COMPUTER CODES
- Python and Tensorflow code for the one-agent model studied in the article by Lilia Maliar, Serguei Maliar and Pablo Winant (2021). “Deep learning for solving dynamic economic models“, Journal of Monetary Economics 122, 76-101.
- Python code for the Krusell and Smith (1998) studied in the article by Lilia Maliar, Serguei Maliar and Pablo Winant (2021). “Deep learning for solving dynamic economic models“, Journal of Monetary Economics 122, 76-101.
- MATLAB code for the article by 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 and Control 117, 103926.
- Python and TensorFlow code from the QuantEcon site for the article by Lilia Maliar, Serguei Maliar and Pablo Winant, (2019). “Will Artificial Intelligence Replace Computational Economists Any Time Soon?” CEPR working paper DP14024.
- MATLAB, python, julia code from the QuantEcon site for the article by Chase Coleman, Spencer Lyon, Lilia Maliar and Serguei Maliar, (2018). “Matlab, Python, Julia: What to Choose in Economics?” CEPR working paper DP13210.
- MATLAB code for the article by Lilia Maliar, Serguei Maliar, John Taylor and Inna Tsener (2015). “A Tractable Framework for Analyzing a Class of Nonstationary Markov Models“, Quantitative Economics, forthcoming.- Earlier version 2015. NBER working paper 21155. Data are available from Inna Tsener’s webpage.
- MATLAB code for the article by Kenneth, L. Judd, Lilia Maliar, Serguei Maliar and Inna Tsener (2017). “How to Solve Dynamic Stochastic Models Computing Expectations Just Once”, Quantitative Economics 8 (3), 851-893.
- MATLAB code for the article by 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.
- MATLAB code for the article by 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.
- MATLAB code for the article by Lilia Maliar and Serguei Maliar, (2013). “Envelope Condition Method versus Endogenous Grid Method for Solving Dynamic Programming Problems”, Economic Letters 120, 262-266.
- MATLAB code for the article by 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.
- MATLAB code for the article by 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).
- MATLAB code for the article by Lilia Maliar, Serguei Maliar and Fernando Valli, (2010). “Solving the Incomplete Markets Model with Aggregate Uncertainty Using the Krusell-Smith Algorithm”, Journal of Economic Dynamics and Control 34, 42-49.
- MATLAB code for the article by Kenneth L. Judd, Lilia Maliar and Serguei Maliar, (2011). “Numerically Stable and Accurate Stochastic Simulation Methods for Solving Dynamic Models”, Quantitative Economics 2, 173-210.
- MATLAB code for the article by Serguei Maliar, Lilia Maliar and Kenneth L. Judd, (2011). “Solving the Multi-Country Real Business Cycle Model Using Ergodic Set Methods” Journal of Economic Dynamic and Control 35(2), 207-228.
- MATLAB code for the article by Lilia Maliar and Serguei Maliar, (2005). “Parameterized Expectations Algorithm: How to Solve for Labor Easily”, Computational Economics 25, 269-274.
- MATLAB code for the article by Lilia Maliar and Serguei Maliar, (2005). “Solving Nonlinear Stochastic Growth Models: an Algorithm Computing Value Function by Simulations”, Economics Letters 87, 135-140.
- MATLAB code for the articleby Lilia Maliar and Serguei Maliar, (2003). “Parameterized Expectations Algorithm and the Moving Bounds”, Journal of Business and Economic Statistics 21/1, 88-92.