DOI: https://doi.org/10.14710/medstat.14.1.56-67

UTILIZATION OF STUDENT’S T DISTRIBUTION TO HANDLE OUTLIERS IN TECHNICAL EFFICIENCY MEASUREMENT

*Rizky Zulkarnain orcid  -  BPS Statistics Indonesia, Indonesia
Anik Djuraidah  -  BPS Statistics Indonesia, Indonesia
I Made Sumertajaya  -  Department of Statistics, IPB University, Indonesia
Indahwati Indahwati  -  Department of Statistics, IPB University, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
Stochastic frontier analysis (SFA) is the favorite method for measuring technical efficiency. SFA decomposes the error term into noise and inefficiency components. The noise component is generally assumed to have a normal distribution, while the inefficiency component is assumed to have half normal distribution. However, in the presence of outliers, the normality assumption of noise is not sufficient and can produce implausible technical efficiency scores. This paper aims to explore the use of Student’s t distribution for handling outliers in technical efficiency measurement. The model was applied in paddy rice production in East Java. Output variable was the quantity of production, while the input variables were land, seed, fertilizer, labor and capital. To link the output and inputs, Cobb-Douglas or Translog production functions was chosen using likelihood ratio test, where the parameters were estimated using maximum simulated likelihood. Furthermore, the technical efficiency scores were calculated using Jondrow method. The results showed that Student’s t distribution for noise can reduce the outliers in technical efficiency scores. Student’s t distribution revised the extremely high technical efficiency scores downward and the extremely low technical efficiency scores upward. The performance of model was improved after the outliers were handled, indicated by smaller AIC value.
Fulltext View|Download
Keywords: Cobb-Douglas; maximum simulated likelihood; robust; productivity; SFA; Translog

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
AUTOREGRESSIVE FRACTIONAL INTEGRATED MOVING AVERAGE (ARFIMA) MODEL TO PREDICT COVID-19 PANDEMIC CASES IN INDONESIA ROBUST PORTFOLIO SELECTION WITH CLUSTERING BASED ON BUSINESS SECTOR OF STOCKS SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL More recent articles
Most cited articles
ANALISIS EFISIENSI BANK PERKREDITAN RAKYAT DI KOTA SEMARANG DENGAN PENDEKATAN DATA ENVOLEPMENT ANALYSIS Front-Matter ANALISIS PENGARUH LOKASI DAN KARAKTERISTIK KONSUMEN DALAM MEMILIH MINIMARKET DENGAN METODE REGRESI LOGISTIK DAN CART ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS More cited articles
  1. Fusco, E. (2017). New Developments in Frontier Models for Objective Assessments. Sapienza. University of Rome
  2. Heriqbaldi, U., Purwono, R., Haryanto, T., Primanthi, M.R. (2015). An Analysis of Technical Efficiency of Rice Production in Indonesia, Asian Soc. Sci., Vol. 11, No.3, pp 91–102
  3. Jondrow, J., Lovell, C.A.K., Materov, I.S., Schmidt, P. (1982). On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model, J. Econom., Vol. 19, pp 233–238
  4. Liu, J., Zhang, C., Hu, R., Zhu, X., Cai, J. (2019). Aging of Agricultural Labor Force and Technical Efficiency in Tea Production: Evidence from Meitan County, China, Sustainability, Vol. 11, No.22, pp 6246
  5. Mariyono, J. (2018). Decomposition Total Factor Productivity of Indonesian Rice Production, Econ. J. Emerg. Mark., Vol. 10, No.2, pp 121–127
  6. O'Donnell, C.J. (2018). Productivity and Efficiency Analysis: An Economic Approach to Measuring and Explaining Managerial Performance. New York. Springer
  7. Rasmussen, S. (2013). Production Economics: The Basic Theory of Production Optimisation, Second. ed. New York. Springer
  8. Ray, S. and Kumbhakar, S. (2015). Benchmarking for Performance Evaluation: A Production Frontier Approach. New Delhi. Springer India
  9. Septianto, H. and Widiharih, T. (2010). Analisis Efisiensi Bank Perkreditan Rakyat di Kota Semarang dengan Pendekatan Data Envelopment Analysis, Media Statistika., Vol. 3, No.1, pp 41–48
  10. Siagian, R.A. and Soetjipto, W. (2020). Cost Efficiency of Rice Farming in Indonesia: Stochastic Frontier Approach, Agricultural Socio-Economics Journal., Vol. 20, No.1, pp 7–14
  11. Sholikah, S. and Kadarmanto. (2020). The Analysis of Technical Efficiency of Inbred and Hybrid Lowland Rice Farming Business, SOCA J. Sos. Ekon. Pertan., Vol. 14, No.3, pp 381–397
  12. Train, K. (2009). Discrete Choice Methods with Simulation, Second. ed. Cambridge. Cambridge University Press
  13. Wheat, P. (2017). Scale, Quality and Efficiency in Road Maintenance: Evidence for English Local Authorities, Transp. Policy, Vol. 59, pp 46–53
  14. Wheat, P., Stead, A.D., Greene, W.H. (2019). Robust Stochastic Frontier Analysis: A Student’s t-Half Normal Model with Application to Highway Maintenance Costs in England, J. Product. Anal., Vol. 51, pp 21–38
  15. Zulkarnain, R. and Indahwati. (2021). Robust Stochastic Frontier Using Cauchy Distribution for Noise Component to Measure Efficiency of Rice Farming in East Java, J. Phys. Conf. Ser., Vol. 1863, pp 012031

Last update:

No citation recorded.

Last update: 2024-04-18 17:55:12

No citation recorded.