You can download all of the code examples for the book here.
The table below contains a brief description of what each example does. For details on how to set up your computer to run the code examples, see the requirements section below.
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|File Name||Language||Keywords||Relevant Chapters||Description|
|Classical ME Example1.gms||GAMS||classical MaxEnt||4||Classical Maximum Entropy First Example|
|Classical Dual ME Example2.gms||GAMS||dual, concentrated, basic examples||4||Concentrated (Dual) Maximum Entropy Example|
|Classical Dual ME Example2 Surprisal||GAMS||surprisal, dual, concentrated||6||Classical concentrated Maximum Entropy example 2 with surprisal analysis.|
|Classical ME Dice Example.gms||GAMS||classical MaxEnt, dice||4||Classical Maximum Entropy Dice Example|
|Classical ME Dice Example.ipynnb||Python||classical MaxEnt, dice||4||Classical Maximum Entropy Dice Example (IPython/Jupyter notebook)|
|Classical ME Dice Example surprisal.gms||GAMS||surprisal, dual, dice, classical||6||Classical Maximum Entropy Dice Example surprisal analysis.|
|Dice mata optimize.do||Stata||dice, classical, dual, cocentrated, additional examples||4||Stata dice example using Stata's Matrix Language|
|Example 2 Dual ME STATA.do||Stata||4|
|Inequality portfolio.gms||GAMS||portfolio, empirical examples||5|
|MV.gms||GAMS||portfolio, empirical examples||5,14||Minimum Variance portfolio example|
|Short sale portfolio.gms||GAMS||portfolio, empirical examples||5,13,14||Portfolio example with short sales|
|Simple case portfolio.gms||GAMS||portfolio, empirical examples||5,14||Simple portfolio example|
|Utility portfolio.gms||GAMS||portfolio, empirical examples||5,14|
|Smoker limdep.lim||Limdep||discrete choice, binomial, logit, empirical examples||12||Binomial Discrete Choice Model example (Limdep).|
|sas smoker.sas||SAS||discrete choice, binomial, logit, empirical examples||12||Binomial Discrete Choice Model example (SAS).|
|Smoker stata.do||Stata||discrete choice, binomial, logit, empirical examples||2||Binomial Discrete Choice Example (Stata).|
|Random multinomial.do||Stata||discrete choice, multinomial, empirical examples||12||Multinomial Discrete Choice example. Generates random data and presents estimates of the MSE for GME multinomial logit as well as for the multinomial logit.|
|gmemultinomial.ado||Stata||discrete choice, multinomial, empirical examples, ADO files||12||ADO file for multinomial discrete choice example. Needs to be installed for the multinomial model using MaxEnt approach to work.|
|gmentropylogit.ado||Stata||ADO files, logit, binomial, discrete choice||12||ADO file for binomial discrete choice (logit) model. Needs to be installed for the multinomial model using MaxEnt approach to work.|
|Linear auto.gms||GAMS||linear regression,empirical examples||13||Linear regression example|
|Linear random.gms||GAMS||linear regression,empirical examples||13||Linear regression example. Similar to linear auto.gms but generates it's own data.|
|GME auto.sas||SAS||linear regression,empirical examples||13||Linear regression in SAS using the example Automobile data|
|GME random.sas||SAS||linear regression, empirical examples||13||Linear regression in SAS. Similar to GME random.sas but generates it's own data.|
|GME auto example.do||Stata||linear regression, empirical examples||13||Linear regression example in Stata.|
|gmentropylinear.ado||Stata||ADO files, linear regression||13||ADO file for linear regression.|
|Matrix1.gms||GAMS||matrix, markov||12||Matrix balancing example|
|Matrix2.gms||GAMS||matrix||9||Matrix balancing example 2|
|Matrix3.gms||GAMS||matrix, markov||9||Matrix balancing example 3|
|dice two mean.do||Stata||noise, dice, two means, stochastic moments||9||Dice example with two means (noisy/stochastic moments) in Stata.|
|two mean dice loop.gms||GAMS||noise, dice, two means, stochastic moments||9||Dice example with two means (programmed using GAMS loop functionality)|
|two mean dice.gms||GAMS||noise, dice, two means, stochastic moments||9||Dice example with two means in GAMS.|
|dual_classical.py||Python||basic examples, classical, concentrated, dual||4||Optimize the dual concentrated maximum entropy objective.|
|matrix balancing.py||Python||matrix balancing, markov process||9||Classical ME CE and Dual formulations for the Matrix Balancing problem: y=Ax where A is a K by K matrix and coefficients of each one of the K columns sum up to 1 (i.e., proper distribution). Example 1: an 11 by 11 SAM of the US Comparing the primal and concentrated (dual) models Try with the given priors and with uniform priors|
|primal classical.py||Python||basic examples, classical||4||Maximize the classical maximum entropy objective function.|
|centropy hessian.gms||GAMS||hessian, curvature, classical MaxEnt, CrossEnt||2||
GAMS code for classical ME and CE Including the ME dual case. Compare ME and CE for uniform and for correct priors as well as for incorrect priors. Compare dual and primal (look at shadow "prices" - moments.m, as well as speed of convergence and resources used.
NOTE: Can generate monotonically increasing/decreasing prob dist and X's
NOTE: Plots up to 20 observations
|entropy function.py||Python||visualization, basic examples||2,3||Explore the properties of the Entropy function graphically. Contributed by Skipper Seabold and Alan Isaac|
|Firm Size Distribution||Matlab,Excel||empirical||5||Distribution of firm sizes based on industry input/output.|
|Entropy function simplex visualization||Matlab||visualization, theoretical||4||Visualization of the entropy function on a 3D simplex|
|Portfolio optimization toolbox||Matlab||empirical||5||A portfolio optimization toolbox. Includes Traditional Portfolio Optimization, Generalized Cross-entropy based Portfolio Optimization, Visualization, Performance Comparison|
|Income Distribution||Python||empirical,grouping property||5,8||Estimate the income distribution of the population based on tax brackets.|
|Simple Matrix Balancing||Excel||simmple matrix balancing,markov||4||Use Excel to explore the grouping property for a 2 dice example. (Please see the included README file to see how to set up Excel to run the example.)
This section describes how to set up your computer to run the code examples. The examples are written in several languages. Please select the language below to get details about how to set up your computer to run the code examples written in that language.
The General Algebraic Modeling System (GAMS) is a proprietary software package that provides a high-level modeling system for mathematical programming and optimization. GAMS code is provided for many of the examples in the text.
Information about GAMS, including installation instructions and a users guide can be found here.
LIMDEP is a complete econometrics and statistics software package. LIMDEP code is provided for some examples in the text.
Python is a general purpose, high-level programming language. We support Python 2.7 - 3.4. For more information about Python language visit the Official Python Site
You will need the python laguage interpreter and several packages in order to run the examples. If you are new to python, the easiest way to get started is by installing one of several Python Distributions.
New to Python? Try a Python distribution already set up for scientific computing.
Already have Python installed? You can install additional packages from these resources.
The IPython notebook is a web-based interactive computational environment that allows you to combine code, text, math markup, and plots provided as part of the IPython project. The Python code examples from the text are provided as IPython notebooks.
Stata code for binary, multinomial and linear regression are available through the Statistical Software Components (SSC) Archive. The relevant programs can be installed by issuing the following commands:
ssc install gmentropylogit ssc install gmemultinomial ssc install gmentropylinear
Detailed guidance about the entropy procedure can be found here
In order to run the examples, you will need to enable up the solver. You can find instructions on how to set up the solver here.