On intrinsic irreducible uncertainty of economic outcomes (forecasting is hard) Here’s a picture of me and some bears. Don’t worry, I didn’t get hurt. Those bears couldn’t hurt anybody. First, because they are behind some glass. And second, because they are stuffed. There are a lot of stuffed bears out there. They’ve grown aggressive lately. Maybe because they haven’t eaten much. This current economic expansion is over 10 years old now.
Updated May 28, 2019 I’m giving a seminar about my new working paper “What Happens in Vegas Doesn’t Always Stay in Vegas”“. The slides for the talk are posted below. I made the slides with R and the xaringan package. You can easily print the html to pdf with Chrome. The pdf version is below and available at this link. Long seminar slides .html or [.pdf](../../../../img/charts_may_22_2019/what happens in vegas preso long.
Last week I gave a speech in Cincinnati, Ohio at the UC/PNC Economic Outlook program. My speech was titled “Forecasting in a Vulnerable Economy”. You can find slides and detailed notes over on LinkedIn: https://www.linkedin.com/pulse/forecasting-vulnerable-economy-leonard-kiefer/. In this post I want to share R code for the first three plots on the Vulnerable Economy. We’ll get the data via the St Louis Fed’s FRED. We’re going to grab the Fed Funds rate FEDFUNDS, the Unemployment Rate UNRATE the Congressional Budget Office’s estimate of the long-run natural rate of unemployment NROU and the spread between the 10-year and 2-year U.
I really like R, but I love the R community. Since I’ve started using R intensively in the past couple of years, I’ve constantly been awed and inspired by all the amazing things that people are doing with R. The spirit of the open source community and people’s willingness to share their thoughts and code is fantastic. Many times in this space we’ve remixed different data visualizations with R, often relying on awesome new packages that others have developed.
EARLIER THIS WEEK I TWEETED out a poll asking whether or not folks wanted to see a thread/tweetstorm with slides from an upcoming presentation on the economy and housing markets that I’m giving. Over 90 percent voted for a thread. So I shared it. In this post let me add a little more commentary on the individual slides. Here’s the thread I ended up posting: Thread (0/5). I'm giving an update on economy, #housing and #mortgage market trends.
I PUT TOGETHER SOME SLIDES SUMMARIZING our recent work on dynamic model averaging. See here and here for more blah blah blah. See below for some slides. Click here for a fullscreen version here. Making the Preso Let me also share with you the R code I used to generate these slides. The code below is the Rmarkdown I used to generate the slides (saved as .txt). The slides were put together using the xaringan package.
WE ARE ON OUR WAY TOWARDS BUILDING a tidy PowerPoint workflow. In this post I want to build on my earlier posts (see here for an introduction and here for a more sophisticated approach) for building a PowerPoint presentation with R and try to make it even purrrtier. I saw that somebody shared my posts on reddit and I thought I would take a look at the comments. Folks on the internet are known for kindness and offering helpful advice right?
I’ve BEEN MESSING AROUND MORE WITH R and OFFICER and having too much fun for a Monday. I’m going to dive into some details later, but I’ll just leave a couple files here. See the attached PowerPoint .pptx file for all the charts. Here’s a gif version I started with: Then after I created the PowerPoint I started messing around with the drawing tools and made increasingly ill-advised edits.
LOOK I DON’T HAVE ANYTHING BAD TO SAY about PowerPoint. Others have said it (see for example Tufte and Harvard Business Review). It’s a tool and a fact of life for many of us. I am interested in making better PowerPoints. In this post we’ll use some R tools to generate a PowerPoint deck. OfficeR The package officer allows you to access and manipulate ‘Microsoft Word’ and ‘Microsoft PowerPoint’ documents from R.
The 100 second crisis About 100 seconds into every talk I give there comes a crisis. This note is about that crisis and how I try to overcome it. If you’re in a situation where you give a presentation or speech-I call them “talks”-you might experience your own 100 second crisis. Perhaps my strategy could work for you. The first 99 seconds Let’s begin with those first 99 seconds when things are going well.
THERE IS A LOVELY BOOK on writing style called “Clear and Simple as the Truth” by Francis-Noël Thomas and Mark Turner (webpage). In it Thomas and Turner distinguish between several writing styles including practical style and classic style. Practical style’s mode is description, while classic style’s is presentation (see description). It strikes me that a similar distinction could be made when exploring statistics. Often, when we are exploring data we are in description mode.
I GIVE A LOT OF TALKS. Some are formal presentations or keynotes to large groups, while many are in small group settings. Sometimes I get impromptu requests so I have to be ready pretty much at all times to give some sort of talk. On this blog I’ve shared many different DATA VISUALIZATIONS which could be part of a talk. Many of the more complex visualizations-probably most of the animated gifs-wouldn’t work great in most presentation settings.
IF YOU HAVE BEEN FOLLOWING along-welcome if you’re stopping by for the first time-you’ll have seen many different data visualizations that I have shared with you. But let’s take a step back and consider what to do with them. I’ve got several thoughts on this topic that I will collect in a series I’ll call “See Data, Speak Data”. See Data, Speak Data “See Data, Speak Data” was the title I gave to a keynote I recently gave to some analysts about how I use data in my professional life.