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.

Primer: Understanding Principal Component Analysis

From the perspective of analysts with an interest in fixed income markets and macroeconomics, principal component analysis (PCA) is mainly of interest for two areas. The first is straightforward: decomposing yield curve movements so that we can get a better handle on the effects of directionality (which is used for risk analysis). The second application is in the development of aggregate economic indicators. This article offers an introductory overview of the mathematical principles of principal component analysis, without attempting to dive into the underlying mathematics. The justification for this approach is straightforward: without an intuition of what we are trying to accomplish, it is very easy to get lost in a mathematical exposition.

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/

Data Tools - Dealing With The FRED HTTPS Migration In Python

The FRED Economic Data System provided by the St. Louis Fed has a programming interface (an API)  which I use to stock the database in my personal research system. The interface is switching over to use the secure HTTPS protocol (from HTTP), and this means that code needs to be updated. I use Python to access the database, and I ran into issues. I am now describing how I worked around the problems.

I assume that the reader knows Python; this is not a tutorial on how to use the fred package in Python. (I expect that I will eventually write tutorials on those lines.)

Primer: Par And Zero Coupon Yield Curves

Par and zero coupon curves are two common ways of specifying a yield curve. Par coupon yields are quite often encountered in economic analysis of bond yields, such as the Fed H.15 yield series. Zero coupon curves are a building block for interest rate pricers, but they are less commonly encountered away from such uses.

Introduction To The Frequency Domain With R

The use of frequency domain concepts is extremely useful when it comes to the analysis of systems. A good portion of communication and control system theory is done almost exclusively within the frequency domain. The advantage of the frequency domain is that it can more easily incorporate uncertainty than time domain analysis. Since the conversion of a signal to a frequency domain representation is a mathematical operator, this is a very mathematical topic. But I believe that many principles can be understood by just working with data and the Fast Fourier Transform (FFT). This article explains some of the fundamentals of the FFT using the open source R programming language. Later articles will then cover more advanced concepts.