Monetary economics: R code

I have received from Hamid Raza, working with Stephen Kinsella in Limerick, a package containing models from Godley & Lavoie Monetary economics, chapters 3 to 9.
They have been published in the model section of the website.
(I have not checked the code yet…)

I deeply thank Hamid, since R is a free software, and the availability of R code will be of great help to anyone who is not willing to purchase a software licence.

SFC models in Python

Here is a letter from Kenn Tamara, who developed the models in Godley-Lavoie using Python:

I was reading “Monetary Economics” by Godley and Lavoie and came across the sfc-models.net website. I have taken your eViews models and reimplemented them using Python (running the experiments and generating the figures).

Everything is open-source and is written with a package that I developed to help specify and solve the models. The models are implemented as iPython notebooks for easier viewing and can be found at:
https://github.com/kennt/monetary-economics

Information on the pysolve package used to specify and solve the models can be found at:
https://github.com/kennt/pylinsolve

(A little warning, the code for pysolve is still under development and there isn’t that much documentation yet)

I hope that the python implementation is useful and would like to contribute it to sfc-models.net.

Thank you,
Kenn Takara

Interactive SFC models

I recently discovered that Kevin W. Capehart has written a piece of code in Mathematica from one of my Eviews files for the Godley – Lavoie Monetary economics book, and turned it into a CDF, to illustrate the paradox of thrift

To run the simulation you need to install the free Wolfram reader, and activate it.

This little tool is potentially very useful in exploring stock-flow models, which are tipically non linear, and therefore difficult to solve analitically. Creating a nice interface which allows the user to check model responses to different values of parameters and exogenous variables could help find the range of parameter values for which the model is producing stable (or unstable etc) solutions.