Real-time GDP nowcasting with monthly indicators

US GDP is published quarterly, with an advance release roughly four weeks after quarter-end. Monthly indicators (retail sales, employment, industrial production, financial conditions) are available much sooner, so analysts build bridge equations that map monthly indicators onto quarterly GDP. The challenge is doing this honestly: the data you have today has been revised many times, so an in-sample fit overstates real-time forecast skill.

This vignette uses fred together with the nowcast package to:

Pull the standard monthly indicator panel from FRED.
Estimate a bridge equation for quarterly GDP growth.
Backtest in pseudo-real-time using fred_as_of() so each forecast only sees data that was actually available at the time.
Compare in-sample fit to honest pseudo-real-time error.

It needs both an API key and the nowcast package. Without them, the code is shown but not executed.

Setup

library(fred)
library(nowcast)

Step 1. The monthly indicator panel

Start with a small, defensible set of monthly indicators that are themselves released early in the month for the previous month. Industrial production, retail sales, employment, ISM-style activity, and financial conditions are the standard choices.

indicators <- c(
  "INDPRO",   # Industrial production (15th)
  "RSAFS",    # Retail sales advance (mid-month)
  "PAYEMS",   # Nonfarm payrolls (first Friday)
  "NFCI",     # Chicago Fed financial conditions (weekly)
  "UMCSENT"   # Michigan consumer sentiment (mid + late month)
)

monthly <- fred_series(indicators, from = "2000-01-01", format = "wide")
head(monthly)

GDP itself, transformed to quarter-on-quarter annualised growth (FRED’s pca or transform = "annualised"):

gdp_growth <- fred_series(
  "GDPC1",
  from = "2000-01-01",
  transform = "annualised"
)
plot(gdp_growth, main = "Real GDP, QoQ annualised growth")

Step 2. Bridge equation, latest vintage

The naive approach: regress GDP growth on aggregated monthly indicators using the latest data. This is what most ad-hoc nowcasts use.

# Aggregate monthlies to quarterly using fred_aggregate()
monthly_long <- fred_series(indicators, from = "2000-01-01")
quarterly <- fred_aggregate(monthly_long, fun = "mean", by = "quarter")
head(quarterly)

To estimate a bridge equation with nowcast, the inputs need to be a single data frame with the target series and the indicators side by side. The sketch below is illustrative; see ?nowcast::nc_bridge for the actual formula interface.

# Pseudo-code: build a wide quarterly frame for nc_bridge(formula, data)
panel <- merge(
  fred_series("GDPC1", from = "2000-01-01", transform = "annualised"),
  fred_aggregate(
    fred_series(indicators, from = "2000-01-01", format = "wide"),
    fun = "mean", by = "quarter"
  ),
  by = "date"
)
bridge <- nowcast::nc_bridge(value ~ INDPRO + RSAFS + PAYEMS, data = panel)
summary(bridge)

The fit looks impressive because we are using revised indicator values that were not available in real time.

Step 3. Pseudo-real-time backtest

Replace each indicator series with its first-release vintage and rerun. The discipline: at each forecast date, fetch only what was published as of that date.

forecast_dates <- seq(as.Date("2015-01-01"), as.Date("2024-12-01"),
                      by = "quarter")

backtest <- lapply(forecast_dates, function(d) {
  ind_rt <- fred_real_time_panel(indicators, vintages = d, from = "2000-01-01")
  gdp_rt <- fred_as_of("GDPC1", date = d, from = "2000-01-01",
                       units = "pca")
  list(date = d, ind = ind_rt, gdp = gdp_rt)
})

You can now run the same bridge equation per forecast date and compare the real-time forecast to the eventual final-vintage GDP value. The nowcast package has helpers for this; see nowcast::nc_backtest().

Step 4. Honest error

The honest pseudo-real-time RMSE is typically 30-100% larger than the in-sample RMSE. This gap is the difference between “what your model would have said at the time” and “what your model says now, with hindsight”. For publication-grade nowcasting, the pseudo-real-time number is the one to report.

Pinning the run for reproducibility

Save a manifest of every series your analysis used, with their hash and vintage dates, alongside the paper code. Reviewers can re-run and verify the data is unchanged.

m <- fred_manifest(
  gdp = gdp_growth,
  monthly = monthly,
  quarterly = quarterly
)
print(m)

Cite each series in the paper itself:

fred_cite_series("GDPC1", vintage_date = "2024-12-01", format = "text")
#> [1] "Federal Reserve Bank of St. Louis. (2024). GDPC1 [GDPC1]. FRED, Federal Reserve Bank of St. Louis. https://fred.stlouisfed.org/series/GDPC1. Accessed 01 December 2024."