The large sample property of the iterative generalized least squares estimation for hierarchical mixed effects model

Abstract

In many fields, we need to deal with hierarchically structured data. For this kind of data, hierarchical mixed effects model can show the correlation of variables in the same level by establishing a model for regression coefficients. Due to the complexity of the random part in this model, seeking an effective method to estimate the covariance matrix is an appealing issue. Iterative generalized least squares estimation method was proposed by Goldstein in 1986 and was applied in special case of hierarchical model. In this paper, we extend the method to the general hierarchical mixed effects model, derive its expressions in detail and apply it to economic examples.

Graphical abstract

Keywords

Hierarchical model / iterative generalized least squares estimation / variance-covariance components / maximum likelihood estimation

References

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