ESTIMASI ROBUST PADA MODEL SEEMINGLY UNRELATED REGRESSION; ROBUST ESTIMATION OF THE SEEMINGLY UNRELATED REGRESSION MODEL

MASFUFA, FELLA SHUFA NUR, Danardono

2016 | Skripsi | FMIPA

Seemingly Unrelated Regression (SUR) model is a system of linear equations consisting equation which errors in different equations are contemporaneously correlated. In this case, the Least Square method (Ordinary Least Squares, OLS) can be used to estimate the parameters of each equation, but the weaknesses of OLS method is remove information on a possible correlation on system equations. This final task, the method of Generalized Least Square (GLS) used to estimate parameters SUR. However GLS less able to withstand the presence of outliers, then used a robust S to estimate model parameters SUR containing outliers. Estimator S is equivariant estimator of regression and the breakdown point can reach as high as 50%, meaning it can handle almost half of bad observation and provides good leverage. Lagrange Multiplier test and Likelihood Ratio test (Likelihood Ratio, LR) is used to test the correlation contemporaneously on the error. The discussion will conclude with a case study on the factors that affect the Foreign Direct Investment (FDI) in the two countries, Indonesia and the Philippines. In the case study concluded that the robust estimation S is better than the GLS estimates.

Kata Kunci : Seemingly Unrelated Regression, Generalized Least Square, Robust S, Lagrange Multiplier test.