1 Department of Economics and Business Economics, Aarhus BSS, Aarhus University2 Department of Economics and Business Economics - Center for Research in Econometric Analysis of Time Series (CREATES), Department of Economics and Business Economics, Aarhus BSS, Aarhus University3 Department of Economics and Business Economics, Aarhus BSS, Aarhus University
A comparative study
The finite sample properties of the state space methods applied to long memory time series are analyzed through Monte Carlo simulations. The state space setup allows to introduce a novel modeling approach in the long memory framework, which directly tackles measurement errors and random level shifts. Missing values and several alternative sources of misspecification are also considered. It emerges that the state space methodology provides a valuable alternative for the estimation of the long memory models, under different data generating processes, which are common in financial and economic series. Two empirical applications highlight the practical usefulness of the proposed state space methods.
Computational Statistics and Data Analysis, 2014, Vol 76, Issue 2, p. 301-319
ARFIMA models; State space; Missing observations; Measurement error; Level shifts