BTtest {BTtest}

Barigozzi & Trapani (2022) Test

Description

Runs the testing routine proposed in Barigozzi & Trapani (2022) to estimate the number and types of common trends in a nonstationary panel. The method can identify the existence of a common factor subject to a linear trend, as well as the number of zero-mean I(1) and zero-mean I(0) factors.

Usage

BTtest(X, r_max = 10, alpha = 0.05, BT1 = TRUE)

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. Note that chaning r_max does not alter the test result for any individual r.

alpha

the significance level. Default is 0.05.

BT1

logical. If TRUE, a less conservative eigenvalue rescaling scheme is used. In small samples, BT1 = FALSE will result in fewer estimated factors. Default is TRUE.

Details

For details on the testing procedure I refer to Barigozzi & Trapani (2022, sec. 4).

Value

A vector with the estimated number of (i) factors with a linear trend (r_1), (ii) zero-mean I(1) factors (r_2) and (ii) zero-mean I(0) factors (r_3).

Author(s)

Paul Haimerl

References

Barigozzi, M., & Trapani, L. (2022). Testing for common trends in nonstationary large datasets. Journal of Business & Economic Statistics, 40(3), 1107-1122. doi:10.1080/07350015.2021.1901719

Examples

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

# Obtain the estimated number of factors (i) with a linear trend (r_1), (ii) zero-mean I(1) (r_2)
# and (iii) zero-mean I(0) (r_3)
BTtest(X = X, r_max = 10, alpha = 0.05, BT1 = TRUE)