Integrated data envelopment analysis: Linear vs. nonlinear model

Mahdiloo, Mahdi; Toloo, Mehdi; Duong, Thach-Thao; Saen, Reza Farzipoor; Tatham, Peter

doi:10.1016/j.ejor.2018.01.008

Cited by 13 publications

(4 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This test, classified as the Lagrange Multiplier (LM) Test, comes from the field of neural network models and is considered a powerful tool for identifying nonlinearities in data. According to Mahdiloo et al (2018), although the Terasvirta Test and the White Test originate from the neural network world and aim to identify nonlinearities in data, they differ in their approach [11]. The Terasvirta Test uses Taylor Series to analyze certain neural network parameters, whereas the White Test relies on random selection of these parameters [12].…”

Section: A Nonlinearity Testmentioning

confidence: 99%

Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach

Sifa,

Galena,

Mardianto

et al. 2024

Inferensi

View full text Add to dashboard Cite

Fluctuations in the rupiah exchange rate against the United States Dollar from 2020 to early 2024 have been analyzed using classical and modern time series approaches. In this study, the classical time series approach based on Gaussian Kernel successfully provides predictions with an RMSE value of 57.5722 and a MAPE of 0.29%. Meanwhile, the modern approach with RBF Kernel SVR shows an RMSE value of 74.9201 and a MAPE of 0.41%. The results of the model performance comparison show the superiority of the classical approach with the Gaussian Kernel in predicting the rupiah exchange rate against the US Dollar as an impact of the Federal Funds Rate (FFR) policy. Therefore, it is recommended to use the classical time series method based on the Gaussian Kernel in dealing with the impact of the FFR policy to improve the accuracy of predicting the Rupiah exchange rate against the United States Dollar. This research supports the achievement of the 8th Sustainable Development Goals (SDGs) related to economic and social matters while providing a better understanding of currency exchange rate fluctuations and providing recommendations that can help in managing economic risks related to global monetary policy.

show abstract

Section: A Nonlinearity Testmentioning

confidence: 99%

Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach

Sifa,

Galena,

Mardianto

et al. 2024

Inferensi

View full text Add to dashboard Cite

show abstract

“…This test is almost same as white test that is both using a neural network model. The difference is that in terravirta test the parameter values of the neural network model are based on taylor expansion [5] while the white test is randomly selected.…”

Section: Terasvirta Testmentioning

confidence: 99%

“…Nonlinearity tests have been extensively developed such as Ramsey test [3], White test [4] and Terasvirta test [5]. Ramsey test is the most common and easy to use in detecting nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

The Performance of Ramsey Test, White Test and Terasvirta Test in Detecting Nonlinearity

2020

View full text Add to dashboard Cite

Abstrak-The objective of this research is to compare Ramsey test, White test and Terasvirta test in the identification of nonlinearity. Ramsey test is a test based on the regression specification error test. While White test and Terasvirta test are based on neural network models. The difference between White test and Terasvirta test is in determining its weight, White test based on random sampling, while Terasvirta test based on Taylor expansion. Simulation studies are carried out with various scenarios in each test by generating linear models, linear models with outliers and nonlinear models. The results of the simulation study showed that Terasvirta test had better power than Ramsey test and White test in detecting nonlinearity. Terasvirta test is also more sensitive to the presence of outliers in linear models.

show abstract

“…There are two main methods for calculating efficiency: the parametric method and the nonparametric method. The parametric method is typified by the stochastic frontier analysis (SFA) method introduced by Aigner et al (Lin and Long 2015), while the nonparametric method is typified by the data envelopment analysis (DEA) method introduced by Farel et al (Mahdiloo et al 2018), with most later studies based upon variants of the two. The SFA method requires specification of a functional form, and whether or not the results of its calculations are reliable is determined by the degree of matching between the function and real conditions.…”

Section: Introductionmentioning

confidence: 99%

Green economic efficiency in the Yangtze River Delta: spatiotemporal evolution and influencing factors

Wang

Liang

2019

Ecosyst Health Sustain

View full text Add to dashboard Cite

Based on economic-social-resource-environment perspective, which people's welfare was considered compared with the traditional perspective, using SSU and PP model, spatial analysis method, spatial econometric model to study green economy efficiency (GRE) of 26 Cities in the Yangtze River Delta from 2005 to 2015. The results show the following: Corrected GRE is markedly lower than conventional efficiency; Stage characteristics are obvious of GRE. An overall spatial pattern has emerged of lower efficiency in the east and higher efficiency in the west, and exist clear signs of spatial agglomeration. The spatial Dubin model has abetter fitting effect. The biggest direct effect on local green economic efficiency and spatial spillover effects on nearby areas is proportion of tertiary industry.

show abstract

Integrated data envelopment analysis: Linear vs. nonlinear model

Cited by 13 publications

References 50 publications

Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach

Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach

The Performance of Ramsey Test, White Test and Terasvirta Test in Detecting Nonlinearity

Green economic efficiency in the Yangtze River Delta: spatiotemporal evolution and influencing factors

Contact Info

Product

Resources

About