alanlujan91

Visiting Assistant Research Professor, Department of Economics, Johns Hopkins University. Core developer @econ-ark.

Johns Hopkins UniversityRockville, MD

Pinned Repositories

BootCamp2019
Repository of syllabi, lecture notes, Jupyter notebooks, code, and problem sets for OSE Lab Boot Camp 2019
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DSE2021
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HARK
Heterogenous Agents Resources & toolKit
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multinterp
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PortfolioChoiceWithRiskyHousing
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SequentialEGM
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DemARK
Demonstrations of how to use material in the Econ-ARK
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DistributionOfWealthMPC
The Distribution of Wealth and the Marginal Propensity to Consume
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EstimatingMicroDSOPs
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HARK
Heterogenous Agents Resources & toolKit
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alanlujan91's Repositories

alanlujan91/SequentialEGM
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alanlujan91/multinterp
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alanlujan91/HARK
Heterogenous Agents Resources & toolKit
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alanlujan91/PortfolioChoiceWithRiskyHousing
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alanlujan91/ConsumptionSavingNotebooks
Jupyter Notebook examples of the ConSav package
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alanlujan91/DistributionOfWealthMPC
The Distribution of Wealth and the Marginal Propensity to Consume
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alanlujan91/Life-Cycle-Prime-Time
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alanlujan91/nber-workshop-2023
Code for the Spring 2023 NBER heterogeneous-agent macro workshop
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alanlujan91/QuantEcon.py
A community based Python library for quantitative economics
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alanlujan91/SHARKFin
Public fork of HARK_ABM_INTRO
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alanlujan91/ai-economist
Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).
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alanlujan91/alanlujan91
Config files for my GitHub profile.
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alanlujan91/alanlujan91.github.io
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alanlujan91/BufferStockTheory
Tested for Apple Silicon, Apple Multipass, Ubuntu 20.04
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alanlujan91/BufferStockTheory-Bloated
Theoretical Foundations of Buffer Stock Saving
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alanlujan91/Chp2-BeliefsandTrust
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alanlujan91/DemARK
Demonstrations of how to use material in the Econ-ARK
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alanlujan91/estimagic
Estimagic is a Python package for nonlinear optimization with or without constraints. It is particularly suited to solve difficult nonlinear estimation problems. On top, it provides functionality to perform statistical inference on estimated parameters.
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alanlujan91/fedsurvey
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alanlujan91/FUES_EGM
EGM using fast upper-envelope scan
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alanlujan91/HAFiscal
Public version of HAFiscal project
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alanlujan91/KrusellSmith
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alanlujan91/KS-HARK-SSJ-Example
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alanlujan91/Nice2023
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alanlujan91/PIR
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alanlujan91/REMARK
Replications and Explorations Made using the ARK
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alanlujan91/RS100_Discussion
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alanlujan91/scipy_proceedings
Tools used to generate the SciPy conference proceedings
alanlujan91/sequence-jacobian
Interactive guide to Auclert, Bardóczy, Rognlie, and Straub (2019): "Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models".
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alanlujan91/SolvingMicroDSOPs
Christopher Carroll's Lecture Notes on Solving Microeconomic Dynamic Stochastic Optimization Problems and Indirect Inference
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