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The choice of time interval in seasonal adjustment: A heuristic approach

  • Giancarlo Bruno

    ()

  • Edoardo Otranto

    ()

A typical problem of the seasonal adjustment procedures arises when the series to be adjusted is subject to structural breaks. In fact, using the full span of the series can result in a biased estimation of the ”true” seasonal adjusted series, with unclear evidence showed by the usual diagnostic tests. In these cases the researcher has to decide where to cut-o the observed series to obtain a homogeneous span; this is generally performed by a simple visual inspection studies of the graph of the series and/or using a-priori information about the occurrence of the break. In this paper we propose a statistical criterion based on a distance measure between filters, evaluating its performance with Monte Carlo experiments.

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File URL: http://hdl.handle.net/10.1007/s00362-006-0295-x
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Article provided by Springer in its journal Statistical Papers.

Volume (Year): 47 (2006)
Issue (Month): 3 (June)
Pages: 393-417

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Handle: RePEc:spr:stpapr:v:47:y:2006:i:3:p:393-417
Contact details of provider: Web page: http://www.springer.com/statistics/business/journal/362

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  1. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July.
  2. Ghysels, Eric & Perron, Pierre, 1996. "The effect of linear filters on dynamic time series with structural change," Journal of Econometrics, Elsevier, vol. 70(1), pages 69-97, January.
  3. Donald W.K. Andrews & Werner Ploberger, 1992. "Optimal Tests When a Nuisance Parameter Is Present Only Under the Alternative," Cowles Foundation Discussion Papers 1015, Cowles Foundation for Research in Economics, Yale University.
  4. Agustín Maravall, 1996. "Unobserved Components in Economic Time Series," Banco de Espa�a Working Papers 9609, Banco de Espa�a.
  5. Ghysels, E. & Perron, P., 1990. "The Effect Of Seasonal Adjustment Filters On Tests For A Unit Root," Papers 355, Princeton, Department of Economics - Econometric Research Program.
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