sfc-models 0.2

Stock-Flow Consistent (SFC) model generation

Introduction

Creation and solution of stock-flow consistent (SFC) models. Currently under construction.

At present, the module sfc_models.iterative_machie_generator.py imports a text block of equations, and then writes a python module that implements that system of equations.

Although such functionality is nice, the objective is to build modules that generate the systems of equations. That is, the user will specify the high-level sector description of the economy (which may include multiple countries), and the high-level description will be parsed to generate the low-level equations.

For another take on SFC models in Python see: https://github.com/kennt/monetary-economics

Developed under Python 3.4, and is compatible with Python 2.7.

Sub-package: gl_book

The subpackage sfc_models.gl_book contains code to generate models from the text “Monetary Economics” by Wynne Godley and Marc Lavoie. Since the ultimate objective is to generate the equations algorithmically, these models are only used for comparative purposes.

The previously mentioned GitHub package by “kennt” consists of well-documented solutions of those models.

Solution Method

The single-period solution of a SFC model relies on market-clearing (not necessarily relying on price adjustments, unlike mainstream models). Market clearing relies on solving many simultaneous equations.

At present, the machine-generated code uses an iterative approach to solve x = f(x) (where x is a vector). We just passing an initial guess vector trhough f(x) and hope it converges.

This works for the simple models tested so far. The objective is to augment this by a brute-force search technique that relies upon economic intuition to reduce the dimension of the search space. The margins in this file are too small to write a proof of this technique.

Dependencies

• matplotlib: for plots in examples. (May be required later.)

Documentation will be placed in the “docs” directory.

Examples are in the “examples” directory.

(Please note that setup.py - which is invoked by pip install - currently does not install those directories. The user will need to go to GitHub to get them, as well as the unit tests (and doctests) found in the tests/ directory.)

The test coverage on the “master” branch is 100%, and the objective is to hold that standard. (There are some sections that are effectively untestable, and there appears to be issues with some lines that are undoubtedly hit as being marked as unreached; they have been eliminated with:: # pragma: no cover

License/Disclaimer

Copyright 2016 Brian Romanchuk

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.