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Spurious deterministic seasonality and autocorrelation corrections with quarterly data: Further Monte Carlo results

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  • Artur C. B. da Silva Lopes

    ()
    (Instituto Superior de Economia e GestÇo, Universidade TÊcnica de Lisboa, gab. 411, Rua do Quelhas, 6, P-1200 Lisboa, Portugal)

Abstract

Following recent work of Franses, Hylleberg and Lee (FHL), this paper analyses the consequences of fitting a deterministic seasonal model to a quarterly time series which can be (at least approximately) described by a seasonal unit root(s) model. Besides the distribution of the coefficient of determination, the empirical distributions of two commonly used statistics are also investigated through Monte Carlo experiments for small, moderately large and large samples. FHL's work is also extended allowing the possibility of residual autocorrelation corrections. The main conclusion that emerges from the results is that one should not try to measure the importance of deterministic seasonality nor test for its presence in the context of such (static) regression models, even when using some form of residual autocorrelation correction. A simple empirical application is provided to illustrate our results.

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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 24 (1999)
Issue (Month): 2 ()
Pages: 341-359

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Handle: RePEc:spr:empeco:v:24:y:1999:i:2:p:341-359

Note: received: July 1997/final version received: July 1998
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Related research

Keywords: Seasonality · unit roots · spurious regression · Monte Carlo;

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Cited by:
  1. da Silva Lopes, Artur C. B., 2001. "The robustness of tests for seasonal differencing to structural breaks," Economics Letters, Elsevier, vol. 71(2), pages 173-179, May.
  2. Artur Da Silva Lopes, 2004. "Deterministic Seasonality In Dickey-Fuller Tests: Should We Care?," Royal Economic Society Annual Conference 2004 75, Royal Economic Society.
  3. Ghassen El Montasser & Talel Boufateh & Fakhri Issaoui, 2013. "The Seasonal KPSS Test When Neglecting Seasonal Dummies: A Monte Carlo analysis," EERI Research Paper Series EERI RP 2013/07, Economics and Econometrics Research Institute (EERI), Brussels.

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