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Activity Number: 279
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
Sponsor: Korean International Statistical Society
Abstract #310994 View Presentation
Title: Lag Selection for Single-Index Time Series Models
Author(s): Guannan Wang*+ and Li Wang
Companies: University of Georgia and UGA
Keywords: B-spline ; diverging parameters ; semiparametric regression
Abstract:

Nonlinear autoregressive models are very popular in time series analysis because of its great exibility. In such modeling process, one often needs to include many lags to capture the persistence of a time series. Thus, the lag length can be very long, or even close to the length of time series. Such "curse of dimensionality" problem is challenging for nonparametric modelling and requires selection of significant explanatory lagged variables. Single-index model is an appealingly fundamental tool for handling "curse of dimensionality". In this paper we consider nonlinear single-index time series models and propose a method for lag selection. We apply polynomial spline basis function expansion and SCAD penalty to perform estimation and lag selection in the framework of high-dimensional time series. Under stationary and strong mixing conditions, the resulting estimators enjoy the "oracle" property even when the number of parameters tends to infinity. An efficient iterative algorithm has been developed to identify the lags and estimate the coefficients simultaneously. Both numerical studies and real data application confirm a good performance of the proposed method.


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