causal

Unemployment Claims COVID-19

In this post I am visualizing and analyzing the unprecedented increase in the number of unemployment claims filed in the US after the lockdown due to COVID 19 pandemic. I am retrieving the data from the tidyquant package (Dancho & Vaughan, 2020). library(CausalImpact) library(tidyverse) library(scales) library(tidyquant) ICSA Data Initial unemployment claims from the first date available, 1967: icsa_dat <- "ICSA" %>% tq_get(get = "economic.data", from = "1967-01-07") %>% rename(claims = price) glimpse(icsa_dat) ## Rows: 2,790 ## Columns: 3 ## $symbol <chr> "ICSA", "ICSA", "ICSA", "ICSA", "ICSA", "ICSA", "ICSA", "ICSA"… ##$ date <date> 1967-01-07, 1967-01-14, 1967-01-21, 1967-01-28, 1967-02-04, 1… ## \$ claims <int> 208000, 207000, 217000, 204000, 216000, 229000, 229000, 242000… icsa_dat %>% ggplot(aes(x = date, y = claims)) + geom_line(color = "blue") + scale_y_continuous(labels = comma) + labs(x = "Date", y = "Claims", subtitle = "As of June 29, 2020") + ggtitle("Unemployment Claims: 1967 to 2020") + theme_bw() Comparison to 2008 Recession In the graph below, I only selected 2008 to 2020.

Propensity Score Analysis with Multiply Imputed Data

In this post, I walk through steps of running propensity score analysis when there is missingness in the covariate data. Particularly, I look at multiple imputation and ways to condition on propensity scores estimated with imputed data. The code builds on my earlier post where I go over different ways to handle missing data when conducting propensity score analysis. I go through tidyeval way of dealing with multiply imputed data.