On asymptotic risk of selecting models for possibly nonstationary time-series

Shu-Hui Yu and Chor-yiu (CY) Sin

Econometric Reviews, 2021, vol. 40, issue 4, 387-414

Abstract: Model selection criteria are often assessed by the so-called asymptotic risk. Asymptotic risk is defined either with the mean-squared error of estimated parameters; or with the mean-squared error of prediction. The literature focuses on i.i.d. or stationary time-series data though. Using the latter definition of asymptotic risk, this paper assesses the conventional AIC-type and BIC-type information criteria, which are arguably most suitable for univariate time series in which the lags are naturally ordered. Throughout we consider a univariate AR process in which the AR order and the order of integratedness are finite but unknown. We prove the BIC-type information criterion, whose penalty goes to infinity, attains zero asymptotic excess risk. In contrast, the AIC-type information criterion, whose penalty goes to a finite number, renders a strictly positive asymptotic excess risk. Further, the asymptotic excess risk increases with the admissible number of lags. The last result gives a warning on possible over-fitting of certain high-dimensional analyses, should the underlying data generating process be strongly sparse, that is, the true dimension be finite. In sum, we extend the existing asymptotic risk results in threefold: (i) a general I(d) process; (ii) same-realization prediction; and (iii) an information criterion more general than AIC. A simulation study and a small-scale empirical application compare the excess risk of AIC with those of AIC3, HQIC, BIC, Lasso as well as adaptive Lasso.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2020.1777709 (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: https://EconPapers.repec.org/RePEc:taf:emetrv:v:40:y:2021:i:4:p:387-414

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2020.1777709

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 ().