Premium
Spectral Regression For Cointegrated Time Series With Long‐Memory Innovations
Author(s) -
Marinucci D.
Publication year - 2000
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 - 10.1111/1467-9892.00204
Subject(s) - mathematics , corollary , cointegration , ordinary least squares , series (stratigraphy) , econometrics , long memory , statistics , central limit theorem , regression , least squares function approximation , quadratic equation , asymptotic distribution , unit root , pure mathematics , volatility (finance) , paleontology , geometry , estimator , biology
Spectral regression is considered for cointegrated time series with long‐memory innovations. The estimates we advocate are shown to be consistent when cointegrating relationships among stationary variables are investigated, while ordinary least squares are inconsistent due to correlation between the regressors and the cointegrating residuals; in the presence of unit roots, these estimates share the same asymptotic distribution as ordinary least squares. As a corollary of the main result, we provide a functional central limit theorem for quadratic forms in non‐stationary fractionally integrated processes.