Preview

Voprosy statistiki

Advanced search

Clustering entrepreneurial assessments of industry events in the small commercial business

https://doi.org/10.34023/2313-6383-2016-0-1-26-37

Abstract

The paper presents an analytical aspect of business surveys data processing, which allows highlighting key points in the dynamics of small retail businesses economic development in various phases of business cycle (case study: retail trade). In the introduction in addition to calculating balance characteristics and composite indicator of business conditions the authors substantiate the necessity to implement methodological approach to studying behavioral modals of economic entities that are attributable to small retail enterprises, based on the statistical distribution of respondents’ answers. In reviewing cluster analysis individual data for clustering is suggested as variables that are an entrepreneurial assesses the actual and expected trends in real time. Features of the application technique of cluster analysis in determining the different «behavioral patterns» can be classified as individual responses of economic agents at different stages of the business cycle. A more thorough examination of this information may be useful in analyses of various operational indicators of organizations activity. This aspect is essential for investigating small business aggregate behavior in specific phases of business cycle, when it is necessary to detail business reaction with respect to actual or expected economic events.

About the Authors

L. A. Kitrar
National Research University Higher School of Economics
Russian Federation


I. S. Lola
National Research University Higher School of Economics
Russian Federation


References

1. Айвазян С.А., Мхитарян В.С. Прикладная статистика. Основы эконометрики: учеб. для студентов экон. специальностей вузов. В 2 т. Т. 2: Основы эконометрики. - 2-е изд., испр. - М.: ЮНИТИ, 2001. - 432 с.

2. Демидов О. Различные индексы прогнозирования экономической активности в России // Квантиль. 2008. № 5. С. 83-102.

3. Carlson J.A., Parkin J.M. Inflation expectations. Economica. 1975. No. 42. P. 123-138.

4. Crosilla L., Leproux L. Leading indicators on construction and retail trade sectors based on ISAE survey data // Journal of Business Cycle Measurement and Analysis. 2008. Iss. 1. Р. 97-123.

5. Crosilla L., Malgarini M. Behavioural models for manufacturing firms: an analysis based on ISAE survey data. URL: http ://ec.europa. eu /economy_finance/db_indicators / surveys / documents/workshops/2010/ec_meeting/ crosilla_ malgarini_isae.pdf (дата обращения: 10.03.2015).

6. Mirkin B.G. Individual approximate clusters: Methods, properties, applications // Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Issue 8170: Lecture Notes in Artificial Intelligence. Heidelberg: Springer, 2013. P. 26-37.

7. Mitchell J., Smith R.J., Weale M.R. Aggregate versus disaggregate survey-based indicators of economic activity. National Institute of Economic and Social Research. -London, 2002. № 194. - 33 р. - Series: National Institute discussion paper.

8. Pesaran M.H., Weale M.R. Survey expectations. CESifo working paper No. 1599. 2005. URL: http://hdl.handle.net/10419/19063 (дата обращения: 20.02.2015).

9. Proietti T., Frale C. New proposals for the quantification of qualitative survey data. URL: http://ssrn.com/ab-stract=967411 (дата обращения: 03.03.2015).


Review

For citations:


Kitrar L.A., Lola I.S. Clustering entrepreneurial assessments of industry events in the small commercial business. Voprosy statistiki. 2016;(1):26-37. (In Russ.) https://doi.org/10.34023/2313-6383-2016-0-1-26-37

Views: 210


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2313-6383 (Print)
ISSN 2658-5499 (Online)