BaiIPC: Bai (2004) IPC

In BTtest: Estimate the Number of Factors in Large Nonstationary Datasets

Bai (2004) IPC

Description

Calculates the Integrated Panel Criteria (IPC) to estimate the total number of common trends in a nonstationary panel as proposed by Bai (2004).

Usage

BaiIPC(X, r_max = 10)

Arguments

X	a T \times N numerical matrix or data.frame of observations.
r_max	the maximum number of factors to consider. Default is 10.

Details

For further details on the three criteria and their respective differences, I refer to Bai (2004, sec. 3).

Value

A vector of the estimated number of factors for each of the three criteria.

Author(s)

Paul Haimerl

References

Bai, J. (2004). Estimating cross-section common stochastic trends in nonstationary panel data. Journal of Econometrics, 122(1), 137-183. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jeconom.2003.10.022")}

Examples

# Simulate a nonstationary panel
X <- sim_DGP(N = 100, n_Periods = 200)

# Obtain the estimated number of common factors pre criterion
BaiIPC(X = X, r_max = 10)

BTtest documentation built on Sept. 11, 2024, 6:53 p.m.