maeforecast.simplified: Economic Forecasting with High-Dimensional Data

In google-trends-v1/gtm: Functions for Economic Nowcasting with Google Trends Data

Description

This function is almost identical to the maeforecast function excpet that it only returns the out-of-sample point forecasts. It is designed primarily for the use of the Bagging and bt.interval functions.

Usage

maeforecast.simplified(data, model="ar", w_size, window="recursive",
            y.index=1, h=0, ...)

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.
model	character, indicating which model should be used to make the forecasts. Default is an AR(1) model. Note that maeforecast(model="ar") is equivalent to maeforecast.ar() and so on. See Details for the full list of supported models.
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.
h	forecasting horizon. Default is 0.
...	other arguments that may be used. Refer to maeforecast for a full list.

Details

Supported models include "ar", "rw" (Random Walk), "lasso", "postlasso" (Post-Lasso), "ridge", "alasso" (Adaptive Lasso), "postalasso" (Post-AdaptiveLasso), "postnet" (Post-Adaptive ElasticNet), "rf" (Random Forests), "dfm" & "dfm2" (Dynamic Factor Models).

Value

A vector of out-of-sample point forecasts.

Author(s)

Zehua Wu

See Also

maeforecast

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