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LAG WINDOW ESTIMATION OF THE DEGREE OF DIFFERENCING IN FRACTIONALLY INTEGRATED TIME SERIES MODELS
Author(s) -
Chen Gemai,
Abraham Bovas,
Peiris Shelton
Publication year - 1994
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1994.tb00205.x
Subject(s) - estimator , autoregressive fractionally integrated moving average , mathematics , autoregressive model , series (stratigraphy) , lag , star model , degree (music) , window (computing) , statistics , econometrics , range (aeronautics) , time series , autoregressive integrated moving average , long memory , computer science , volatility (finance) , paleontology , computer network , physics , materials science , acoustics , composite material , biology , operating system
. In this paper we consider the estimation of the degree of differencing d in the fractionally integrated autoregressive moving‐average time series model ARFIMA ( p, d, q ). Using lag window spectral density estimators we develop a regression type estimator of d which is easy to calculate and does not require prior knowledge of p and q. Some large sample properties of the estimator are studied and the performance of the estimator for small samples is investigated using the simulation method for a range of commonly used lag windows. Some practical recommendations on the choice of lag windows and the choice of the window parameters are provided.