Data envelopment analysis with shrinkage estimators
研究了在数据包络分析中应用收缩估计量来降低估计风险，通过对冲基金数据展示该方法能提高效率估计值且不显著改变排序。
Abstract Shrinkage estimators reduce estimation risk in multivariate statistics such as mean and standard deviation. They have not been used before in data envelopment analysis (DEA). By considering models of investment fund returns, we show that estimation risk can cause the range of estimates of inputs and outputs in a DEA model to be overestimated so that shrinkage estimators should improve them. We show how to use shrinkage estimators for mean and standard deviation in DEA and develop a shrinkage estimator for expected shortfall. We further show how to adapt these estimators for diversification-consistent models. We illustrate DEA with shrinkage estimation on returns for hedge funds and find that using shrinkage estimators to improve the estimates of efficiencies tends to increase efficiency estimates without substantially changing their rank order.