QuantEcon Notebook Library

Note: QuantEcon also supports the open notebook library – QuantEcon Notes.

Control

• Multiobjective control of cash management systems - Paco Salas 

Econometrics and Financial Economics

• Implementing and vectorizing a maximum likelihood model with SciPy - Matt Ranger 

• Estimating simple regression models in Julia - Tyler Ransom 

• A Quick Start Introduction to Stan for Economists - Jim Savage 

• Bayesian Estimation of a Univariate First Order Autoregression - David Evans, Chase Coleman, and Thomas Sargent 

• Introduction to Relative Valuation using Market Comparables - Quentin Batista 

• Continuous Sequential Importance Resampling for Stochastic Volatility Models - Hans-Peter Hollwirth, Robert T. Lange and Davide Viviano 

• Spurious Correlation - Samuel (Siyang) Li, Thomas Sargent, and Natasha Watkins 

• Samuelson Model - Thomas Sargent and Natasha Watkins 

Optimization

• Nonlinear optimization using JuMP (Julia for Mathematical Programming) - Tyler Ransom 

Game Theory

• Tools for Game Theory - Daisuke Oyama 

• Tools for Game Theory - Daisuke Oyama 

• Recursive Repeated Games - Chase Coleman with Thomas Sargent 

Working with Data

• Introduction to DataFrames in Julia - Tyler Ransom 

• Visualizing Data with Pandas and Matplotlib - David Backus 

• Demography (Data Bootcamp) - David Backus, Chase Coleman, and Spencer Lyon 

Dynamic Programming in Continuous Time

• Aiyagari (1994) in Continuous Time - SeHyoun Ahn and Benjamin Moll 

Dynamic Programming

• How to pay for a war, part 1 - Sebastian Graves and Thomas J. Sargent 

• How to pay for a war, part 2 - Sebastian Graves and Thomas J. Sargent 

• How to pay for a war, part 3 - Sebastian Graves and Thomas J. Sargent 

• A Problem that Stumped Milton Friedman (Python) - Chase Coleman and Thomas J. Sargent 

• A Problem that Stumped Milton Friedman (Julia) - Chase Coleman and Thomas J. Sargent, edited by Alberto Polo 

• DiscreteDP: Getting Started - Daisuke Oyama 

• DiscreteDP: Implementation Details (Python) - Daisuke Oyama 

• DiscreteDP: Implementation Details (Julia) - Daisuke Oyama 

• DiscreteDP: Automobile Replacement - Daisuke Oyama 

• DiscreteDP: Modeling Career Choice - Daisuke Oyama 

• DiscreteDP: Job Search - Daisuke Oyama 

• DiscreteDP: Job Search - Daisuke Oyama 

• DiscreteDP: Discrete Optimal Growth Model - Daisuke Oyama 

• Examples from Miranda and Fackler

  •  Section 7.6.1: Mine Managment  - Daisuke Oyama 
  • Section 7.6.2: Asset Replacement - Daisuke Oyama 
  • Section 7.6.3: Asset Replacement with Maintenance  - Daisuke Oyama 
  • Section 7.6.4: Option Pricing - Daisuke Oyama  
  • Section 7.6.5: Water Management - Daisuke Oyama  
  • Section 7.6.5: Water Management - Shosei Yu 
  • Section 7.6.6: Bioeconomics - Daisuke Oyama 

• Insurance and Incentives - Sebastian Graces and Thomas Sargent  

General

• Scientific Python Quickstart: Using NumPy and SciPy - John Stachurski 

Approximation

• Approximation Methods for the Lucas Asset Pricing Model - Joao Brogueira and Fabian Schuetze 

• Quadrature routines in QuantEcon - Chase Coleman and Spencer Lyon 

Algorithms

• Solving Initial Value Problems in QuantEcon.py - David R. Pugh 

• The Nelder-Mead Algorithm - Chase Coleman 

• Gaussian Process Regression and Active Subspaces - Spencer Lyon 

Dynamic Models

• MarkovChain: Examples - Daisuke Oyama 

• The Permanent Income Model - Chase Coleman and Thomas Sargent 

• Perfect Foresight Experiments with Dolo - Spencer Lyon and Pablo Winant 

• Solving the Krusell-Smith aggregate shocks model using Julia - Shunsuke Hori