Skip to main content

Partisan Inflation Forecast Errors & Hurricanes

Soon to be Hurricane Isaac has thrown off mine and my coauthor Cassie Grafström's plans to present a paper at the APSA Annual Conference. The paper is on whether or not US Federal Reserve staff bias their inflation forecasts based on the president's party. In lieu of the actual presentation I thought that I would at least post our slides (below).

In a couple earlier posts I showed some graphs indicating that there might be a presidential partisan bias to Fed inflation forecasts. The bias would look like this:

  • Democratic Presidents: Inflation is over-estimated.
  • Republican Presidents: Inflation is under-estimated.

In our paper we try to find out if this is just a coincidence or if inflation forecasts are really biased by the president's party identification. After running many different models we find that, yes Fed staff predict inflation will be higher than it actually is during Democratic presidencies and lower than it is during Republican presidencies.

This figure from the paper gives you a sense of how big we predict the inflation forecast errors will be under Republican and Democratic presidents.

Simulated Inflation Forecast Errors
Simulated Expected Inflation Forecast Error (Gandrud & Grafström)
0 = no forecasting error

Inflation is expected to be about 10% higher than it actually is during Democratic presidencies. It is expected to be about 20% lower than it actually is during Republican presidencies. I'll write another post describing how we made this graph and a few others in the paper.

Now for the slides from our ill-fated APSA presentation (you can find the full paper here):

Comments

Popular posts from this blog

Dropbox & R Data

I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox . There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not. Dropbox Public Folder Dropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name...

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame. I've found the various R methods for doing this hard to remember and usually need to look at old blog posts . Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command– slide –to the DataCombine R package (v0.1.5). Building on the shift function TszKin Julian posted on his blog , slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data. Note: your data needs to be in ascending time order with equally spaced time increments. For example 199...

A Link Between topicmodels LDA and LDAvis

Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background). LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package. I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object. To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis . The full function is below. To...