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Spurious Regressions With Time-Series data: Further Asymptotic Results

A “spurious regression” is one in which the time-series variables are non-stationary and independent. It is well-known that in this context the OLS parameter estimates and the R2 converge to functionals of Brownian motions; the “t-ratios” diverge in distribution; and the Durbin-Watson statistic converges in probability to zero. We derive corresponding results for some common tests for the Normality and homoskedasticity of the errors in a spurious regression.
Year of publication:
2006-08-17
Authors: Giles, David E. A.
Institutions: Department of Economics, University of Victoria
Subject: Spurious regression | normality | homoskedasticity | asymptotic theory | unit roots
Extent:
application/pdf
Series:
Type of publication: Book / Working Paper
Language: English
Notes:
ISSN 1485-6441 Number 0603 18 pages
Classification: C12 - Hypothesis Testing ; C22 - Time-Series Models ; C52 - Model Evaluation and Testing
Source:
Persistent link: https://www.econbiz.de/10005839160
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