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Spurious regression: A higher-order problem

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Author Info

  • Sollis, Robert

Abstract

This paper investigates solving the spurious regression problem using an autocorrelation correction. It is shown that if the relevant data generation processes contain higher-order terms, this solution is not as effective as in the first-order case.

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File URL: http://www.sciencedirect.com/science/article/B6V84-520TJK9-3/2/164d65c84247c18d383f228ce56ac0e5
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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 111 (2011)
Issue (Month): 2 (May)
Pages: 141-143

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Handle: RePEc:eee:ecolet:v:111:y:2011:i:2:p:141-143

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Web page: http://www.elsevier.com/locate/ecolet

Related research

Keywords: Spurious regression Cochrane-Orcutt Simulation;

References

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  1. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
  2. Breusch, T S, 1978. "Testing for Autocorrelation in Dynamic Linear Models," Australian Economic Papers, Wiley Blackwell, vol. 17(31), pages 334-55, December.
  3. McCallum, Bennett T., 2010. "Is the spurious regression problem spurious?," Economics Letters, Elsevier, vol. 107(3), pages 321-323, June.
  4. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
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Cited by:
  1. Jin, Hao & Zhang, Jinsuo & Zhang, Si & Yu, Cong, 2013. "The spurious regression of AR(p) infinite-variance sequence in the presence of structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 25-40.

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