Len Kiefer

Helping people understand the economy, housing and mortgage markets

30 sigma

Earlier today I tweeted out a chart of the U.S. Labor Department’s estimate of initial jobless claims Link to pdf report. weekly jobless claims, a 30Οƒ event pic.twitter.com/LEO7s5TXsH — πŸ“ˆ π™»πšŽπš— π™Ίπš’πšŽπšπšŽπš› πŸ“Š (@lenkiefer) March 26, 2020 Below I share R code to generate a chart like the one above. We can get data from the St.Β Louis Fed’s Federal Reserve Economic Data (FRED). Then it’s easy to make an animation.

Map of maps

On Friday a colleague showed me an interesting chart, a map of maps. I believe the original was made in Tableau, but I decided to spin one up in R. I tweeted out the picture: A map of maps, showing the correlation between state house price growth rates You see pretty strong spatial correlation, with some interesting exceptions. Florida correlated with AZ, NV pic.twitter.com/9hzwZLkb41 — πŸ“ˆ π™»πšŽπš— π™Ίπš’πšŽπšπšŽπš› πŸ“Š (@lenkiefer) March 6, 2020 In this post I will supply the R code to make one.

Location Quotient Map

I have been thinking about how the recent volatility could impact the economy. If travel and tourism contract due to fears of a pandemic, the impact will differ in markets around the United States. One way to think about this is to compute the Location Quotient, or the percentage of the employment in an area that is in the leisure and hospitality industry. Conside the graphic below: This map shows areas (states and core based statistical areas) color-coded by their location quotient.

Housing Response to a Consumer Sentiment Shock

I’ve been thinking about how different macroeconomic shocks might affect the U.S. housing market. Given recent volatility it is hard to know how to size risks. But it could be a useful exercise to think through how certain typical shocks might impact the housing market. Rather than take on a full structural approach, I just want to extend the reduced form VAR analysis we did in a post from last year.

Forecasting with logs

As an economist and all-around friend of strictly positive numbers I often use the log function. The natural logarithm of course, need I specify it? Apparently in certain spreadsheet software you do. In this note I just wanted to write down a couple of observations about how to generate mean or median forecasts of a variable \(y\) given the model is fit in \(log(y)\). Of course, I am going to borrow heavily from Rob Hyndman’s blog, where he coverse this.

Killing it while shilling it

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.

Phoning it in with R

Recently I have been running R from my Android phone. There are some apps on the Google Play Store that seem to let you emulate R, or connect to a remote version. Instead of doing that, I have been running R directly off my phone using the terminal. Rocking now Writing, running #rstats scripts from the terminal with Emacs, pulling data from Fred, making chartz, All from my oh so very smart phone https://t.

Double Trouble Hazard of Dual Axis Charts

In a blog post the dual y-axis chart just say no Tim Duy asks analysts to give up dual y-axis charts for a new year’s resolution. Like with many resolutions, I predict most will fail at this challenge. I also predict few will take it up. Dual y-axis charts are super popular, especially in finance/economics. As you all know, I care a lot about data visualization. And I have been fighting a losing battle against dual y-axis charts for about a decade.

Shady housing market chart

I shared a chart recently on Twitter that got some attention: static version pic.twitter.com/vtD54nXGio — πŸ“ˆ π™»πšŽπš— π™Ίπš’πšŽπšπšŽπš› πŸ“Š (@lenkiefer) November 14, 2019 But not just any attention (though I do appreciate all your likes and retweets). This was special. Robert Allison [at]RobertAllison__ at SAS replicated the chart with SAS software and wrote a blog about it. These mortgage rates look shady to me. I worked on a lot of SAS stuff early in my days working at Freddie Mac, and Robert’s SAS graph examples were a resource I often used.

Write Anything

This could have been a tweet. Sometimes it is a tweet. After I have taken a break from social media, blogging, etc and I try to get back in the swing I find it incredibly difficult to get restarted. I feel enormous pressure to say something insightful, something funny, something grand. An effective strategy is not to shoot for the stars. Rather than writing something great, I set my sights lower.