An R package for eigenvalue-based estimation of the number of factors in approximate factor models. Designed to work when either N or T is large, without requiring both dimensions to grow simultaneously.
# Development version from GitHub
devtools::install_github("penny4nonsense/factorselect")library(factorselect)
# Simulate a factor model with 3 true factors
X <- simulate_factor_model(N = 100, TT = 200, k = 3, sd = 0.5, seed = 42)
# Estimate number of factors using Ahn & Horenstein (2013)
result <- select_factors(X, method = "ahn_horenstein", kmax = 8)
print(result)
#> Factor Number Selection
#> =======================
#> Call: select_factors(X = X, method = "ahn_horenstein", kmax = 8)
#>
#> kmax: 8
#>
#> Estimated number of factors:
#> ahn_horenstein 3# Run all estimators at once
result <- select_factors(X,
method = c("ahn_horenstein", "bai_ng", "abc",
"lam_yao", "onatski_2009", "onatski_2010"),
kmax = 8)
print(result)
#> Factor Number Selection
#> =======================
#> Call: select_factors(X = X, method = c("ahn_horenstein", "bai_ng",
#> "abc", "lam_yao", "onatski_2009", "onatski_2010"), kmax = 8)
#>
#> kmax: 8
#>
#> Estimated number of factors:
#> ahn_horenstein 3
#> bai_ng 3
#> abc 3
#> lam_yao 6
#> onatski_2009 3
#> onatski_2010 3# Visualize the eigenvalue spectrum
result <- select_factors(X, method = "ahn_horenstein", kmax = 8)
plot(result)
| Method | Reference |
|---|---|
| Eigenvalue Ratio (ER, GR) | Ahn & Horenstein (2013) |
| Information Criteria (PC, IC) | Bai & Ng (2002) |
| Tuned Information Criteria | Alessi, Barigozzi & Capasso (2010) |
| Auto-covariance Ratio | Lam & Yao (2012) |
| Edge Distribution Test | Onatski (2009) |
| Edge Distribution Estimator | Onatski (2010) |
Ahn, S.C. and Horenstein, A.R. (2013). Eigenvalue Ratio Test for the Number of Factors. Econometrica, 81(3), 1203-1227.
Bai, J. and Ng, S. (2002). Determining the Number of Factors in Approximate Factor Models. Econometrica, 70(1), 191-221.
Alessi, L., Barigozzi, M. and Capasso, M. (2010). Improved Penalization for Determining the Number of Factors in Approximate Factor Models. Statistics and Probability Letters, 80, 1806-1813.
Lam, C. and Yao, Q. (2012). Factor Modelling for High-Dimensional Time Series: Inference for the Number of Factors. The Annals of Statistics, 40(2), 694-726.
Onatski, A. (2009). Testing Hypotheses About the Number of Factors in Large Factor Models. Econometrica, 77(5), 1447-1479.
Onatski, A. (2010). Determining the Number of Factors From Empirical Distribution of Eigenvalues. The Review of Economics and Statistics, 92(4), 1004-1016.