Economics at your fingertips  

Multistep forecast selection for panel data

Ryan Greenaway-McGrevy

Econometric Reviews, 2020, vol. 39, issue 4, 373-406

Abstract: We develop a new set of model selection methods for direct multistep forecasting of panel data vector autoregressive processes. Model selection is based on minimizing the estimated multistep quadratic forecast risk among candidate models. To attenuate the small sample bias of the least squares estimator, models are fitted using bias-corrected least squares. We provide conditions sufficient for the new selection criteria to be asymptotically efficient as n (cross sections) and T (time series) approach infinity. The new criteria outperform alternative selection methods in an empirical application to forecasting metropolitan statistical area population growth in the US.

Date: 2020
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1080/07474938.2019.1651490

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().

Page updated 2022-01-18
Handle: RePEc:taf:emetrv:v:39:y:2020:i:4:p:373-406