extr_mkt_events_basefc {bayesRecon} | R Documentation |
Base forecasts for the extreme market events dataset
Description
Base forecasts for the extr_mkt_events
dataset, computed using the model by
Agosto, A. (2022).
Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion.
doi:10.2139/ssrn.4119895.
Usage
extr_mkt_events_basefc
Format
A list extr_mkt_events_basefc
containing
extr_mkt_events_basefc$mu
data frame of the base forecast means, for each day
extr_mkt_events_basefc$size
data frame of the static base forecast size parameters
Details
The predictive distribution for the bottom time series is a multivariate negative binomial with a static vector of dispersion parameters and a time-varying vector of location parameters following a score-driven dynamics. The base forecasts for the upper time series are computed using a univariate version of this model. They are in-sample forecasts: for each training instant, they are computed for time t+1 by conditioning on the counts observed up to time t.
Source
Agosto, A. (2022). Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion. doi:10.2139/ssrn.4119895
References
Agosto, A. (2022). Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion. doi:10.2139/ssrn.4119895
Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). Properties of the reconciled distributions for Gaussian and count forecasts. International Journal of Forecasting (in press). doi:10.1016/j.ijforecast.2023.12.004.