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A note on the asymptotic efficiency of the restricted estimation

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  • José A. Hernández

    (University of Las Palmas de Gran Canaria; Facultad de CC. EE y EE. Despacho D312; Campus de Tafira; C/ Saulo Torón 4; 35017; Las Palmas de G.C. Spain Tfno (0034) 928458206, Fax (0034) 928458183)

Abstract

This paper provides a unified framework for the analysis of the stochastic and deterministic constrained estimation. In a general framework it is show that stochastic restrictions method estimates can be asymptotically more e.cient than estimates ignoring prior information, and can achieve efficiency of the restricted estimate if prior information grows faster than the sample information in the asymptotics. As an example of the applicability of the previous result, the maximum likelihood stochastically restricted criterion is provided.

Suggested Citation

  • José A. Hernández, 2005. "A note on the asymptotic efficiency of the restricted estimation," Documentos de trabajo conjunto ULL-ULPGC 2005-01, Facultad de Ciencias Económicas de la ULPGC.
    • Handle: RePEc:can:series:2005-01
    as

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    References listed on IDEAS

    as
    1. Kadane, Joseph B, 1971. "Comparison of k-Class Estimators when the Disturbances are Small," Econometrica, Econometric Society, vol. 39(5), pages 723-737, September.
    2. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    3. Shiller, Robert J, 1973. "A Distributed Lag Estimator Derived from Smoothness Priors," Econometrica, Econometric Society, vol. 41(4), pages 775-788, July.
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