Nonparametric Tests for Periodic Integration

Tomás del Barrio Castro and Denise Osborn

Journal of Time Series Econometrics, 2011, vol. 3, issue 1, 35

Abstract: We propose two nonparametric methods to test the null hypothesis of periodic integration, one based on the variance ratio unit root test of Breitung (2002) and the other on the modified Sargan-Bhargava test developed by Stock (1999). The former does not require specification of short-run dynamics, while nevertheless delivering a pivotal limiting distribution; however, the latter requires estimation of the long-run variance. The asymptotic distributions of the new statistics are shown to be invariant to whether the process is periodically integrated or a conventional I(1) process, whereas tests based on the I(1) assumption are not. Further, the new tests do not require nonlinear estimation and can be implemented in a straightforward way. Monte Carlo results show that the variance ratio test has very good finite sample size, but the modified Sargan-Bhargava test has much better power, which can be comparable to that of the parametric likelihood ratio test. Finally, an empirical application illustrates the use of the tests through an analysis of six monthly component Industrial Production Indices for the U.S.

Keywords: Periodic Integration; variance ratio tests; modified Sargan-Bhargava tests; seasonality; unit roots (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.2202/1941-1928.1090 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:jtsmet:v:3:y:2011:i:1:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jtse/html

DOI: 10.2202/1941-1928.1090

Access Statistics for this article

Journal of Time Series Econometrics is currently edited by Javier Hidalgo

More articles in Journal of Time Series Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().