Gaussian Semiparametric Estimation of Multivariate Fractionally Integrated Processes

Arbeitspapier

This paper analyzes the semiparametric estimation of multivariate long-range dependent processes. The class of spectral densities considered is motivated by and includes those of multivariate fractionally integrated processes. The paper establishes the consistency of the multivariate Gaussian semiparametric estimator (GSE), which has not been shown in other work, and the asymptotic normality of the GSE estimator. The proposed GSE estimator is shown to have a smaller limiting variance than the two-step GSE estimator studied by Lobato (1999). Gaussianity is not assumed in the asymptotic theory. Some simulations confirm the relevance of the asymptotic results in samples of the size used in practical work.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Queen's Economics Department Working Paper ; No. 1062

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• Subject: fractional integration ; long memory ; semiparametric estimation ; Stochastischer Prozess ; Nichtparametrisches Verfahren ; Stochastischer Prozess ; Nichtparametrisches Verfahren

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Shimotsu, Katsumi
• Event: Veröffentlichung
• (who): Queen's University, Department of Economics
• (where): Kingston (Ontario)
• (when): 2006

Further information

• Handle: http://hdl.handle.net/10419/189342
• Last update: 10.03.2025, 11:44 AM CET