### Abstract

Original language | English |
---|---|

Pages | 109-135 |

Number of pages | 26 |

Publication status | Published - 2019 |

### Publication series

Name | Advances in Econometrics |
---|---|

Publisher | Emerald Publishing Limited |

Volume | 39 |

ISSN (Print) | 0731-9053 |

### Fingerprint

### Cite this

*Model Selection Tests for Complex Survey Samples*. (pp. 109-135). (Advances in Econometrics; Vol. 39).

**Model Selection Tests for Complex Survey Samples.** / Rahmani, Iraj; Wooldridge, Jeffrey.

Research output: Working paper

}

TY - UNPB

T1 - Model Selection Tests for Complex Survey Samples

AU - Rahmani, Iraj

AU - Wooldridge, Jeffrey

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

M3 - Working paper

T3 - Advances in Econometrics

SP - 109

EP - 135

BT - Model Selection Tests for Complex Survey Samples

ER -