Incorporating spatial structures in ecological inference: an information theoretic approach
Abstract
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This paper proposes a maximum entropy (ME) – based method for modeling economic aggregates and estimating their sub-group (sub-area) decomposition when no individual or sub-group data are available. This method also offers a tractable framework for modeling the underlying variation in sub-group indicators. A basic ecological inference problem which allows for spatial heterogeneity is presented with the aim of estimating the model at the aggregate level and then employing the estimated coefficients to obtain the sub-group level indicators.
This paper proposes a maximum entropy (ME) – based method for modeling economic aggregates and estimating their sub-group (sub-area) decomposition when no individual or sub-group data are available. This method also offers a tractable framework for modeling the underlying variation in sub-group indicators. A basic ecological inference problem which allows for spatial heterogeneity is presented with the aim of estimating the model at the aggregate level and then employing the estimated coefficients to obtain the sub-group level indicators.
DOI Code:
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Keywords:
Generalized Cross Entropy Estimation; Ecological Inference; Spatial Heterogeneity
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