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On the Optimal Segment Length for Parameter Estimates for Locally Stationary Time Series
Author(s) -
Dahlhaus Rainer,
Giraitis Liudas
Publication year - 1998
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 - 10.1111/1467-9892.00114
Subject(s) - mathematics , autoregressive model , series (stratigraphy) , stationary process , estimation theory , statistics , autoregressive integrated moving average , time series , representation (politics) , star model , autoregressive–moving average model , mean squared error , moving average model , political science , law , biology , paleontology , politics
We discuss the behaviour of parameter estimates when stationary time series models are fitted locally to non‐stationary processes which have an evolutionary spectral representation. A particular example is the estimation for an autoregressive process with time‐varying coefficients by local Yule–Walker estimates. The bias and the mean squared error for the parameter estimates are calculated and the optimal length of the data segment is determined.