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Filtered least squares and measurement error

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  • Andrew P. Blake and

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  • Gonzalo Camba-Mendez

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Abstract

Li, Maddala, and Rush (1995) proposed a low-pass spectral filter method to estimate cointegrating vectors in small samples. This paper tests the effectiveness of the approach in the presence of measurement error. Two other methods, valid under the assumption of stationarity, are also tested.

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Bibliographic Info

Paper provided by National Institute of Economic and Social Research in its series NIESR Discussion Papers with number 130.

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Date of creation: Jan 1998
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Handle: RePEc:nsr:niesrd:130

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  1. Granger, Clive W J, 1986. "Developments in the Study of Cointegrated Economic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 213-28, August.
  2. Banerjee, Anindya, et al, 1986. "Exploring Equilibrium Relationships in Econometrics through Static Models: Some Monte Carlo Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 253-77, August.
  3. Phillips, Peter C B & Hansen, Bruce E, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," Review of Economic Studies, Wiley Blackwell, vol. 57(1), pages 99-125, January.
  4. Li, Yikang & Maddala, G. S. & Rush, Mark, 1995. "New small sample estimators for cointegration regression: Low-pass spectral filter method," Economics Letters, Elsevier, vol. 47(2), pages 123-129, February.
  5. Perron, P, 1988. "The Great Crash, The Oil Price Shock And The Unit Root Hypothesis," Papers 338, Princeton, Department of Economics - Econometric Research Program.
  6. Fischer, Andreas M., 1990. "Cointegration and I(0) measurement error bias," Economics Letters, Elsevier, vol. 34(3), pages 255-259, November.
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Cited by:
  1. Fukuda, Kosei, 2005. "Unit-root detection allowing for measurement error," Statistics & Probability Letters, Elsevier, vol. 74(4), pages 373-377, October.

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