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Least Orthogonal Distance Estimator and Total Least Square for Simultaneous Equation Models

Author

Listed:
  • Alessia Naccarato

    (Università degli Studi Roma Tre, Roma - Italy)

  • Davide Zurlo

    (ISTAT, Istituto Nazionale di Statistica, Roma - Italy)

  • Luciano Pieraccini

    (Università degli Studi Roma Tre, Roma - Italy)

Abstract

Least Orthogonal Distance Estimator (LODE) of Simultaneous Equation Models’ structural parameters is based on minimizing the orthogonal distance between Reduced Form (RF) and the Structural Form (SF) parameters. In this work we propose a new version – with respect to Pieraccini and Naccarato (2008) – of Full Information (FI) LODE based on decomposition of a new structure of the variance-covariance matrix using Singular Value Decomposition (SVD) instead of Spectral Decomposition (SD). In this context Total Least Square is applied. A simulation experiment to compare the performances of the new version of FI LODE with respect to Three Stage Least Square (3SLS) and Full Information Maximum Likelihood (FIML) is presented. Finally a comparison between the FI LODE new and old version together with few words of conclusion conclude the paper

Suggested Citation

  • Alessia Naccarato & Davide Zurlo & Luciano Pieraccini, 2013. "Least Orthogonal Distance Estimator and Total Least Square for Simultaneous Equation Models," Statistica, Department of Statistics, University of Bologna, vol. 73(2), pages 201-219.
    • Handle: RePEc:bot:rivsta:v:73:y:2013:i:2:p:201-219
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