Robust Estimation for a Generalised Ratio Model

Abstract

It is known that data such as business sales and household income need data transformation prior to regression estimate as the data has a homoscedastic error. However, data transformations make the estimation of mean and total unstable. Therefore, the ratio model is often used for imputation in the field of official statistics to avoid the problem.

Our study aims to robustify the estimator following the ratio model by means of M-estimation. Reformulation of the conventional ratio model with homoscedastic quasi-error term provides quasi-residuals which can be used as a measure of outlyingness as same as a linear regression model. A generalisation of the model, which accommodates varied error terms with different heteroscedasticity, is also proposed.

Functions for robustified estimators of the generalised ratio model are implemented by the iterative re-weighted least squares algorithm in R environment and illustrated using random datasets. Monte Carlo simulation confirms accuracy of the proposed estimators, as well as their computational efficiency. A comparison of the scale parameters between the average absolute deviation (AAD) and median absolute deviation (MAD) is made regarding Tukey's biweight function. The results with Huber's weight function are also provided for reference.

The proposed robust estimator of the generalised ratio model is used for imputation of major corporate accounting items of the 2016 Economic Census for Business Activity in Japan.