TY - JOUR

T1 - Regularised estimators for ARFIMA processes

AU - Vivero, O.

AU - Heath, W.P.

PY - 2016/4/21

Y1 - 2016/4/21

N2 - Stochastic processes with long-range dependence are found in many applications. ARFIMA models can be used to characterise both their short-term correlations and the phenomenon of long-range dependence. Maximum likelihood estimates of the model parameters have nice statistical properties but are ill-conditioned and hard to compute. Whittle's approximation has the same asymptotic properties and yet is easier to compute. We propose a regularisation of Whittle's approximation that overcomes the problem of ill-conditioning. Good results are demonstrated in numerical simulations

AB - Stochastic processes with long-range dependence are found in many applications. ARFIMA models can be used to characterise both their short-term correlations and the phenomenon of long-range dependence. Maximum likelihood estimates of the model parameters have nice statistical properties but are ill-conditioned and hard to compute. Whittle's approximation has the same asymptotic properties and yet is easier to compute. We propose a regularisation of Whittle's approximation that overcomes the problem of ill-conditioning. Good results are demonstrated in numerical simulations

U2 - 10.3182/20120711-3-BE-2027.00335

DO - 10.3182/20120711-3-BE-2027.00335

M3 - Erthygl

VL - 45

SP - 298

EP - 303

JO - IFAC Proceedings Volumes (IFAC-PapersOnline)

JF - IFAC Proceedings Volumes (IFAC-PapersOnline)

IS - 16

ER -