Cristina Arellano, Lilia Maliar, Serguei Maliar and Viktor Tsyrennikov, (2016). “Envelope Condition Method with an Application to Default Risk Models”, Journal of Economic Dynamics and Control 69, 436-459.

    9. Lilia Maliar and Serguei Maliar, (2016). “Ruling Out Multiplicity of Smooth Equilibria in Dynamic Games: A Hyperbolic Discounting Example”, Dynamic Games and Applications 6(2), 243–261, in special issue "Dynamic Games in Macroeconomics" edited by Edward C. Prescott and Kevin L Reffett.

    10. Lilia Maliar and Serguei Maliar, (2015). “Merging Simulation and Projection Aproaches to Solve High-Dimensional Problems with an Application to a New Keynesian model”, Quantitative Economics 6, 1-47 (LEAD ARTICLE).

    11. Kenneth L. Judd, Lilia Maliar, Serguei Maliar and Rafael Valero, (2014). “Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain”, Journal of Economic Dynamic and Control 44(C), 92-123.

    12. Lilia Maliar and Serguei Maliar, (2013). “Envelope Condition Method versus Endogenous Grid Method for Solving Dynamic Programming Problems”, Economic Letters 120, 262-266.


      - Journal of Economic Dynamics & Control


      - Canadian Central Bank, Model Development Division


      - Becker Friedman Institute at the University of Chicago, Macro Financial Modeling group

    NSF grants:

      - Analyzing non-stationary and unbalanced growth economic models, SES-1559407, 08/15/2016- 07/31/2019.

      - Artificial intelligence and deep learning solution methods for dynamic economic models, SES-1949430, 05/01/2020-04/30/2023.

    MINICIURSE "Solution Methods for State-Dependent and Time-Dependent Models" taught for the Federal Reserve Board and for the SCE - 2017 meeting:

      - Slides

      - Codes and papers


      Lilia Maliar and Serguei Maliar, (2014). "Numerical Methods for Large Scale Dynamic Economic Models” in: Schmedders, K. and K. Judd (Eds.), Handbook of Computational Economics, Volume 3, Chapter 7, 325-477, Amsterdam: Elsevier Science.

      Summary. This chapter provides an introduction to perturbation, projection, value function iteration, Smolyak, endogeneous grid and envelope condition methods, parallel computation, supercomputers, GPUs and many other methods and shows how to use these methods to solve dynamic stochastic economic models with hundreds of state variables. Check our MATLAB codes.