Serguei Maliar


  1. Chase Coleman, Spencer Lyon, Lilia Maliar and Serguei Maliar, (2018). "“Matlab, Python, Julia: What to Choose in Economics?"" CEPR working paper DP 13210, MATLAB, python and julia codes for neoclassical growth and new Keynesian models are available from QuantEcon site.

  2. Vadym Lepetuyk, Lilia Maliar and Serguei Maliar (2017). "Should Central Banks Worry about Nonlinearities of Their Large-Scale Macroeconomic Models?" Bank of Canada staff paper #2017-21.

  3. Kenneth L. Judd, Lilia Maliar and Serguei Maliar, (2016). “Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models”, Econometrica 85(3), 991-1020.

  4. 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.

  5. 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.

  6. 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.

  7. Lilia Maliar, Serguei Maliar, John Taylor and Inna Tsener (2015). “A Tractable Framework for Analyzing a Class of Nonstationary Markov Models”, NBER 21155.

  8. 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).

  9. 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.

  10. 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

    - Income Club Investment Company


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

NSF grant:

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

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.