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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14783
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| Title: | MODEL SELECTION FOR BROADBAND SEMIPARAMETRIC ESTIMATION OF LONG MEMORY
IN TIME SERIES |
| Authors: | Hurvich, Clifford M. |
| Issue Date: | Mar-1999 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | SOR-99-1 |
| Abstract: | We study the properties of Mallowsâ CL
criterion for selecting a fractional exponential (FEXP) model for a
Gaussian long-memory time series. The aim is to minimize the mean
squared error of a corresponding regression estimator dFEXP of the
memory parameter, d. Under conditions which do not require that the
data were actually generated by a FEXP model, it is known that the mean
squared error MSE = E[dFEXP âÂÂ
d]ò can converge to zero as fast as (log n)/n,
where n is the sample size, assuming that the number of parameters grows
slowly with n in a deterministic fashion. Here, we suppose that the
number of parameters in the FEXP model is chosen so as to minimize a
local version of CL, restricted to frequencies in a neighborhood of
zero. We show that, under appropriate conditions, the expected value of
the local CL is asymptotically equivalent to MSE. A combination of
theoretical and simulation results give guidance as to the choice of the
degree of locality in CL. |
| URI: | http://hdl.handle.net/2451/14783 |
| Appears in Collections: | IOMS: Statistics Working Papers
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