First-differencing is generally taken to imply the loss of one observation, the first, or at least that the effect of ignoring this observation is asymptotically negligible. However, this is not always true, as in the case of GLS detrending. In order to illustrate this, the current paper considers as an example the use of GLS detrended data when testing for a unit root. The results show that the treatment of the first observation is absolutely crucial for test performance, and that ignorance causes test break-down.
History
Pagination
1-13
Language
eng
Notes
School working paper (Deakin University. School of Accounting, Economics and Finance) ; 2014/07
First-differencing is generally taken to imply the loss of one observation, the first, or at least that the effect of ignoring this observation is asymptotically negligible. However, this is not always true, as in the case of GLS detrending. In order to illustrate this, the current paper considers as an example the use of GLS detrended data when testing for a unit root. The results show that the treatment of the first observation is absolutely crucial for test performance, and that ignorance causes test break-down.
Publication classification
CN.1 Other journal article
Copyright notice
2014, The Author
Publisher
Deakin University, School of Accounting, Economics and Finance
Place of publication
Geelong, Vic.
Series
School Working Paper - Financial Econometrics Series