maeforecast.alasso: Economic Forecasting with High-Dimensional Data (Adaptive...

Usage Arguments Value Author(s) See Also

Usage

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maeforecast.alasso(data, w_size, window="recursive", y.index=1,
            standardize=TRUE, lambda.ridge, lambda.lasso, h=0,
            t.select, t.update=F)

Arguments

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, w_size should be set to be 72.

window

character, indicating the forecasting scheme to be applied. Options include "recursive", "rolling", and "fixed".

y.index

numeric, indicating the column position of the time series for which the forecasts are made (Y). Defualt is 1.

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.select number of them with the highest test statistics will be included in the machine learning algorithms. Note that the forecasting horizon h is considered.

t.update

logical, indicating wheter the preselection process should be repeated in evert iteration, if t.select is specified. Under the defualt FALSE, the preselection process will be implemented only with for the first window.

h

forecasting horizon. Default is 0.

lambda.ridge

optional user-supplied lambda sequence used for the ridge regression; default is NULL, and glmnet chooses its own sequence.

lambda.lasso

optional user-supplied lambda sequence used for the lasso regression; default is NULL, and glmnet chooses its own sequence.

standardize

logical, indicating whether the data matrix should be scaled before the model is fitting, for the use of variable selection/shrinkage models. Default is TRUE.

Value

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.

Variables

list, containing the predictors selected by the shrinkage model at every iteration.

Author(s)

Zehua Wu

See Also

maeforecast, maeforecast.lasso, maeforecast.postlasso, maeforecast.postalasso, maeforecast.ridge


google-trends-v1/gtm documentation built on June 5, 2019, 5:13 p.m.