SF.PC

Principal component regression for sufficient forecasting

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

SF.PC function

<dl><dt>y</dt>
<dd>Response, T by 1 matrix</dd>
<dt>X</dt>
<dd>Predictors, p by T matrix</dd>
<dt>newX</dt>
<dd>New predictors, a vector contains p entries (or <code>NULL</code>)</dd>
<dt>K</dt>
<dd>The number of common factors (default = obtained
by <code>getK</code>)</dd>
<dt>L</dt>
<dd>The number of principal components used in the prediction,
L is required to be no greater than K (default = <code>K</code>)</dd></dl>

Arguments

Principal component regression for sufficient forecasting — SF.PC

<dl>

<dt>y</dt>
<dd>Response, T by 1 matrix</dd>


<dt>X</dt>
<dd>Predictors, p by T matrix</dd>


<dt>newX</dt>
<dd>New predictors, a vector contains p entries (or <code>NULL</code>)</dd>


<dt>K</dt>
<dd>The number of common factors (default = obtained
by <code>getK</code>)</dd>


<dt>L</dt>
<dd>The number of principal components used in the prediction,
L is required to be no greater than K (default = <code>K</code>)</dd>

</dl>

SF.PC: Principal component regression for sufficient forecasting

Description

Usage

Value

Arguments

Examples