Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation.

Jurgen A. Doornik; Marius Ooms

Abstract:

Practical aspects of likelihood-based inference and forecasting of series with long memory are considered, based on the ARFIMA(p; d; q) model with deterministic regressors. Sampling characteristics of approximate and exact first-order asymptotic methods are compared. The analysis is extended using modified profile likelihood analysis, which is a higher-order asymptotic method suggested by Cox and Reid (1987). The relevance of the differences between the methods is investigated for models and ...

Expand abstract

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Cite

Cite this record

APA Style

Doornik, J. A., & Ooms, M. (2004). Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation. Studies in Nonlinear Dynamics and Econometrics, 8(2).

MLA Style

Doornik, Jurgen A., and Marius Ooms. “Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation.” Studies in Nonlinear Dynamics and Econometrics, vol. 8, no. 2, 2004.

Chicago Style

Doornik, Jurgen A., and Marius Ooms. 2004. “Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation.” Studies in Nonlinear Dynamics and Econometrics 8 (2).
Tweet
Print