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

Do Political Scientists Care About Effect Sizes: Replication and Type M Errors

Reproducibility has come a long way in political science. Many major journals now require replication materials be made available either on their websites or some service such as the Dataverse Network. Most of the top journals in political science have formally committed to reproducible research best practices by signing up to the The (DA-RT) Data Access and Research Transparency Joint Statement.This is certainly progress. But what are political scientists actually supposed to do with this new information? Data and code availability does help avoid effort duplication--researchers don't need to gather data or program statistical procedures that have already been gathered or programmed. It promotes better research habits. It definitely provides ''procedural oversight''. We would be highly suspect of results from authors that were unable or unwilling to produce their code/data.However, there are lots of problems that data/code availability requirements do not address.…

Showing results from Cox Proportional Hazard Models in R with simPH

Update 2 February 2014: A new version of simPH (Version 1.0) will soon be available for download from CRAN. It allows you to plot using points, ribbons, and (new) lines. See the updated package description paper for examples. Note that the ribbons argument will no longer work as in the examples below. Please use type = 'ribbons' (or 'points' or 'lines'). Effectively showing estimates and uncertainty from Cox Proportional Hazard (PH) models, especially for interactive and non-linear effects, can be challenging with currently available software. So, researchers often just simply display a results table. These are pretty useless for Cox PH models. It is difficult to decipher a simple linear variable’s estimated effect and basically impossible to understand time interactions, interactions between variables, and nonlinear effects without the reader further calculating quantities of interest for a variety of fitted values.So, I’ve been putting together the simPH R p…

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 blogposts. 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 1995, 1996, 1997. ExamplesNot…