PERBANDINGAN ESTIMATOR CENSORED LEAST ABSOLUTE DEVIATIONS (CLAD) DAN SYMMETRICALLY CENSORED LEAST SQUARES (SCLS) UNTUK MODEL REGRESI TOBIT (Studi Kasus : Analisis Faktor-Faktor yang Mempengaruhi Partisipasi Perempuan dalam Perekonomian Rumah Tangga di Provinsi Daerah Istimewa Yogyakarta) - , VANIA PRIMA AMELINDA ([Yogyakarta] : Universitas Gadjah Mada, 2014)
This graduating paper discusses alternatives for maximum likelihood estimation of the censored regression or censored âTobitâ model. There are two alternative methods that will be discusses and compared: Censored Least Absolute Deviations (CLAD) and Symmetrically Censored Least Absolute Deviations (SCLS). Unlike maximum likelihood estimator, CLAD is consistent and asymptotically normal for a wide class of error distributions and robust to heterokedasticity. Meanwhile, SCLS is not completely general, since it is based upon the assumption of symmetrically (and independently) distributed error terms. However this estimator will be consistent even though the residuals are not identically distributed and remain robust to heterokedasticity data. In this case study, the researcher use National Labor Force Survey Data 2013 to examine factors that influence womenâs participant in domestic economy of Yogyakarta province.