FDLS: Narrow band estimation of the cointegrating vector.

In FunWithR/LongMemoryTS: Long Memory Time Series

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

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

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 May 12, 2019, 10:29 p.m.