More is better than one: the impact of different numbers of input aggregators in technical efficiency estimation

Aldanondo, Ana M. and Casasnovas, Valero L. (2015): More is better than one: the impact of different numbers of input aggregators in technical efficiency estimation.

Abstract

The results of an experiment with simulated data show that combining inputs with different criteria (as cost, material inputs aggregates and other) increases the accuracy of the Data Envelopment Analysis (DEA) technical efficiency estimator in data sets with dimensionality problems. The positive impact of this approach surpasses that of reducing the number of variables, since replacement of the original inputs with an equal number of aggregates improves DEA performance in a wide range of cases.

• Item Type: MPRA Paper
• Language: English
• Keywords: Technical efficiency, Aggregation bias, Monte Carlo, DEA Estimator accuracy
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis; D - Microeconomics > D2 - Production and Organizations > D20 - General

References

Adler, N. and Golany, B. (2001) Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe, European Journal of Operational Research,132, 260-73.

Adler, N. and Yazhemsky, E. (2010) Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction, European Journal of Operational Research 202, 273-84.

Aldanondo, A. M. and Casasnovas; V. L. (2015) Input aggregation bias in technical efficiency with multiple criteria analysis, Applied Economic Letters, 22, 430-35.

Allen, R., Athanassopoulos, A., Dyson, R. G. and Thanassoulis, E. (1997) Weights restrictions and value judgements in data envelopment analysis: Evolution, development and future directions, Annals of Operations Research, 73, 13-34.

Allen, R. and Thanassoulis, E. (2004) Improving envelopment in data envelopment analysis, European Journal of Operational Research, 154, 363-79

Banker, R. D., Gadh, V. M. and Gorr, W. L. (1993) A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis, European Journal of Operational Research, 67, 332-43.

Banker, R. D. and Maindiratta, A. (1988) Nonparametric analysis of technical and allocative efficiencies in production, Econometrica, 56, 1315-32.

Charnes, A., Cooper, W. W. and Huang, Z. M. (1990) Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks, Journal of Econometrics, 46, 73-91.

Charnes, A., Cooper, W. W. and Rhodes, E. (1978) Measuring the efficiency of decision-making units, European Journal of Operational Research, 2, 429-44.

Färe, R., Grosskopf, S. and Zelenyuk, V. (2004) Aggregation bias and its bounds in measuring technical efficiency, Applied Economics Letters, 11, 657-60. Färe, R. and Zelenyuk, V. (2002) Input aggregation and technical efficiency, Applied Economics Letters, 9, 635-6.

Førsund, F. R. (2013) Weight restrictions in DEA: misplaced emphasis?, Journal of Productivity Analysis, 40, 271-83.

Olesen, O. B. and Petersen, N. C. (1996) Indicators of ill-conditioned data sets and model misspecification in data envelopment analysis: an extended facet approach, Management Science, 42, 205-19.

Podinovski, V. V and Thanassoulis, E. (2007). Improving discrimination in data envelopment analysis: some practical suggestions, Journal of Productivity Analysis, 28, 117-26.

Primont, D. (1993) Efficiency measures and input aggregation, in Mathematical Modelling in Economics. Essays in Honor of Wolfgang Eichhorn, Diewert, W. E., Spremann, K. and Stehling, F. (Eds), Springer, Berlin, pp. 288-94.

Simar, L. and Wilson, P. W. (2001) Testing restrictions in nonparametric efficiency models, Communications in Statistics: Simulation and Computation, 30, 159-84.

Simar, L. and Wilson, P. W. (2015) Statistical approaches for nonparametric frontier models: a guided tour, International Statistical Review, 83, 77-110.

Smith, P., (1997) Model misspecification in data envelopment analysis, Annals of Operations Research, 73, 233-52.

Tauer, L. W. (2001) Input aggregation and computed technical efficiency, Applied Economics Letters, 8, 295-7.

Varian, H. R. (1984) The nonparametric approach to production analysis, Econometrica, 52, 579-97.

Wilson, P. W. (2008). FEAR: a software package for frontier efficiency analysis with R. Socio-Economic Planning Sciences, 42, 247-54.

Yap, G. L. C., Ismail, W. R., and Isa, Z. (2013) An alternative approach to reduce dimensionality in data envelopment analysis, Journal of Modern Applied Statistical Methods, 12, 128-47.