speclagreg: Estimate regresson operators in a lagged linear model
Description
Estimate regresson operators in a lagged linear model using spectral methods.
Assume model
$$Y_t = \sum_{k=-q}^p A_k X_{t-k} + \varepsilon_t$$
where \(X_t\) is a stationary multivariate time series, \((A_k)_{-q \leq k \leq p}\) is a filter and \(\varepsilon_t\) is white noise.
Function speclagreg estimates parameters \(A_k\) with \(k \in \)lags
Usage
speclagreg(X, Y, Kconst = 1, K = NULL, lags = 0:0, freq = NULL,
p = 10, q = 10, weights = "Bartlett")
Arguments
X
first process
Y
second process, if null then autocovariance of X is computed
# NOT RUN {X = rar(100)
Y = rar(100)
#estimate regressors in model $Y_t = \sum_{i\in Z} A_i X_{t-i}$A = speclagreg(X,Y)
# check an advanced examples in demo(lagged.reg)# }