Usage Arguments Value Author(s) See Also
1 2 | maeforecast.dfm(data, w_size, window="recursive", y.index=1,
factor.num=3, h=0, t.select, t.update=F)
|
data |
a data frame or a matrix; the first column should contain the time series variable for which the forecasts are to be made. Other columns should contain the covariates. |
w_size |
numeric, indicating the index where the forecasting should begin. If the first point forecast should be made at the 73th observation, for example, |
window |
character, indicating the forecasting scheme to be applied. Options include |
y.index |
numeric, indicating the column position of the time series for which the forecasts are made (Y). Defualt is |
t.select |
number of covariates to be included. If omitted, every covariate will be included. Otherwise, a regression between the dependant variable, its lag and each covariate will be run and a statistical test will be applied for the significance of the covariate's coefficient. The covariates will then be ranked based on their test statistics, and |
t.update |
logical, indicating wheter the preselection process should be repeated in evert iteration, if |
h |
forecasting horizon. Default is |
factor.num |
numeric, indicating the number of dynamic factors to be extracted from the covariates in the Dynamic Factor Model. Default is |
Forecasts |
data matrix, containing the point forecasts, realized values, forecast errors, signs of the forecasts and realized values, and success in predicting the signs. |
MSE |
numeric, mean squred error of the point forecasts. |
SRatio |
numeric, success ratio of the point forecasts. Success is claimed when the point forecasts and realized values have the same sign. |
Data |
the data as used in the model. |
Model |
some specifics about the model used. |
Zehua Wu
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.