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Increasing the efficiency of pooled estimation with a block-diagonal covariance structure

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Abstract

A small number of time-series observations relative to regions precludes estimation of the entire structure of regional dependence in a pooled regression model. The resulting need for parsimonious models of regional dependence can be satisfied through the use of spatial autocorrelation structures. This article explores an alternative methodology that allows the researcher to estimate disturbance covariances for regions that are closely linked, even when the number of time-series observations is relatively low. The approach presented here shares the advantage of spatial autocorrelation structures in being parsimonious, but offers the additional advantage of relying more completely on sample information to provide estimates of dependence between regions within specified regional groups. Monte Carlo experiments suggest that block-covariance models offer substantial efficiency gains over simple heteroskedastic models. The experiments also suggest that when the number of time-series observations is limited and the correlations of disturbances between regions are small, block structures yield efficiency gains over a full-information model.

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Authors and Affiliations

  1. Federal Reserve Bank of Dallas, 2200 North Pearl Street, 75201, Dallas, TX, USA

    Jeffery W. Gunther

  2. Federal Reserve Bank of San Francisco, 101 Market Street, 94105, San Francisco, CA, USA

    Ronald H. Schmidt

Authors
  1. Jeffery W. Gunther
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  2. Ronald H. Schmidt
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Additional information

We thank Thomas Fomby, Esfandiar Maasoumi, and the referees for helpful comments. The views expressed are those of the authors and do not necessarily reflect official views of the Federal Reserve System.

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Gunther, J.W., Schmidt, R.H. Increasing the efficiency of pooled estimation with a block-diagonal covariance structure. Ann Reg Sci 27, 133–142 (1993). https://doi.org/10.1007/BF01581941

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  • DOI: https://doi.org/10.1007/BF01581941

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