BGM: Separate R From X

In FAVAR: Bayesian Analysis of a FAVAR Model

Separate R From X

Description

X may include some information related with R. The function extract factors from X which is not related with R by iteration based on Boivin et al. (2009).

Usage

BGM(X, R, K = 2, tolerance = 0.001, nmax = 100)

Arguments

X	a large matrix from which principle components are extracted.
R	a numeric vector which we are interesting in, for example interest rates.
K	the number of extracted principle components.
tolerance	the difference between factors when iterating.
nmax	the max iterations, see details.

Details

The algorithm is as follows:

1. Extract the first K principal components noted F_t^{(0)} from X.

2. Regress X on F_t^{(0)} and R_t, and get regression coefficients β_R^{(0)} of R_t.

3. compute X_0^{(0)} = X_t- R_t β_R.

4. Extract the first K principal components noted F_t^{(1)} from X_t^{(0)}.

5. repeat step 2 - step 4 until precision you want.

Value

the first K principle components, i.e. F_t^{(n)}, not containing the information R.

References

Boivin, J., M.P. Giannoni and I. Mihov, Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data. American Economic Review, 2009. 99(1): p. 350-384.

Examples

data('regdata')
BGM(X = regdata[,1:115],R = regdata[,ncol(regdata)], K = 2)

FAVAR documentation built on May 28, 2022, 1:20 a.m.