Spurious Regressions With Time-Series data: Further Asymptotic Results
AbstractA “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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Bibliographic InfoPaper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0603.
Length: 18 pages
Date of creation: 17 Aug 2006
Date of revision:
Note: ISSN 1485-6441
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Spurious regression; normality; homoskedasticity; asymptotic theory; unit roots;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-08-26 (All new papers)
- NEP-ECM-2006-08-26 (Econometrics)
- NEP-ETS-2006-08-26 (Econometric Time Series)
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