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Repeated surveys and the Kalman filter

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  • Jo Thori Lind

Abstract

The time-series nature of repeated surveys is seldom taken into account. The few studies that do so smooth the period-wise estimates without using the cross-sectional information. This leads to inefficient estimation. We present a statistical model of repeated surveys and construct a computationally simple estimator based on the Kalman filter algorithm. The method efficiently uses the whole underlying data set, but only the first and second moments of the data are required for computational purposes. Copyright 2005 Royal Economic Society

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2005.00172.x
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Bibliographic Info

Article provided by Royal Economic Society in its journal The Econometrics Journal.

Volume (Year): 8 (2005)
Issue (Month): 3 (December)
Pages: 418-427

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Handle: RePEc:ect:emjrnl:v:8:y:2005:i:3:p:418-427

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  1. Andrew Harvey & Chia-Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
  2. Pfeffermann, Danny, 1991. "Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 177, April.
  3. Pfeffermann, Danny, 1991. "Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(2), pages 163-75, April.
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