Abstract
This paper has two themes. First, we classify some effects which outliers in the data have on unit root inference. We show that, both in a classical and a Bayesian framework, the presence of additive outliers moves 'standard' inference towards stationarity. Second, we base inference on an independent Student-t instead of a Gaussian likelihood. This yields results that are less sensitive to the presence of outliers. Application to several time series with outliers reveals a negative correlation between the unit root and degrees of freedom parameter of the Student-t distribution. Therefore, imposing normality may incorrectly provide evidence against the unit root.
| Original language | English |
|---|---|
| Pages (from-to) | 27-59 |
| Number of pages | 33 |
| Journal | Journal of Econometrics |
| Volume | 69 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1995 |
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Keywords
- Bayesian analysis
- Outliers
- Robustness
- Student-t distribution
- Unit root inference
Cite this
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Classical and Bayesian aspects of robust unit root inference. / Hoek, Henk; Lucas, André; van Dijk, Herman K.
In: Journal of Econometrics, Vol. 69, No. 1, 1995, p. 27-59.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Classical and Bayesian aspects of robust unit root inference
AU - Hoek, Henk
AU - Lucas, André
AU - van Dijk, Herman K.
PY - 1995
Y1 - 1995
N2 - This paper has two themes. First, we classify some effects which outliers in the data have on unit root inference. We show that, both in a classical and a Bayesian framework, the presence of additive outliers moves 'standard' inference towards stationarity. Second, we base inference on an independent Student-t instead of a Gaussian likelihood. This yields results that are less sensitive to the presence of outliers. Application to several time series with outliers reveals a negative correlation between the unit root and degrees of freedom parameter of the Student-t distribution. Therefore, imposing normality may incorrectly provide evidence against the unit root.
AB - This paper has two themes. First, we classify some effects which outliers in the data have on unit root inference. We show that, both in a classical and a Bayesian framework, the presence of additive outliers moves 'standard' inference towards stationarity. Second, we base inference on an independent Student-t instead of a Gaussian likelihood. This yields results that are less sensitive to the presence of outliers. Application to several time series with outliers reveals a negative correlation between the unit root and degrees of freedom parameter of the Student-t distribution. Therefore, imposing normality may incorrectly provide evidence against the unit root.
KW - Bayesian analysis
KW - Outliers
KW - Robustness
KW - Student-t distribution
KW - Unit root inference
UR - http://www.scopus.com/inward/record.url?scp=0039175067&partnerID=8YFLogxK
U2 - 10.1016/0304-4076(94)01661-I
DO - 10.1016/0304-4076(94)01661-I
M3 - Article
VL - 69
SP - 27
EP - 59
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 1
ER -