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?
Today I tweeted something that turned out to be pretty popular: US consumer prices pic.twitter.com/LxIxvEnGFe — 📈 𝙻𝚎𝚗 𝙺𝚒𝚎𝚏𝚎𝚛 📊 (@lenkiefer) October 10, 2019 This was an update to a visualization we have talked about here. In this post I want to provide some updated R code to make this visualization taking advantage of the new gganimate api. I’ll also share some code for mortgage rate plots. The code for the inflation plot is pretty simple.
I’m hearing that folks have been invited to speak at the upcoming Rstudio conference. Congratulations to the folks who got accepted this year. I am not sure if I’m going to go to the conference this year, but I recommend you consider it if you love R. I spoke there last year, giving an E-Poster. It was a lot of fun. The best part was getting a chance to meet other R enthusiasts.
I’ve been thinking about smoothing time series data. I tweeted out a bite size bit of code. To fit it into a tweet, I had to squeeze things down a bit. Slightly more verbose, and using fredr to get data from FRED using their API. You’ll need an API key from FRED. These data happen to be for New Private Housing Units Authorized by Building Permits - in Structures with 1 Unit.
Early last Friday morning I was sitting in Palm Springs International Airport waiting to catch a flight back to Virginia. I had traveled out west to speak at the 2019 NAGLREP Conference. This Friday happened to be jobs Friday, when the U.S. Bureau of Labor Statistics releases the employment situation. Jobs Fridays are busy on Twitter. Everybody seems eager to offer a perspective on what the latest jobs numbers mean for the U.
Over the summer I spent a few weeks in Asia. One of the places I visited was Fushimi Inari-taisha in Kyoto, Japan. This shrine is situated on a beautiful mountain. What really struck me about this place was the vibrant colors used in the Torii gates and to decorate some of the fox statues like these: The color got stuck in my head, so I decided to look up the hex code here and created a modified ggplot2 theme using the color.
Found in Translation On a hill in Kyoto, Japan there is a most delightful sign. Near the Kiyomizu Temple temple in the Higashiyama District there are several picturesque streets. The presevered historic district is a favorite place for tourist shopping, tasty snacks, photo opportunities with majestic temples in the background. Many folks like to rent Kimonos (mostly women, but also a few men) and snap photos in the street. On my way through the district I happened across this wonderful sign attached to a private residence:
Earlier today I tweeted out some yield curve charts. I won’t go into great detail into the why, but here I will share some R code to make the charts. My Thread: hope you all are ready for some crazy yield curve charts, cuz you're about to get some crazy yield curve charts — 📈 𝙻𝚎𝚗 𝙺𝚒𝚎𝚏𝚎𝚛 📊 (@lenkiefer) August 7, 2019 We can get Treasury yield curve data from the U.
I have been exploring some visualizations for housing seasonality. In recent days I’ve tried out various ways of using tile plots to display seasonal patterns in home sales and other related data. In this post I want to share some of the R code I used to wrangle data and generate those plots. You can see some for example, in this thread (and others): though outside there's blistering heat