IDEAS home Printed from https://ideas.repec.org/a/vrs/stintr/v20y2019i2p49-67n5.html
   My bibliography  Save this article

A Comparative Analysis Of Economic Efficiency Of Medium-Sized Manufacturing Enterprises In Districts Of Wielkopolska Province Using The Hybrid Approach With Metric And Interval-Valued Data

Author

Listed:
  • Dehnel Grażyna

    (Poznan University of Economics and Business, Department of Statistics, Poznań, Poland .)

  • Walesiak Marek

    (Wroclaw University of Economics, Department of Econometrics and Computer Science, Jelenia Góra. Poland)

Abstract

The article describes a hybrid approach to evaluating economic efficiency of medium-sized manufacturing enterprises (employing from 50 to 249 people) in districts of Wielkopolska province, using metric and interval-valued data. The hybrid approach combines multidimensional scaling with linear ordering. In the first step, multidimensional scaling is applied to obtain a visual representation of objects in a two-dimensional space. In the next step, a set of objects is ordered linearly based on the distance from the pattern (ideal) object. This approach provides new possibilities for interpreting linearly ordered results of a set of objects. Interval-valued variables characterise the objects of interests more accurately than metric data do. Metric data are atomic, i.e. an observation of each variable is expressed as a single real number. In contrast, an observation of each interval-valued variable is expressed as an interval. The analysis was based on data prepared in a two-stage process. First, a data set of observations was obtained for metric variables describing economic efficiency of medium-sized manufacturing enterprises. These unit-level data were aggregated at district level (LAU 1) and turned into two types of data: metric and interval-valued data. In the analysis of interval-valued data, two approaches are used: symbolic-to-classic, symbolic-to-symbolic. The article describes a comparative analysis of results of the assessment of economic efficiency based on metric and interval-valued data (the results of two approaches). The calculations were made with scripts prepared in the R environment.

Suggested Citation

  • Dehnel Grażyna & Walesiak Marek, 2019. "A Comparative Analysis Of Economic Efficiency Of Medium-Sized Manufacturing Enterprises In Districts Of Wielkopolska Province Using The Hybrid Approach With Metric And Interval-Valued Data," Statistics in Transition New Series, Polish Statistical Association, vol. 20(2), pages 49-67, June.
    • Handle: RePEc:vrs:stintr:v:20:y:2019:i:2:p:49-67:n:5
    DOI: 10.21307/stattrans-2019-014
    as

    Download full text from publisher

    File URL: https://doi.org/10.21307/stattrans-2019-014
    Download Restriction: no
    File URL: https://libkey.io/10.21307/stattrans-2019-014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Grażyna Dehnel, 2015. "Robust regression in monthly business survey," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(1), pages 137-152, May.
    2. Marek Walesiak & Andrzej Dudek, 2017. "Selecting The Optimal Multidimensional Scaling Procedure For Metric Data With R Environment," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 521-540, September.
    3. Groenen, P.J.F. & Winsberg, S. & Rodriguez, O. & Diday, E., 2006. "I-Scal: Multidimensional scaling of interval dissimilarities," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 360-378, November.
    4. Grażyna Dehnel, 2015. "Robust Regression In Monthly Business Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 16(1), pages 137-152, March.
    5. Federica Gioia & Carlo Lauro, 2006. "Principal component analysis on interval data," Computational Statistics, Springer, vol. 21(2), pages 343-363, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Grażyna Dehnel & Marek Walesiak, 2019. "A Comparative Analysis Of Economic Efficiency Of Medium-Sized Manufacturing Enterprises In Districts Of Wielkopolska Province Using The Hybrid Approach With Metric And Interval-Valued Data," Statistics in Transition New Series, Polish Statistical Association, vol. 20(2), pages 49-68, June.
    2. Drago, Carlo & Gatto, Andrea, 2022. "Policy, regulation effectiveness, and sustainability in the energy sector: A worldwide interval-based composite indicator," Energy Policy, Elsevier, vol. 167(C).
    3. Groenen, P.J.F. & Terada, Y., 2015. "Symbolic Multidimensional Scaling," Econometric Institute Research Papers EI 2015-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. J. Le-Rademacher & L. Billard, 2013. "Principal component histograms from interval-valued observations," Computational Statistics, Springer, vol. 28(5), pages 2117-2138, October.
    5. Andrzej Młodak, 2014. "On the construction of an aggregated measure of the development of interval data," Computational Statistics, Springer, vol. 29(5), pages 895-929, October.
    6. Lima Neto, Eufrásio de A. & de Carvalho, Francisco de A.T., 2010. "Constrained linear regression models for symbolic interval-valued variables," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 333-347, February.
    7. Marek Walesiak & Grażyna Dehnel, 2023. "A Measurement of Social Cohesion in Poland’s NUTS2 Regions in the Period 2010–2019 by Applying Dynamic Relative Taxonomy to Interval-Valued Data," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    8. Huiwen Wang & Liying Shangguan & Rong Guan & Lynne Billard, 2015. "Principal component analysis for compositional data vectors," Computational Statistics, Springer, vol. 30(4), pages 1079-1096, December.
    9. Groenen, P.J.F. & Winsberg, S., 2006. "3WaySym-Scal: three-way symbolic multidimensional scaling," Econometric Institute Research Papers EI 2006-49, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt's exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759, July.
    11. Yan Sun & Guanghua Lian & Zudi Lu & Jennifer Loveland & Isaac Blackhurst, 2020. "Modeling the Variance of Return Intervals Toward Volatility Prediction," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 492-519, July.
    12. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt’s exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759.
    13. Pełka Marcin, 2019. "Analysis of Happiness in EU Countries Using the Multi-Model Classification based on Models of Symbolic Data," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 15-25, September.
    14. Meiling Chen & Huiwen Wang & Zhongfeng Qin, 2015. "Principal component analysis for probabilistic symbolic data: a more generic and accurate algorithm," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(1), pages 59-79, March.
    15. Roy, Falguni & K. Gupta, Dharmendra., 2018. "Sufficient regularity conditions for complex interval matrices and approximations of eigenvalues sets," Applied Mathematics and Computation, Elsevier, vol. 317(C), pages 193-209.
    16. Liu, Bingsheng & Shen, Yinghua & Zhang, Wei & Chen, Xiaohong & Wang, Xueqing, 2015. "An interval-valued intuitionistic fuzzy principal component analysis model-based method for complex multi-attribute large-group decision-making," European Journal of Operational Research, Elsevier, vol. 245(1), pages 209-225.
    17. Paola Zuccolotto, 2012. "Principal component analysis with interval imputed missing values," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 1-23, January.
    18. Marek Walesiak & Grażyna Dehnel, 2020. "The Measurement of Social Cohesion at Province Level in Poland Using Metric and Interval-Valued Data," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    19. Michael Greenacre & Patrick J. F Groenen & Trevor Hastie & Alfonso Iodice d’Enza & Angelos Markos & Elena Tuzhilina, 2023. "Principal component analysis," Economics Working Papers 1856, Department of Economics and Business, Universitat Pompeu Fabra.
    20. Pełka Marcin, 2019. "Assessment of the Development of the European Oecd Countries with the Application of Linear Ordering and Ensemble Clustering of Symbolic Data," Folia Oeconomica Stetinensia, Sciendo, vol. 19(2), pages 117-133, December.

    More about this item

    Keywords

    medium-sized enterprise; metric data; interval-valued data; multidimensional scaling; composite measures; C38; C43; C63; C88; R12;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:stintr:v:20:y:2019:i:2:p:49-67:n:5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.