reviser - Analyzing Revisions in Real-Time Time Series Vintages
Analyzes revisions in real-time time series vintages. The package converts between wide revision triangles and tidy long vintages, extracts selected releases, computes revision series, visualizes vintage paths, and summarizes revision properties such as bias, dispersion, autocorrelation, and news-noise diagnostics. It also identifies efficient releases and estimates state-space models for revision nowcasting. Methods are based on Howrey (1978) <doi:10.2307/1924972>, Jacobs and Van Norden (2011) <doi:10.1016/j.jeconom.2010.04.010>, and Kishor and Koenig (2012) <doi:10.1198/jbes.2010.08169>.
Last updated
forecastingmacroeconomicsnowcastingrevisionstime-series
6.91 score 11 stars 14 scripts 448 downloads
reviser - Analyzing Revisions in Real-Time Time Series Vintages
Analyzes revisions in real-time time series vintages. The package converts between wide revision triangles and tidy long vintages, extracts selected releases, computes revision series, visualizes vintage paths, and summarizes revision properties such as bias, dispersion, autocorrelation, and news-noise diagnostics. It also identifies efficient releases and estimates state-space models for revision nowcasting. Methods are based on Howrey (1978) <doi:10.2307/1924972>, Jacobs and Van Norden (2011) <doi:10.1016/j.jeconom.2010.04.010>, and Kishor and Koenig (2012) <doi:10.1198/jbes.2010.08169>.
Last updated
forecastingmacroeconomicsnowcastingrevisionstime-series
6.88 score 11 stars 14 scripts 481 downloads
bridgr - Bridging Data Frequencies for Timely Economic Forecasts
Implements bridge and MIDAS-style mixed-frequency models for nowcasting and forecasting macroeconomic variables by linking higher-frequency indicator variables to a lower-frequency target series. The package standardizes input data, infers regular frequencies, forecasts missing indicator observations, and aggregates indicators to the target frequency before fitting a regression with autoregressive target dynamics. Frequency alignment can be customized through user-supplied conversion rules. For more on bridge and MIDAS models, see Baffigi, A., Golinelli, R., & Parigi, G. (2004) <doi:10.1016/S0169-2070(03)00067-0>, Ghysels, Sinko, & Valkanov (2007) <doi:10.1080/07474930600972467>, Andreou, Ghysels, & Kourtellos (2010) <doi:10.1016/j.jeconom.2010.01.004>, Schumacher (2016) <doi:10.1016/j.ijforecast.2015.07.004>, and Burri (2026) <doi:10.1111/obes.70073>.
Last updated
bridge-regressionforecastingmidas-regressionmixed-frequency-datanowcastingtime-seriestime-series-models
6.13 score 6 stars 9 scripts 404 downloads