Description Usage Arguments Value Examples

View source: R/forecasting_gq.R

Estimates (weighted) forecasted means, variances, and correlations from a fitted bmgarch model.

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`object` |
bmgarch object. |

`ahead` |
Integer (Default: 1). Periods to be forecasted ahead. |

`xC` |
Numeric vector or matrix. Covariates(s) for the constant variance terms in C, or c. Used in a log-linear model on the constant variance terms. If vector, then it acts as a covariate for all constant variance terms. If matrix, must have columns equal to number of time series, and each column acts as a covariate for the respective time series (e.g., column 1 predicts constant variance for time series 1). |

`newdata` |
Future datapoints for LFO-CV computation |

`CrI` |
Numeric vector (Default: |

`seed` |
Integer (Optional). Specify seed for |

`digits` |
Integer (Default: 2, optional). Number of digits to round to when printing. |

`weights` |
Takes weights from model_weight function. Defaults to 1 – this parameter is not typically set by user. |

`L` |
Minimal length of time series before engaging in lfocv |

`method` |
Ensemble methods, 'stacking' (default) or 'pseudobma' |

`inc_samples` |
Logical (Default: FALSE). Whether to return the MCMC samples for the fitted values. |

`...` |
Not used |

forecast.bmgarch object. List containing `forecast`

, `backcast`

, and `meta`

data.
See `fitted.bmgarch`

for information on `backcast`

.
`forecast`

is a list of four components:

- mean
`[N, 7, TS]`

array of mean forecasts, where N is the timeseries length, and TS is the number of time series. E.g.,`fc$forecast$mean[3,,"tsA"]`

is the 3-ahead mean forecast for time series "tsA".- var
`[N, 7, TS]`

array of variance forecasts, where N is the timeseries length, and TS is the number of time series. E.g.,`fc$forecast$var[3,,"tsA"]`

is the 3-ahead variance forecast for time series "tsA".- cor
`[N, 7, TS(TS - 1)/2]`

array of correlation forecasts, where N is the timeseries length, and`TS(TS - 1)/2`

is the number of correlations. E.g.,`fc$forecast$cor[3,, "tsB_tsA"]`

is the 3-ahead forecast for the correlation between "tsB" and "tsA". Lower triangular correlations are saved.- meta
Meta-data specific to the forecast. I.e., TS_length (number ahead) and xC.

- samples
List

. If inc_samples is `TRUE`

, then a list of arrays of MCMC samples for means, vars, and cors. Each array is [Iteration, Period, ..., ...].

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
## Not run:
data(panas)
# Fit DCC(2,2) with constant mean structure.
fit <- bmgarch(panas, parameterization = "DCC", P = 2, Q = 2, meanstructure = "constant")
# Forecast 8 ahead
fit.fc <- forecast(fit, ahead = 8)
# Print forecasts
fit.fc
print(fit.fc)
# Plot variance forecasts
plot(fit.fc, type = "var")
# Plot correlation forecasts
plot(fit.fc, type = "cor")
# Save backcasted and forecasted values as data frame.
fit.fc.df <- as.data.frame(fit.fc)
# Save only forecasted values as data frame.
fit.fc.df <- as.data.frame(fit.fc, backcast = FALSE)
# Add another model, compute model weights and perform a model weighted forecast
# Fit a DCC(1,1) model
fit1 <- bmgarch(panas, parameterization = "DCC", P = 1, Q = 1, meanstructure = "constant")
# Compute model stacking weights based on the last 19 time points (with L = 80)
blist <- bmgarch_list( fit1, fit )
mw <- model_weights(blist, L = 80)
# Weighted forecasts:
w.fc <- forecast(object = blist, ahead = 8, weights = mw)
## End(Not run)
``` |

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