Estimation in Functional Lagged Regression

The paper introduces a functional time series (lagged) regression model. The impulse-response coefficients in such a model are operators acting on a separable Hilbert space, which is the function space L-2 in applications. A spectral approach to the estimation of these coefficients is proposed and asymptotically justified under a general nonparametric condition on the temporal dependence of the input series. Since the data are infinite-dimensional, the estimation involves a spectral-domain dimension-reduction technique. Consistency of the estimators is established under general data-dependent assumptions on the rate of the dimension-reduction parameter. Their finite-sample performance is evaluated by a simulation study that compares two ad hoc approaches to dimension reduction with an alternative, asymptotically justified method.


Published in:
Journal Of Time Series Analysis, 36, 4, 541-561
Year:
2015
Publisher:
Hoboken, Wiley-Blackwell
ISSN:
0143-9782
Keywords:
Laboratories:




 Record created 2015-09-28, last modified 2018-01-28


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