M-estimates for the multiplicative error model. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Lu, Wanbo; Wang, Yanfeng; Gao, Yuxuan
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 1(2020)
• Page Start: 1
• Page End: 27
• Publication Date: 2020-01-02
• Subjects: M-estimates and BM-estimates -- loss function -- multiplicative error model -- outliers; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1671387
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In this paper, we propose two types of robust estimates for the multiplicative error model, M-estimates and BM-estimates, and analyse their estimation effects for different loss functions. According to the Monte Carlo simulation, we find that the M-estimates perform well regardless of whether the data contain outliers. The BM-estimates based on the loss function L are less affected than the other estimation methods for additive outliers. The M-estimates based on the loss function L present better robustness than the other estimation methods for innovational outliers. The empirical application shows that the out-of-sample forecasting capacity of the M-estimates based on the loss function L is better.