New Hedonic Quality Adjustment Method using Sparse Estimation

In the application of the hedonic quality adjustment method to the price index, multicollinearity and the omitted variable bias arise as practical issues. This study proposes the new hedonic quality adjustment method using esparse estimation f in order to overcome these problems. The new method deals with these problems by ensuring two properties: the egrouped effect f that gives robustness for multicollinearity and the eoracle property f that provides the appropriate variable selection and asymptotically unbiased estimators. We conduct an empirical analysis applying the new method to the producer price index of passenger cars in Japan. In comparison with the conventional standard estimation method, the new method brings the following benefits: 1) a significant increase in the number of variables in the regression model; 2) an improvement in the fit of the regression model to actual prices; and 3) reduced overestimation of the product quality improvements due to the omitted variable bias. These results suggest the possible improvement in the accuracy of the price index while enhancing the usefulness of the hedonic quality adjustment method.