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stuff for LVM

create LVM partition on new physical volume

Find the new physical volume using fdisk -l. Let's say it is /dev/sda.

Then partition using fdisk /dev/sda. Type n to create primary partition, default partition 1, default sectors. Then type t to change partition type to 8e (Linux LVM). Then type w to write the partition table.

initialize physical volume for LVM

pvcreate /dev/sda1

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clear all 
set more off

* download CES data, if new
!wget -N https://download.bls.gov/pub/time.series/ce/ce.data.0.AllCESSeries
!wget -N https://download.bls.gov/pub/time.series/ce/ce.series
!wget -N https://download.bls.gov/pub/time.series/ce/ce.industry

* identify 3-digit industries
insheet using ce.industry, clear tab

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library(tidyverse)
library(arrow)

# utility to download single quarterly file for a year of data
# and partition it into years X aggregation levels
dl_partition_qcew <- function(year) {
  url <- paste0("https://data.bls.gov/cew/data/files/", 
                year, 
                "/csv/", 
                year, 

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sysuse auto, clear

* separately add required estimate to each model and store models
foreach yvar in mpg price length {
    foreach xvar in weight foreign {
        reg `yvar' `xvar'
        lincomestadd `xvar', statname(`xvar'_)
        eststo `yvar'_`xvar'
    }
}

benzipperer

library(tidyverse)
library(viridis)


cbo_elast_2021 <- -0.48
cbo_elast_2019 <- -0.38
dube_elast_2019 <- -0.04

emp_effects <- tribble(
  ~source, ~emp,

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library(tidyverse)
library(haven)
library(data.table)


clean_qcew_annual <- function(x) {
  # QCEW data from https://www.bls.gov/cew/downloadable-data-files.htm
  fread(cmd = paste0("unzip -p ", x, "_annual_singlefile.zip")) %>% 
    # keep only county-level private sector 
    filter(agglvl_code == 71 & own_code == 5) %>% 

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summarize_groups <- function(.data, .groups, ...) {
  dots <- enquos(...)
  
  map_dfr(.groups, ~ summarize_onegroup(.data, all_of(.x), !!!dots)) %>%
    relocate(name, value)
}

summarize_onegroup <- function(.data, .group, ...) {
  data <- .data %>% 
    rename(value = {{ .group }}) %>% 

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library(tidyverse)

mydata <- mtcars

groups <- c("cyl", "gear", "carb")

group_summarize <- function(x) {
  mydata %>% 
    rename(group_value = {{ x }}) %>% 
    group_by(group_value) %>% 

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benzipperer

library(tidyverse)
library(vroom)

# sector names
sector_names <- read_csv("https://raw.githubusercontent.com/Economic/ui_state_detailed/master/output/state_ui_industry_recoded.csv") %>% 
  group_by(sector, sectorname) %>% 
  tally() %>% 
  select(sector, sector_name = sectorname)

# Read in 2019 March CPS