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.
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.
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.
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.
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.
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.
I shared a chart recently on Twitter that got some attention: static version pic.twitter.com/vtD54nXGio — 📈 𝙻𝚎𝚗 𝙺𝚒𝚎𝚏𝚎𝚛 📊 (@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.
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.
Mortgage interest rates have moved about a percentage point lower from where they were a year ago. The housing market seems to have responded favorably. On my way into D.C. the other day to do some business, I joined a Twitter exchange originally between [at]Graykimbrough and Adam Ozimek, [at]ModeledBehavior about the effects of Federal Reserve interest policy on the housing market. Seems unlikely housing market was slowed by trade war.
This post is for me and future me, though if you get something out of that, that’s great too. Here I will jot down some notes on something I’ve been thinking about. Because reasons, I have been interested in Vector Error Correction Models (VECM). I’ve been thinking of the case where you estimate an error correction model, and have available external forecasts for one of the variables. How can you easily construct the conditional forecasts for the VECM in R?