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Matlab, Python, Julia: What to Choose in Economics?

Computational economics, 2021-12, Vol.58 (4), p.1263-1288

2021

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Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models

Quantitative economics, 2011-07, Vol.2 (2), p.173-210

2011

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Deep learning classification: Modeling discrete labor choice

Journal of economic dynamics & control, 2022-02, Vol.135, p.104295, Article 104295

2022

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Merging simulation and projection approaches to solve high-dimensional problems with an application to a new Keynesian model

Quantitative economics, 2015-03, Vol.6 (1), p.1-47

2015

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Deep learning for solving dynamic economic models

Journal of monetary economics, 2021-09, Vol.122, p.76-101

2021

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Capital-skill complementarity and inequality: Twenty years after

Economics letters, 2022-11, Vol.220, p.110844, Article 110844

2022

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When the U.S. catches a cold, Canada sneezes: A lower-bound tale told by deep learning

Journal of economic dynamics & control, 2020-08, Vol.117, p.103926, Article 103926

2020

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Odyssean forward guidance in normal times

Journal of economic dynamics & control, 2024-08, Vol.165, p.104877, Article 104877

2024

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Envelope condition method versus endogenous grid method for solving dynamic programming problems

Economics letters, 2013-08, Vol.120 (2), p.262-266

2013

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Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain

Journal of economic dynamics & control, 2014-07, Vol.44, p.92-123

2014

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LOWER BOUNDS ON APPROXIMATION ERRORS TO NUMERICAL SOLUTIONS OF DYNAMIC ECONOMIC MODELS

Econometrica, 2017-05, Vol.85 (3), p.991-1012

2017

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Ruling Out Multiplicity of Smooth Equilibria in Dynamic Games: A Hyperbolic Discounting Example

Dynamic games and applications, 2016-06, Vol.6 (2), p.243-261

2016

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Envelope condition method with an application to default risk models

Journal of economic dynamics & control, 2016-08, Vol.69, p.436-459

2016

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Numerical Methods for Large-Scale Dynamic Economic Models

Handbook of Computational Economics, 2014, Vol.3, p.325-477

2014

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Merging simulation and projection approaches to solve high-dimensional problems with an application to a new Keynesian model: Approaches to solve high-dimensional problems

Quantitative economics, 2015-03, Vol.6 (1), p.1-47

2015

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ENDOGENOUS GROWTH AND ENDOGENOUS BUSINESS CYCLES

Macroeconomic dynamics, 2004-11, Vol.8 (5), p.559-581

2004

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A tractable framework for analyzing a class of nonstationary Markov models

Quantitative economics, 2020-11, Vol.11 (4), p.1289-1323

2020

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Capital-Skill Complementarity and Balanced Growth

Economica (London), 2011-04, Vol.78 (310), p.240-259

2011

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How to solve dynamic stochastic models computing expectations just once

Quantitative economics, 2017-11, Vol.8 (3), p.851-893

2017

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Solving the incomplete markets model with aggregate uncertainty using the Krusell–Smith algorithm

Journal of economic dynamics & control, 2010, Vol.34 (1), p.42-49

2010

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Capital-Skill Complementarity and Balanced Growth: Capital-Skill Complementarity

Economica (London), 2011-04, Vol.78 (310), p.240-259

2011

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Solving the multi-country real business cycle model using ergodic set methods

Journal of economic dynamics & control, 2011-02, Vol.35 (2), p.207-228

2011

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How to solve dynamic stochastic models computing expectations just once: How to solve dynamic stochastic models

Quantitative economics, 2017-11, Vol.8 (3), p.851-893

2017

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Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions

Computational economics, 2013-10, Vol.42 (3), p.307-325

2013

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Deep Learning: Solving HANC and HANK Models in the Absence of Krusell-Smith Aggregation

SSRN Electronic Journal

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Matlab, Python, Julia: What to Choose in Economics?

Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models

Deep learning classification: Modeling discrete labor choice

Merging simulation and projection approaches to solve high-dimensional problems with an application to a new Keynesian model

Deep learning for solving dynamic economic models

Capital-skill complementarity and inequality: Twenty years after

When the U.S. catches a cold, Canada sneezes: A lower-bound tale told by deep learning

Odyssean forward guidance in normal times

Envelope condition method versus endogenous grid method for solving dynamic programming problems

Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain

LOWER BOUNDS ON APPROXIMATION ERRORS TO NUMERICAL SOLUTIONS OF DYNAMIC ECONOMIC MODELS

Ruling Out Multiplicity of Smooth Equilibria in Dynamic Games: A Hyperbolic Discounting Example

Envelope condition method with an application to default risk models

Numerical Methods for Large-Scale Dynamic Economic Models

Merging simulation and projection approaches to solve high-dimensional problems with an application to a new Keynesian model: Approaches to solve high-dimensional problems

ENDOGENOUS GROWTH AND ENDOGENOUS BUSINESS CYCLES

A tractable framework for analyzing a class of nonstationary Markov models

Capital-Skill Complementarity and Balanced Growth

How to solve dynamic stochastic models computing expectations just once

Solving the incomplete markets model with aggregate uncertainty using the Krusell–Smith algorithm

Capital-Skill Complementarity and Balanced Growth: Capital-Skill Complementarity

Solving the multi-country real business cycle model using ergodic set methods

How to solve dynamic stochastic models computing expectations just once: How to solve dynamic stochastic models

Taking Perturbation to the Accuracy Frontier: A Hybrid of Local and Global Solutions

Deep Learning: Solving HANC and HANK Models in the Absence of Krusell-Smith Aggregation

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