FDLS: Narrow band estimation of the cointegrating vector.

Description Usage Arguments Details References Examples

View source: R/FDLS.R

Description

Semiparametric estimator for the cointegrating vector as suggested by Robinson (1994) and discussed by Robinson and Marinucci (2003) and Christensen and Nielsen (2006), among others.

Usage

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FDLS(X, Y, m)

Arguments

X

data matrix.

Y

data matrix.

m

bandwith parameter specifying the number of Fourier frequencies. used for the estimation of d, usually floor(1+T^delta), where 0<delta<1.

Details

add details here. Assumes that there is no long-run coherence between the errors and the regressors. Consistency and Normality, Stationarity, assumptions,...

References

Christensen, B. J. and Nielsen, M. O. (2006): Asymptotic normality of narrow-band least squares in the stationary fractional cointegration model and volatility forecasting. Journal of Econometrics, 133, pp. 343-371.

Robinson, P. M., (1994): Semiparametric analysis of long-memory time series. Annals of Statistics, 22, pp. 515-539.

Robinson, P. M. and Marinucci, D. (2003): Semiparametric frequency domain analysis of fractional cointegration. In: Robinson, P. M. (Ed.), Time Series with Long Memory, Oxford University Press, Oxford, pp. 334-373.

Examples

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T<-500
d<-0.4
beta<-1

data<-FI.sim(T, q=2, rho=0, d=c(d,0))
xt<-data[,1]
et<-data[,2]
yt<-beta*xt+et
FDLS(xt,yt,m=floor(1+T^0.4))

data<-FI.sim(T, q=2, rho=0.8, d=c(d,0))
xt<-data[,1]
et<-data[,2]
yt<-beta*xt+et
FDLS(xt,yt,m=floor(1+T^0.4))

FunWithR/LongMemoryTS documentation built on June 9, 2018, 12:22 a.m.