In previous work with Skip Krueger, we conceptualized bond ratings as a multiple rater problem and extracted measure of state level creditworthiness. I had always had it on my list to do something like this and recently ran across a package called geofacet that makes it simply to easy to do. So here goes. The code is below the post.
library(haven) library(dplyr) Pew.Data <- read_dta(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/Pew/modeledforprediction.dta")) library(tidyverse) load(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/Pew/Scaled-BR-Pew.RData")) state.ratings <- data.
Some Data from FREDr Downloading the FRED data on national debt as a percentage of GDP. I first want to examine the US data and will then turn to some comparisons. fredr makes it markable asy to do! I will use two core tools from fredr. First, fredr_series_search allows one to enter search text and retrieve the responsive series given that search text. They can be sorted in particular ways, two such options are shown below.
FRED via fredr The Federal Reserve Economic Database [FRED] is a wonderful public resource for data and the r api that connects to it is very easy to use for the things that I have previously needed. For example, one of my students was interested in commercial credit default data. I used the FRED search instructions from the following vignette to find that data. My first step was the vignette for using fredr.
Pew on Rainy Day Funds and Credit Quality The Pew Charitable Trusts released a report last May (2017) that portrays rainy day funds that are well designed and deployed as a form of insurance against ratings downgrades. One the one hand, this is perfectly sensible because the alternatives do not sound like very good ideas. A poorly designed rainy day fund, for example, is going to have to fall short on either the rainy day or the fund.
Longitudinal and Panel Data Analysis in R Goal: A CRAN task view for panel/longitudinal data analysis in R.
What is Panel Data? Panel data are variously called longitudinal, panel, cross-sectional time series, and pooled time series data. The most precise definition is two-dimensional data; invariably one of the dimensions is time. We can think about a general depiction of what a model with linear coefficients typical for such data structures, though ridiculously overparameterized, like so: