Economic Resilience and Recovery Index

Estimates multidimensional economic resilience following a disruption by comparing observed outcomes with a counterfactual path. Components describe shock depth, cumulative loss, recovery time, recovery strength, post-shock stability, and positive transformation. The package supports grouped analysis, residual-bootstrap uncertainty, alternative weighting schemes, ranking probabilities, sensitivity analysis, shock screening, and diagnostic plots. Methods are designed for regional, sectoral, market, and other regularly observed economic time series.

ERRI

ERRI estimates an Economic Resilience and Recovery Index by comparing an observed economic outcome with an estimated no-shock counterfactual trajectory. It reports shock depth, cumulative loss, recovery time, recovery strength, post-shock stability, positive transformation, and a transparent composite index from 0 to 100.

Installation

install.packages("ERRI")

Basic example

library(ERRI)

dat <- erri_example_data()
fit <- erri(
  dat,
  time = "year",
  outcome = "income",
  unit = "region",
  shock_time = 2020,
  method = "trend"
)

fit
plot(fit, type = "trajectory", unit = "North")
plot(fit, type = "index")

uncertainty <- erri_bootstrap(fit, R = 499, seed = 123)
uncertainty
rank_probability(uncertainty)

The index is descriptive unless the shock and counterfactual are identified by a credible research design. Users should report the six components, weighting scheme, sensitivity results, and uncertainty intervals alongside the composite score.