nowcast

A technical working paper for this package can be found here.

nowcast is an R package for economic nowcasting. It estimates the current state of a macroeconomic variable (like GDP) before the official data are released, using higher-frequency indicators that are available sooner.

Installation

install.packages("nowcast")

# Or install the development version from GitHub
# install.packages("devtools")
# devtools::install_github("charlescoverdale/nowcast")
install.packages("nowcast", repos = c('https://charlescoverdale.r-universe.dev', 'https://cloud.r-project.org'))

library(nowcast)

# Nowcast GDP from monthly indicators
aligned <- nc_align(gdp, retail = retail, ip = ip)
result  <- nc_bridge(target ~ retail + ip, data = aligned)
result$nowcast

Why nowcasting?

GDP is published with a lag, typically 6 to 8 weeks after the quarter ends. But monthly indicators like retail sales, industrial production, and labour market data arrive much sooner. Nowcasting uses these higher-frequency series to estimate GDP growth in real time, before the official number appears.

The core challenge is the ragged edge: different indicators have different publication lags. Retail sales might be available through February, industrial production only through January, and employment through December, all at the same calendar date. A nowcasting model must handle this jagged pattern of data availability.

nowcast aligns mixed-frequency data, estimates bridge equations (the standard model at most central banks), and evaluates nowcast performance through pseudo-real-time backtesting.

How does nowcast compare to other packages?

Feature nowcast bridgr midasr dfms
Bridge equations Yes (with AR terms) Yes No No
Mixed-frequency alignment Yes No No Yes
Ragged-edge diagnostics Yes No No Yes
Pseudo-real-time backtest Yes No Yes (rolling) No
Diebold-Mariano test Yes (HLN corrected) No No No
MIDAS No No Yes No
Dynamic factor models No No No Yes
Dependencies 1 (cli) 8+ (tidyverse) 6+ 3

nowcast provides bridge equations with integrated evaluation in a lightweight package. bridgr does bridge equations within the tidyverse ecosystem. midasr is a mature MIDAS implementation. dfms is a high-quality DFM package with a C++ backend. nowcast focuses on the evaluation side: pseudo-real-time backtesting and the HLN-corrected Diebold-Mariano test let you compare nowcasting specifications on the same data.

Quick start

1. Align mixed-frequency data

library(nowcast)

# Quarterly target (e.g. GDP growth)
gdp <- data.frame(
  date = seq(as.Date("2015-01-01"), by = "quarter", length.out = 40),
  value = rnorm(40, 0.5, 0.3)
)

# Monthly indicators (e.g. retail sales, industrial production)
retail <- data.frame(
  date = seq(as.Date("2015-01-01"), by = "month", length.out = 120),
  value = rnorm(120, 0.4, 0.5)
)
ip <- data.frame(
  date = seq(as.Date("2015-01-01"), by = "month", length.out = 118),  # ragged edge
  value = rnorm(118, 0.3, 0.4)
)

# Align: monthly indicators are averaged to quarterly frequency
aligned <- nc_align(gdp, retail = retail, ip = ip)
aligned
#> ── Aligned Nowcasting Dataset ──
#> • Target: target (quarterly)
#> • Indicators: 2 (retail, ip)
#> • Periods: 40 (2015-01-01 to 2024-10-01)

# See the ragged edge
nc_ragged_edge(aligned)

2. Estimate a bridge equation

# Bridge equation with AR(1) term (default)
result <- nc_bridge(target ~ retail + ip, data = aligned)
result
#> ── Nowcast (Bridge Equation) ──
#> • Nowcast: 0.4832
#> • SE: 0.3156
#> • 95% CI: [-0.1467, 1.1131]
#> • Target period: 2024-10-01
#> • R-squared: 0.1523 | Obs: 38

# Full summary with coefficients
summary(result)

# Static bridge equation (no AR terms)
nc_bridge(target ~ retail + ip, data = aligned, ar_order = 0)

3. Evaluate with pseudo-real-time backtesting

# Expanding-window backtest: train on 1:i-1, nowcast i
bt <- nc_backtest(target ~ retail + ip, data = aligned, start = 20)
bt
#> ── Nowcast Backtest (bridge, expanding) ──
#> • Evaluations: 18
#> • RMSE: 0.2847
#> • MAE: 0.2312
#> • Bias: 0.0043

# Compare two specifications
bt_static <- nc_backtest(target ~ retail + ip, data = aligned,
                          start = 20, ar_order = 0)
nc_dm_test(bt$results$error, bt_static$results$error)
#> Diebold-Mariano (HLN): p = 0.42

# Plot actual vs nowcast
plot(bt)

Functions

Category Function Description
Data nc_align() Align mixed-frequency target and indicators
Data nc_ragged_edge() Summarise data availability across series
Data nc_aggregate() Temporal aggregation (monthly to quarterly/annual)
Data nc_transform() Stationarity transforms (diff, log_diff, standardize)
Estimation nc_bridge() Bridge equation with optional AR terms
Evaluation nc_evaluate() RMSE, MAE, and bias
Evaluation nc_dm_test() Diebold-Mariano test (HLN corrected)
Evaluation nc_backtest() Pseudo-real-time expanding/rolling window evaluation
Utility nc_available() List available methods
Utility nc_compute() Generic dispatcher by method name
S3 print() / summary() cli-formatted output for all objects
S3 plot() Actual vs fitted/nowcast plots
S3 predict() Predict from fitted nowcast model

Methodology

Bridge equations

Bridge equations are the most widely used nowcasting method. They were developed at the ECB (Runstler and Sedillot 2003, Baffigi et al. 2004) and remain the standard model at most central banks and treasuries. The idea is simple: aggregate monthly indicators to the quarterly frequency, then regress the quarterly target on those aggregated indicators.

Following standard practice, nc_bridge() includes an autoregressive term by default (ar_order = 1) to capture GDP momentum. The model is:

$$y_t = \alpha + \phi y_{t-1} + \sum_i \beta_i \bar{x}_{i,t} + \varepsilon_t$$

where $\bar{x}_{i,t}$ is the within-quarter mean of monthly indicator $i$.

Prediction intervals come from predict.lm(..., interval = "prediction"), which accounts for both residual variance and coefficient estimation uncertainty, evaluated against the t distribution.

Diebold-Mariano test

The nc_dm_test() function implements the Harvey, Leybourne, and Newbold (1997) modification of the Diebold-Mariano (1995) test. The HLN correction applies a finite-sample scaling factor and uses the t_{n-1} distribution rather than the standard normal. This matters at the sample sizes typical in nowcasting (20-60 quarters). The Bartlett (triangular) kernel is used for HAC variance estimation, which guarantees non-negative variance.

Backtesting

nc_backtest() performs pseudo-real-time evaluation on final revised data. At each step, the model sees only past data (expanding or rolling window) and produces a nowcast for the next period. This simulates real-time performance but does not account for data revisions. True real-time evaluation requires vintage data (e.g. from FRED's ALFRED database).

Design decisions

Limitations

References

Related packages

Package Description
ons UK Office for National Statistics data (high and low frequency indicators)
boe Bank of England data (Bank Rate, financial conditions)
fred Federal Reserve Economic Data (US monthly and quarterly indicators)
readecb European Central Bank data (HICP, monetary aggregates)
readoecd OECD cross-country macro indicators
predictset Conformal prediction intervals around nowcast point estimates
mpshock Monetary policy shock series
inflationkit Inflation analysis (decomposition, persistence, Phillips curve)
ivcheck IV diagnostics (for treatment-effect-style nowcasts)

Issues

Found a bug or have a feature request? Please open an issue on GitHub.

nowcasting, GDP, macroeconomics, bridge equations, mixed frequency, economic forecasting, real-time data, ragged edge, time series, R package