High covariate dimensionality is an increasingly occurrent phenomenon in model estimation. A common approach to handling high-dimensionality is regularisation, which requires sparsity of model parameters. However, sparsity may not always be supported by economic theory or easily verified in some empirical contexts; severe bias and misleading inference can occur. This paper introduces a grouped parameter estimator (GPE) that circumvents this problem by using a parameter clustering technique. The large sample properties of the GPE hold under fairly standard conditions including a compact parameter support that can be bounded away from zero. Monte Carlo simulations demonstrate the excellent performance of the GPE relative to competing estimators in terms of bias and size control. Lastly, an empirical application of the GPE to the estimation of price and income elasticities of demand for gasoline illustrates the practical utility of the GPE.

翻译：高协变量维度在模型估计中日益常见。处理高维度的常用方法是正则化，这要求模型参数具有稀疏性。然而，稀疏性不一定总能得到经济理论支持，或在某些实证背景下易于验证，可能导致严重偏差和误导性推断。本文提出了一种分组参数估计量（GPE），通过参数聚类技术规避了这一问题。GPE的大样本性质在相当标准条件下成立，包括一个可能远离零的有界紧致参数支撑。蒙特卡洛模拟表明，GPE在偏差和尺度控制方面相较于竞争估计量具有优异表现。最后，将GPE应用于汽油需求价格弹性和收入弹性的实证估计，展示了其实用价值。