System Data Analysis is a self-directed learning project.

A money system: Exploring within the context of a simple model.

Agent Based Models

Monetary policy decisions: Computational (agent-based) models are adaptations of the government money systems described in Monetary Economics, a book written by Wynne Godley and Marc Lavoie (G&L). Agent-based models solve computationally; not as a system of equations. Models must remain consistent with G&L accounting.


Agent-Based Model Liquidity Preference (ABMLP) is a computational adaptation of the third sectoral system described by G&L.

View a model accounting . A simple code gist of comments outlines model functions.

The Portfolio Decision

The interest rate offered will affect the composition of a household agent's asset portfolio. Model households now have a third financial asset in which to invest. Household agents will choose, based on their expectations and liquidity preferences, to allocate wealth between money, bills and long-term government bonds.

Long-Term Bonds

Mirroring G&L pp133 - 135.

When household agents make their long-term bond decisions, three features matter. First households are concerned with the price that the long-term bond fetches in the current period, for this defines the yield of the asset which will arise in the next period (model step). Second, what also matters is the expected price of the bond in the next period, when it will be possible to sell the bond. These two prices help define what we shall call the pure expected rate of return on bonds. The third factor is the confidence with which households hold their expectations about future bond prices. In a model where there may be a multiplicity of household agent opinions, it is a measure of the weight that households investors attribute to the validity of their expectations.

View a long-term bond framework .

State Money System

The Hugging Face space, abmlp-test , shows test output of agent-based model liquidity preference. Visit the Three Fiscal Systems app for a simple analysis of output from the model as it consumes real-world economic time-series. Spaces and apps may sleep. Please allow them time to wake.

Exploratory Data Analysis

The data analysis part of a self-learning project. Visit studio-data-reports for model and real-world time-series analysis.

For earlier agent-based models, view abmsim and abmpc.