In the context of competing IV econometric models and estimators, we demonstrate a semiparametric Stein-like estimator (SSLE) that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide whether covariate endogeneity is present and makes use of a pretest estimator choice between IV and non-IV methods unnecessary. A sampling study is used to illustrate finite sample performance over a range of sampling designs, including its performance relative to pretest estimators. An important applied problem from the literature is analyzed to indicate possible applied implications and the relation of SSLE to other modern IV estimators.
Judge, G. and Mittelhammer, R. (2012), "A Risk Superior Semiparametric Estimator for Overidentified Linear Models", Terrell, D. and Millimet, D. (Ed.) 30th Anniversary Edition (Advances in Econometrics, Vol. 30), Emerald Group Publishing Limited, Bingley, pp. 237-255. https://doi.org/10.1108/S0731-9053(2012)0000030013Download as .RIS
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