How To Start Data Sciencing With Python!

I put a post about "starting data science with Python" on my Substack. Since I needed to deal with this quickly, I am not going to re-post here, as I would need to clean up formatting and images.

Economics/Finance Analysis Package

I wrote a free post on my Patreon discussing my open source econ_platform package, which is mainly in Python — although it supports other languages. (I produce my charts in R.) The package is available at https://github.com/brianr747/platform.

The article discusses the core of the package - the fetch() function. Using the fetch command allows me to generate this exciting Icelandic HICP inflation rate in just four lines of code (one of which is the boilerplate library initialisation command).

Small Platform/sfc_models Update

I just wanted to give a small update on what I am doing. I am currently working on tying the sfc_models framework into econ_platform. This is not adding new modelling functionality, but will make it easier to work with (eventually). For example, the entire set of series generated by a model - including optional solution convergence output and initial steady state solvers - will be loaded into a database, which can be browsed. (Obviously, this assumes some database browsers are developed for econ_platform.

Otherwise, econ_platform is closer to "version 1.0." There are some missing features, but they are of lesser importance, and should be fairly easy to incorporate.

Avoiding Problems When Drawing Recession Bars With ggplot

I discovered an interesting "feature" with ggplot - the  R language plotting package when it came to drawing recession bars (as in the figure above). You need to be very careful with how you start building the chart graphic object, as I explain below.

(Note: Courtesy of hunting down these formatting issues,  I will only be publishing this programming hint rather than a regular blog article today. I should be be back on the weekend with macro content. Although I would prefer to get back to the economic content, getting the recession bars formatted right is somewhat important for a book on recessions!)

Video On econ_platform Usage

Video of using the econ_platform, including grabbing data from the new Jordà-Schularick-Taylor Macrohistory Database.

(Of course, Excel refused to cooperate...)

Platform Update: RBA Interest Rate Data

I've made a push to put my platform into a state where it could conceivably be used by other people. It is a long way from feature-complete, but it has now reached a state where it is clear how the code works. I've worked on adding the storage of series meta-data, and I used the interest rate data sets from the Reserve Bank of Australia (RBA) as an example.

Python/R Programming Platform Functioning

I have been working heavily on my research platform; it has reached a stage where I can do most of my work with it. Which means that I can go back to economics analysis. It is probably too early for other people to start using it, but a Python programmer could probably get the code up and running. This article explains the current features of the platform, taken from https://github.com/brianr747/platform/blob/master/README.md.

At the end of this article, a R language usage example is given.

Adding DBNomics To A Python Research Platform

I bought a new laptop, and I am now migrating my platform to the new computer. Rather than copy as-is, my plan is to clean up the code base. As a result, I have created a new GitHub repository that will contain the package: https://github.com/brianr747/platform. My initial plan is to do the international charts for my recessions book using the new system, and I will probably get the bulk of the data from DB.nomics (I discussed DB.nomics earlier here). This article explains what this platform aims to do, and what it took to integrate the DB.nomics interface. (Since I spent the day programming, this article is brief.)

Initial Comments On The Minsky Software Package

I have started looking at the Minsky software package, developed by Steve Keen and with coding by Russell Standish. The package is in C++, with the source code available at https://github.com/highperformancecoder/minsky. An older version is available as a precompiled executable at SourceForge; later versions are now available as a benefit for the Minsky Patreon support page: https://www.patreon.com/hpcoder/overview. I will be using the package as part of my recession modelling book; this article offers some initial pointers for those of us who do not read documentation.

New Data Resource: DB.nomics

Some French agencies (including the central bank) have rolled out a useful new data resource called  DB.nomics - https://db.nomics.world/. This site acts like a "European FRED," with a large variety of official data sources rolled into a single data provider. And like FRED, it comes at the wonderful price of free. (The advantage of being backed by central banks is that they have money to burn...) I have been looking at hooking into external data providers as a side project, and DB.nomics looks like an excellent option for most economic analysis purposes.

(Editorial note: I was hit by a cold last week, and have to catch up on various things. I expect that I will have a publishing pause until next week. I was working on my PCA tutorial, but I want to take time on that.)

Loading OECD Data With Python

One of the projects I have been juggling is building a package to import data from the OECD. The OECD helpfully provides an API which allows for custom queries into their large data sets: https://data.oecd.org/api/ (for free!). Unless you really like XML or JSON (which I do not), you want to find a wrapper for the query and download protocols. As a Python developer, the solution that worked best for me was the pandaSDMX Python library:  https://pandasdmx.readthedocs.io/en/latest/

Building A SFC Model In Python

This article demonstrates the first basic application of my Stock-Flow Consistent (SFC) models package in Python: sfc_models. The code is under construction, and needs to be extended in many directions. However, with the core functionality built, adding new analytic capabilities should be straightforward. In this article, I explain how to implement the simplest SFC model -- Model SIM ("SIMplest") -- from Chapter 3 of Godley and Lavoie's Monetary Economics. The key advantage of my library is that the user just specifies the high level description of the sectors of the economy, and the package generates the underlying equations.

SFC Models Library For Python

I have been "in the zone" for coding the last little while, and have an initial version of a Python library to analyse Stock-Flow Consistent models. I still need to work on making the package easier to install, so I will wait to give a fuller description when that is ready. Since I am supposed to be finishing a book, there are no guarantees when that will happen. (Although I expect to use this module within the following book, so the delay will not be too long.)

Primer: Low Yields and Duration

The current low yield environment generates considerable anxiety about the bond market. Some of this reflects the reality that it is more entertaining to read articles predicting doom. However, my suspicion is that this reflect the fact that the consol pricing formula is one of the few things people remember about fixed income pricing.

Launching Open Source Code Libraries

Figure generated by Python library code

I have created a basic fixed income pricing package in the Python programming language -- the "simplepricers" package, available at Github: https://github.com/brianr747/SimplePricers . The code is open source, but the package is now extremely small -- so far, I have spent a lot of time developing my work flow. This package will be used to do fixed income analysis in my books -- and readers will be able to use the source code to replicate the results. For those of you with an interest in economics, I want to package my Stock-Flow Consistent model code in another open source library.

Techniques For Finding SFC Model Solutions

The advantage of standard Stock-Flow Consistent (SFC) models is their analytical tractability. Although some researchers might want to include infinite horizon utility function optimisations, there is little value added if you cannot find the solution to the model. We start off with SFC models that we can find the solution, and then we can incrementally add complexity, so long as a solution method is available. I discuss various techniques to solve these models.

Lessons from Piketty and Reinhart & Rogoff

People were quick to draw parallels between the data problems of Thomas Piketty and those faced by Reinhart and Rogoff. I think there are a few lessons that can be drawn from these episodes, even though the problems with Piketty’s data appear much less serious. Since the details of the wealth distribution is not a priority research topic for me, I will not comment on the details of Piketty's alleged errors. Instead my observations here are more about methodology.