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Model-Selection Tests for Complex Survey Samples

Iraj Rahmani () and Jeffrey Wooldridge ()

A chapter in The Econometrics of Complex Survey Data, 2019, vol. 39, pp 109-135 from Emerald Publishing Ltd

Abstract: Abstract We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

Keywords: Survey sampling; weighted estimation; cluster sampling; nonnested models; model selection test; m-estimation (search for similar items in EconPapers)
Date: 2019
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