Comparison of Differencing Parameter Estimation Using Spectral Regression for Short Memory Data (Simulation Study)

Authors

  • Ferdian Agustiana, Gumgum Darmawan , Septiadi Padmadisastra , Asnan Furinto

Abstract

One of the most common methods used for the estimation of differentiating parameters in
the Long Memory model is spectral regression. The spectral density function of the Long Memory Model is
formed into a simple linear regression equation to estimate the parameter d by the least-squares method.
This approach has drawn many researchers ' attention because it can solve the problems of reducing the
autocovariance function of the Long Memory system. Parameter estimation d by the regression method can
be done directly without knowing the parameters p and q beforehand. This method was first proposed by
Geweke and Porter-Hudak (1983) and modified by Reisen (1994) using smooth Periodogramby the Parzen
Window. Then, Robinson (1995) added Trimming l to the Periodogram. Hurvich and Ray (1995) and
Velasco (1999a) added the Cosine-Bell taper to the Periodogram, Velasco (1999b) replaced the independent
variable to j, which is the index of the Periodogram. In this study, the accuracy of these methods will be
compared to the short memory data (ARIMA) by using a simulation study. In this study, the simulation
study results show the Geweke and Porter-Hudak (GPH) and Reisen (SPR) methods provide relatively
accurate results in estimating the parameters d of the long memory model, even for short memory data. The
results of the comparison show that the GPH and SPR methods are better than the other methods based on
the estimated values of the true value of d (generated) and based on the resulting deviation of the
differentiating coefficient (d).

Published

2020-06-30

Issue

Section

Articles