Deriving Benefit Measures with Higher Precision: A Study of Economic Values of Air Quality
提出一种使用岭回归校准策略，从标准OLS结果中计算更精确的参数估计，并应用于30个住房研究中空气污染对房产价值的边际效应，发现岭估计在均方误差准则下优于OLS估计。
A calibration strategy using ridge regression to generate more precise estimates for a particular parameter in a model is proposed. Formulae to compute the proposed ridge estimates from standard ordinary least squares (OLS) results are provided. The strategy is applied to recomputing marginal effects of air pollution on property values for 30 housing studies. Results show that ridge estimates are superior to the OLS estimates under the mean squared error criterion. The same strategy can be used to reestimate key parameters of interest in other applications, such as price elasticities for demand forecasts or the value of a statistical life from hedonic wage regressions. <i></i>