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IDENTIFICATION OF UNOBSERVED COMPONENTS MODELS
Author(s) -
Hotta Luiz Koodi
Publication year - 1989
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.1467-9892.1989.tb00027.x
Subject(s) - autocovariance , identification (biology) , mathematics , autoregressive integrated moving average , series (stratigraphy) , autoregressive model , function (biology) , econometrics , system identification , time series , algorithm , statistics , computer science , data mining , mathematical analysis , paleontology , botany , fourier transform , evolutionary biology , biology , measure (data warehouse)
. Unobserved components ARIMA models are common in time series applications. However, fitting models of this type leads to problems of model identification. In this paper we derive a methodology to check whether a proposed model is identifiable. We show that this kind of identification can be checked using the autocovariance generating function and/or the (pseudo‐)spectral generating function.