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Biases of Correlograms and of AR Representations of Stationary Series

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  • Karim M. Abadir

    (Imperial College London, UK)

  • Rolf Larsson

    (Uppsala University, Sweden)

Abstract

We derive the relation between the biases of correlograms and of estimates of auto-regressive AR(k) representations of stationary series, and we illustrate it with a simple AR example. The new relation allows for k to vary with the sample size, which is a representation that can be used for most stationary processes. As a result, the biases of the estimators of such processes can now be quantified explicitly and in a unified way.

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 24_12.

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Date of creation: Jun 2012
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Handle: RePEc:rim:rimwps:24_12

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Keywords: Auto-correlation function (ACF) and correlogram; autoregressive (AR) representation; least-squares bias;

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  1. Kiviet, Jan F. & Phillips, Garry D. A., 1994. "Bias assessment and reduction in linear error-correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 215-243, July.
  2. Abadir, K.M. & Magnus, J.R., 2001. "Notation in Econometrics: A Proposal for a Standard," Discussion Paper 2001-8, Tilburg University, Center for Economic Research.
  3. Abadir, Karim M., 1993. "Ols Bias in a Nonstationary Autoregression," Econometric Theory, Cambridge University Press, vol. 9(01), pages 81-93, January.
  4. Liudas Giraitis & Javier Hidalgo & Peter M Robinson, 2001. "Gaussian Estimation of Parametric Spectral Density with Unknown Pole," STICERD - Econometrics Paper Series /2001/424, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  5. L Giraitis & J Hidalgo & Peter M. Robinson, 2001. "Gaussian estimation of parametric spectral density with unknown pole," LSE Research Online Documents on Economics 297, London School of Economics and Political Science, LSE Library.
  6. Abadir,Karim M. & Magnus,Jan R., 2005. "Matrix Algebra," Cambridge Books, Cambridge University Press, number 9780521537469, October.
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