inst/prepare_econ_us.R

In srlanalytics/BDFM: Bayesian and Maximum Likelihood Estimation of Dynamic Factor Models

library(tsbox)
library(fredr)
library(bdfm)

# Use Sys.setenv(FRED_API_KEY = 'XXXX') to set FRED API Key.
# Optain here:https://research.stlouisfed.org/docs/api/api_key.html

series_q <- c(
  'A191RL1Q225SBEA',       # 01 Real GDP, seasonally adjusted, quarterly, annualized % change
  'W068RCQ027SBEA'         # 02 Governemnt expenditures
)

series_m <- c(
  'USSLIND',               # 03 Federal Reserve leading index, monthly, percent
  'PCEDG',                 # 04 persional consumption: durable goods, monthly, level
  'PCEND',                 # 05 persional consumption: non-durable goods, monthly, level
  'UMCSENT',               # 06 Consumer Sentiment, monthly, delayed 1 month for free data
  'UNRATE',                # 07 Unemployment, monthly
  'JTSJOL',                # 08 Job openenings, total non-farm
  'INDPRO',                # 09 Industrial Production Index, monthly, level
  'CSUSHPINSA',            # 10 Case-Shiller home price index, monthly, two month lag, level
  'HSN1F',                 # 11 New 1 family houses sold, level
  'TSIFRGHT',              # 12 Freight transportation index, monthly, 2-3 month lag, level
  'FRGSHPUSM649NCIS',      # 13 CASS freight index, level, not SA
  'CAPUTLG2211S',          # 14 Electricity usage, % capacity, monthly
  'IPG2211S',              # 15 Electricity, industrial production index, monthly, level
  'DGORDER',               # 16 New Orders, durable manufacturing goods, monthly, level
  'AMTMNO',                # 17 New Orderes, all manufacuring industries, level
  'MNFCTRIRSA',            # 18 Manufacturers inventories:sales ratio
  'RETAILIRSA',            # 19 Retail inventories:sales ratio
  'WHLSLRIRSA',            # 20 Wholesalers, inventories:sales ratio
  'CPILFESL'               # 21 CPI
)

series_wd <- c(
  'ICSA',                  # 22 Initial claims, SA, weekly
  'TWEXB',                 # 23 exchange rate index, weekly
  'T10Y3M'                 # 24 10Y to 3M treasury spread, daily
)

multi_fredr <- function(x, by = 0, frequency = NULL) {
  z <- lapply(x, fredr, observation_start = as.Date("1980-01-01"), frequency = frequency)
  lapply(z, ts_lag, by = by)
}
econ_us <- ts_ts(do.call(rbind, c(
  multi_fredr(series_q, by = "2 month"),
  multi_fredr(series_m),
  multi_fredr(series_wd, frequency = "m")
  )
))

# A lot of our data is already seasonally adjusted. We actually only need to SA
# three series (though whether it's necessary to adjust consumer sentiment is
# debatable).
series_sa <- c('UMCSENT', 'CSUSHPINSA', 'FRGSHPUSM649NCIS')
econ_us_sa <- do.call(cbind, lapply(ts_tslist(econ_us[, series_sa]), function(e) seas_we(e, lags = 3)$values))
window(econ_us[, series_sa], end = end(econ_us_sa)) <- econ_us_sa

save(econ_us, file = "data/econ_us.RData")