Interpolation and correlation

Philip Hans Franses

Applied Economics, 2022, vol. 54, issue 14, 1562-1567

Abstract: Historical time series sometimes have missing observations. It is common practice either to ignore these missing values or otherwise to interpolate between the adjacent observations and continue with the interpolated data as true data. This paper shows that interpolation changes the autocorrelation structure of the time series. Ignoring such autocorrelation in subsequent correlation or regression analysis can lead to spurious results. A simple method is presented to prevent spurious results. A detailed illustration highlights the main issues.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2021.1980199 (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:applec:v:54:y:2022:i:14:p:1562-1567

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2021.1980199

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().