Let’s update our earlier analysis to examine the Federal Reserve’s Beige Book.
Following my earlier post, we can construct a sentiment measure for each report. It turns out that after turning sharply negative in spring, the October 2020 report returned to positive territory.
The sentiment index looks at all words and after adjusting for economics terms (like gross) we score them for sentiment. We could just count up the number of times we see words like “stong” vs words like “weak”.
Economist Play-in Round
Bracket madness is about the descend on us. Before we get to March Madness we’ll have to suffer through a different kind of madness: the Neoliberal Shill Bracket. This year the Neoliberal project has succumbed to inflation and has expanded the field. This year features a play-in round.
In this post we analyze the Economist Play-in:
Economist Play-in (8)
---@mioana @imbernomics @stanveuger @jodiecongirl @cblatts @jonathaneyer @R_Thaler @florianederer pic.
If I cannot send Adam Ozimek (at Modeled Behavior ) a Diet Pepsi, then the next best thing might be a chart about epop. epop is the term economists use to describe the employment-to-population ratio, a useful summary statistic about the labor market. Perhaps the summary statistic. Adam (and others) has been talking about epop as a key labor market statistic for years. It seems the Federal Reserve is catching on to the usage of the term epop (though many economists over there have been looking at the statistic for a long while too).
Let’s pick up where we left off yesterday and do some more exploration with text mining.
Like yesterday we’ll use the tidytext package for R. And we’ll lean heavily on Julie Silge and David Robinson’s Text Mining with R.
Data
We’ll turn again to the Federal Reserve for our text data. But today we’ll explore the Beige Book, which gathers anecdotal information on current economic conditions across the Federal Reserve Districts.
Textmining is an exciting topic. There is tremendous potential to gain insights from textual analysis. See for example Gentzko, Kelly and Taddy’s Text as Data. While text mining may be quite advanced in other fields, in finance and economics the application of these techniques is still in its infancy.
In order to take advantage of text as data, economists and financial analysts need tools to help them. Fortunately, there is a great resource: Text Mining with R by Julia Silge (blog and on Twitter atjuliasilge) and David Robinson (blog and on Twitter atdrob).