M.C. Medeiros, A. Veiga, and M.G.C. Resende
Journal of Computational and Graphical Statistics, vol. 11, pp. 236-258, 2002
ABSTRACT
Over recent years, several nonlinear time series
models have been proposed in the literature. One model that has
found a large number of successful applications is the threshold
autoregressive model (TAR). The TAR model is a piecewise linear process
whose central idea is to change the parameters of a linear
autoregressive model according to the value of an observable variable,
called the threshold variable. If this variable is a lagged value of the
time series, the model is called a self-exciting threshold
autoregressive (SETAR) model. In this paper, we propose a heuristic to
estimate a more general SETAR model, where the thresholds are
multivariate. We formulated the task of finding multivariate
thresholds as a combinatorial optimization problem. We developed an
algorithm based on a Greedy Randomized Adaptive Search Procedure (GRASP)
to solve the problem. GRASP is an iterative randomized sampling
technique that has been shown to quickly produce good quality solutions
for a wide variety of optimization problems. The proposed model
performs well on both simulated and real data.
PostScript file of full paper
PDF file of full paper
Go back
Mauricio G.C. Resende's Home PageLast modified: 28 June 2002