Serguei Maliar


Deep learning for solving dynamic economic models:

    DL for HANK (CEPR 2020, DP15614, joint with Yuriy Gorodnichenko, Lilia Maliar and Christopher Naubert)

    DL for Krusell-Smith: constructing decision functions with 2001 state variables (JME 2021, joint with Lilia Maliar and Pablo Winant)

    - Python and TensorFlow code for one-agent problem.

    DL method for solving large-scale bToTEM model of Bank of Canada (JEDC 2020, joint with Vadym Lepetyuk and Lilia Maliar)

    - Matlab code for solving bToTEM model of Bank of Canaga.

    DL classification method for Krusell-Smith with discrete choice: modeling indivisible labor (JEDC 2021, joint with Lilia Maliar)

    Recording of 2020 PhD minicourse at Columbia University "Artificial intelligence and deep learning solution methods for dynamic economic models"

    Recording of CEF-2018 presentation where the DL method was introduced



Recent papers:

  1. Serguei Maliar and Bernard Salanie (2026). "Testing for Asymmetric Information with Neural Networks", CEPR DP 19105.

  2. Yuriy Gorodnichenko, Lilia Maliar, Serguei Maliar and Christopher Naubert (2026). "U.S. versus Europe: How Differential COVID-19 Policies Affect Inequality", manuscript.

  3. Keith Kuester, Lilia Maliar, Serguei Maliar and Josef Schroth (2026). "Macroprudential Policy and Precautionary Savings", manuscript.

  4. Yuriy Gorodnichenko, Lilia Maliar, Serguei Maliar and Christopher Naubert (2026). “Household Savings and Monetary Policy under Individual and Aggregate Stochastic Volatility", CEPR DP 15614.

  5. Laurence Kotlikoff, Seung Lee, Lilia Maliar and Serguei Maliar (2026), “Long-Term Implications of Aging Population in the Macroeconomy”. manuscript.

  6. James Kahn and Serguei Maliar (2026). "What Drives Housing Prices?" manuscript.

  7. Vadym Lepetyuk, Lilia Maliar, Serguei Maliar and John Taylor (2026). “The power of open mouth policies;, Journal of Money, Credit and Banking DOI: 10.1111/jmcb.70083.

  8. Lilia Maliar, Serguei Maliar and Inna Tsener (2022). “Capital-Skill Complementarity and Inequality: Twenty Years After;, Economics Letters 220, 110844.

    - Data are available from Inna Tsener's webpage.

  9. Lilia Maliar, Serguei Maliar and Pablo Winant (2021). "Deep learning for solving dynamic economic models.", Journal of Monetary Economics 122, 76-101.

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

  10. 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, 103926.

  11. Chase Coleman, Spencer Lyon, Lilia Maliar and Serguei Maliar, (2020). "Matlab, Python, Julia: What to choose in economics?" Computational Economics 58, pages 1263–1288.

    - MATLAB, python and julia codes for neoclassical growth and new Keynesian models are available from QuantEcon site.

  12. Lilia Maliar, Serguei Maliar, John Taylor and Inna Tsener (2020). “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.

  13. 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.

ASSOCIATE EDITOR:

    - Journal of Economic Dynamics & Control

ADVISER:

    - Canadian Central Bank, Model Development Division

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.

    - Slides

    - Codes and papers

HANDBOOK OF COMPUTATIONAL ECONOMICS:

    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.