Heteroskedastic cointegration. (English) Zbl 0755.62085
Summary: This paper develops an asymptotic theory of estimation and inference in ’cointegrated’ regression models with errors displaying nonstationary variances. Least squares estimates are shown to be consistent at a \(T^{1/2}\) rate. Hypothesis testing requires the use of a robust covariance matrix estimate, in contrast to earlier work on cointegrated regressions. The inference theory is not nuisance-free, but preliminary investigations indicate that approximation by the normal distribution may be adequate in practice.
MSC:
| 62P20 | Applications of statistics to economics |
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
| 62J05 | Linear regression; mixed models |
| 62F12 | Asymptotic properties of parametric estimators |
| 62F05 | Asymptotic properties of parametric tests |
Keywords:
heteroskedastic cointegration; normal approximations; consistency; nonstationary variances; least squares estimates; robust covariance matrix estimate; cointegrated regressionsReferences:
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