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.

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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

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Extent:	application/pdf
Series:	Econometrics Working Papers.
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:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005839160