Semiparametric Regression Analysis on the AB Data with Zero-Point Drift

2014 ◽  
Vol 568-570 ◽  
pp. 227-232
Author(s):  
Yuan Tian ◽  
Jie Luo

To deal with the AB data with zero-point drift, Swanson and Schlamminger have proposed a filter method to remove the drift of any desired polynomial order, and then give the best linear unbiased estimator of the observable, on the condition that the order of drift is known. Since this method directly removes the drift, one can not know the tendency of the systematic error. This paper proposes to construct a semiparametric regression model for the data, and then take the penalized least squares method to estimate the observable and the drift tendency simultaneously. Simulation analysis shows that the semiparametric method can reach the same accuracy of the filter method, and the estimation of the drift fits well with its actual value.

Author(s):  
Elton G. Aráujo ◽  
Julio C. S. Vasconcelos ◽  
Denize P. dos Santos ◽  
Edwin M. M. Ortega ◽  
Dalton de Souza ◽  
...  

2018 ◽  
Vol 2 (2) ◽  
pp. 96-107
Author(s):  
Freddy Wangke

The purpose of the study was to determine the effect of increasing expenditure and increasing the minimum wage of the government in the simultaneous model of the industrial sector of DKI Jakarta province. The estimation model in the simultaneous model of the industrial sector of DKI province uses the 2 SLS (Two-Stage Least Squares) method. The simulation results of a 10% increase in the expenditure of the provincial government of DKI has resulted in an increase of investment of 4.72%, production growth of 0.19%, employment of 0.17%, an increase in production costs by 0.24%, and company profits increased by 0.10%. On the other hand, the simulation results of a 10% increase in the provincial minimum wage has resulted in a decrease in labor absorption by 0.55%, a decrease in production in the industrial sector has resulted in 0.21%, a decrease in investment by 0.07%, and a decrease in production costs by 0.04%.


2020 ◽  
Vol 2 (1) ◽  
pp. 14-20
Author(s):  
Rahmawati Pane ◽  
Sutarman

A heteroskedastic semiparametric regression model consists of two main components, i.e. parametric component and nonparametric component. The model assumes that any data (x̰ i′ , t i , y i ) follows y i = x̰ i′ β̰+ f(t i ) + σ i ε i , where i = 1,2, … , n , x̰ i′ = (1, x i1 , x i2 , … , x ir ) and t i is the predictor variable. Parameter vector β̰ = (β 1 , β 2 , … , β r ) ′ ∈ ℜ r is unknown and f(t i ) is also unknown and is assumed to be in interval of C[0,π] . Random error ε i is independent on zero mean and varianceσ 2 . Estimation of the heteroskedastic semiparametric regression model was conducted to evaluate the parametric and nonparametric components. The nonparametric component f(t i ) regression was approximated by Fourier series F(t) = bt + 12 α 0 + ∑ α k 𝑐 𝑜𝑠 kt Kk=1 . The estimation was obtained by means of Weighted Penalized Least Square (WPLS): min f∈C(0,π) {n −1 (y̰− Xβ̰−f̰) ′ W −1 (y̰− Xβ̰− f̰) + λ ∫ 2π [f ′′ (t)] 2 dt π0 } . The WPLS solution provided nonparametric component f̰̂ λ (t) = M(λ)y̰ ∗ for a matrix M(λ) and parametric component β̰̂ = [X ′ T(λ)X] −1 X ′ T(λ)y̰


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