lagreg.est: Estimate the optimal dimension in linear regression problem

In freqdom: Frequency Domain Analysis for Multivariate Time Series

Description

Estimate regression operator P_k (matrices d \times d) in model

Y_t = ∑_{l \in L} P_k X_{t-l} + \varepsilon_t,

where X_t is a d-dimensional stationary process and \varepsilon_t forms a white noise.

Usage

lagreg.est(X, Y, lags = -5:5, K = NULL, Kconst = 1)

Arguments

X	regressors process
Y	response process
lags	lags which should be estimated
K	how many directions should be inverted (as in pseudoinverse)
Kconst	constant for heuristic (as in reg.dim.est)

Value

Estimated regression operator

References

Siegfried Hormann and Lukasz Kidzinski A note on estimation in Hilbertian linear models Research report, 2012

See Also

reg.dim.est, speclagreg

Examples

X = rar(100)
e = rar(100)
Y = X + 0.3 * e
Psi = lagreg.est(X,Y,lags=0:2)

freqdom documentation built on May 2, 2019, 5:55 p.m.