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Trans. Planning Journal
| Title | Estimating Parameters and Commercial Performance by Using Bayesian Stochastic Frontier Analysis Method |
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| Author | Erwin T. J. Lin |
| Summary | The most commonly applied commercial performance evaluation methods comprise the data envelopment analysis (DEA) method and the stochastic frontier analysis (SFA) method. Of these methods, the SFA method sequentially requires the researcher to establish functional forms, calibrate and estimate relevant parameters using the maximum likelihood (ML) method, and estimate the efficiency values of the various businesses. However, the estimation of parameters using the ML method often yields incorrect signs, which consequently violates the regularity conditions and results in convoluted conclusions. To rectify this drawback, this study endeavors to employ an alternative Bayesian SFA method to calibrate and estimate relevant parameters and efficiency values of the businesses. The advantage of Bayesian methods is that conditions are restricted to the SFA model. This facilitates the calibration and estimation results to comply with economic regularity conditions. For the empirical research, this study collected the operational data (2006 to 2008) of 24 railway companies located in countries that were members of the European Union. Subsequently, this study established output functions to estimate the operational performance of businesses. The results suggested that the proposed Bayesian SFA method successfully yielded results that were more satisfactory. This study further provided relevant conclusions and suggestions based on these results, which can be used as a reference for future research. |
| Vol. | 42 |
| No. | 2 |
| Page | 95 |
| Year | 2013 |
| Month | 6 |
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