POETKhat: Khat - number of factors in approximate factor model

Description Usage Arguments Details Value Author(s) References Examples

Description

This function is for calculating the optimal number of factors in an approximate factor model.

Usage

1

Arguments

Y

p by n matrix of raw data, where p is the dimensionality, n is the sample size. It is recommended that Y is de-meaned, i.e., each row has zero mean.

Details

This method was proposed by Bai & Ng (2002) and Hallin & Liska (2007). They propose two penalty functions and in turn minimize the corresponding information criteria. Notice that this method may underestimate K. POET is very robust to over-estimating K. But under-estimating K can result to VERY BAD performance. Therefore we strongly recommend choosing a relatively large K (normally less than 8) to avoid missing any important common factor.

Value

K1HL

estimated number of factors based on the first infomation criterion using Hallin & Liska method

K2HL

estimated number of factors based on the second information criterion using Hallin & Liska method

K1BN

estimated number of factors based on the first infomation criterion using Bai & Ng method

K2BN

estimated number of factors based on the second information criterion using Bai & Ng method

Author(s)

Jianqing Fan, Yuan Liao, Martina Mincheva

References

Bai,Ng,2002.Determining the number of factors in approximate factor models. Econometrica 70,191-221.

Hallin,Liska,2007.Determining the number of factors in the general dynamic factor model.JASA 102,603-617.

Alessi,Barigozzi,Capasso,2010. Improved penalization for determining the number of factors in approximate factor models. Statistics and Probability Letters 80, 1806-1813.

Examples

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p=100
n=100
Y<-array(rnorm(p*n),dim=c(p,n))
K<-POETKhat(Y)


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