dataExample

This is a simulated dataset consisting of predictors, responses, and additional
predictors to make forecasting at a future time. In this example, the dimension
of predictors is p=100, and the number of observed time periods is T=100.

datasets

The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) <doi:10.1016/j.jeconom.2017.08.009>, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) <doi:10.1093/biomet/asab037> and Yu, X., Yao, J. and Xue, L. (2022) <doi:10.1080/07350015.2020.1813589>.

Jing Fu

sufficientForecasting

Sufficient Forecasting using Factor Models

Jianqing Fan

Wei Luo

Lingzhou Xue

Jiawei Yao

Xiufan Yu

dataExample function

<ul>
<li><code>dataExample$X</code> simulated predictors; a p by T matrix</li>
<li><code>dataExample$y</code> simulated responses; a T by 1 vector</li>
<li><code>dataExample$newX</code> simulated new predictors for forecasting; a p by 1 vector</li>
</ul>

Format

A simulated dataset — dataExample

A simulated dataset

dataExample: A simulated dataset

Description

Usage

Arguments

Format