Classical and Bayesian aspects of robust unit root inference
January 1995
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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.
Keywords
Classifications using
Journal of Economic Literature (JEL) Classification System
- C11 : Bayesian Analysis
- C12 : Hypothesis Testing
- C22 : Time-Series Models; Dynamic Quantile Regressions
Automatically Extracted Terms
- series
- outlier
- estimator
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- parameter
- econometrics
- student-t
- likelihood
- bayesian
- distribution
- time series
- unit root hypothesis
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- 27-59
- ols estimator
