In this paper we propose a sparse indirect inference estimator. In order to achieve sparse estimation of the parameters, the Smoothly Clipped Absolute Deviation (SCAD) L1–penalty of Fan and Li (2001) is added into the indirect inference objectivefunctionintroducedbyGouri´erouxetal.(1993).Wederivetheasymptotic theory and we show that the sparse–Indirect Inference estimator enjoys the oracle properties under mild regularity conditions. The method is applied to estimate the parameters of large dimensional non–Gaussian regression models
Sparse indirect inference / Paola, Stolfi; Bernardi, Mauro; Petrella, Lea. - STAMPA. - (2017), pp. 961-968. (Intervento presentato al convegno Statistics and Data Science: new challenges, new generations. Proceedings of the Conference of the Italian Statistical Society 2017 tenutosi a Firenze).
Sparse indirect inference
Lea Petrella
2017
Abstract
In this paper we propose a sparse indirect inference estimator. In order to achieve sparse estimation of the parameters, the Smoothly Clipped Absolute Deviation (SCAD) L1–penalty of Fan and Li (2001) is added into the indirect inference objectivefunctionintroducedbyGouri´erouxetal.(1993).Wederivetheasymptotic theory and we show that the sparse–Indirect Inference estimator enjoys the oracle properties under mild regularity conditions. The method is applied to estimate the parameters of large dimensional non–Gaussian regression models| File | Dimensione | Formato | |
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