Robust Nonstationary Regression
This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed that allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities, and they belong to the same family as the fully modified least-squares (FM-OLS) estimator of Phillips and Hansen (1990, <italic>Review of Economic Studies</italic> 57,99–125). Specific attention is given to fully modified least absolute deviation (FM-LAD) estimation and fully modified <italic>M</italic> (FM-M) estimation. The criterion function for LAD and some <italic>M</italic>-estimators is not always smooth, and this paper develops generalized function methods to cope with this difficulty in the asymptotics. The results given here include a strong law of large numbers and some weak convergence theory for partial sums of generalized functions of random variables. The limit distribution theory for FM-LAD and FM-M estimators that is developed includes the case of finite variance errors and the case of heavytailed (infinite variance) errors. Some simulations and a brief empirical illustration are reported.
Year of publication: |
1995
|
---|---|
Authors: | Phillips, Peter C.B. |
Published in: |
Econometric Theory. - Cambridge University Press. - Vol. 11.1995, 05, p. 912-951
|
Publisher: |
Cambridge University Press |
Description of contents: | Abstract [journals.cambridge.org] |
Saved in:
Saved in favorites
Similar items by person
-
Explosive behavior in the 1990s Nasdaq: when did exuberance escalate asset values?
Phillips, Peter C.B., (2010)
-
Two New Zealand pioneer econometricians
Phillips, Peter C.B., (2010)
-
Testing mean stability of heteroskedastic time series
Dalla, Violetta, (2015)
- More ...