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postshock: An R Package for Donor-Adjusted Forecasting After Structural Shocks
Abstract
We present the postshock R package, which implements and extends a donor-based framework for forecasting when a structural shock is known and the target response of interest is not yet observed. The package estimates shock effects from historical donor episodes, balances donors using specified matching features, and transfers the resulting adjustment to a target-series forecast. It provides integrated workflows for conditional mean forecasting through ARIMA and ARIMAX models and conditional variance forecasting through GARCH-X models. Additional functionality includes structured donor pools, control-shock regressors, automated GARCH-X order selection, processed data objects, and reproducible empirical workflows.
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BibTeX
@misc{wang2026postshock,
title = {{postshock: An R Package for Donor-Adjusted Forecasting After Structural Shocks}},
author = {Qiyang Wang and Daniel J. Eck},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.11841},
eprint = {2610.11841},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.11841},
}
RIS
TY - UNPB
TI - postshock: An R Package for Donor-Adjusted Forecasting After Structural Shocks
AU - Qiyang Wang
AU - Daniel J. Eck
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.11841
UR - https://arxiv.org/abs/2610.11841
AB - We present the postshock R package, which implements and extends a donor-based framework for forecasting when a structural shock is known and the target response of interest is not yet observed. The package estimates shock effects from historical donor episodes, balances donors using specified matching features, and transfers the resulting adjustment to a target-series forecast. It provides integrated workflows for conditional mean forecasting through ARIMA and ARIMAX models and conditional variance forecasting through GARCH-X models. Additional functionality includes structured donor pools, control-shock regressors, automated GARCH-X order selection, processed data objects, and reproducible empirical workflows.
ER -