IDEAS home Printed from https://ideas.repec.org/a/rsr/supplm/v64y2016i10p33-40.html
   My bibliography  Save this article

Statistical-econometric model used in performance analysis of the company

Author

Listed:
  • Madalina-Gabriela ANGHEL

    (“ARTIFEX” University of Bucharest)

  • Luminita Madalina CALOTA

    (“ARTIFEX” University of Bucharest)

Abstract

The objective of this article consists of analyzing the performances of a company by using the statistical-econometric model in order to obtain estimation of financial results. In this regard the correlation between the turnover and the average number of employees was analysed by applying the econometric model of simple regression. The final model was checked using specific tests, resulting that it observes the hypothesis of linear regression and that the results can be considered significant for the analysed data series. Also, in the static and dynamic analysis of the company’s evolution a series of statistical-mathematical could be applied methods, such as chronological series or index method. Based on the registered values in five consecutive timelines, there were calculated specific indicators of the chronological series like absolute changes of the studied characteristic, dynamic indexes, growth rhythm with fixed and mobile base or the average value of the studied characteristic.

Suggested Citation

  • Madalina-Gabriela ANGHEL & Luminita Madalina CALOTA, 2016. "Statistical-econometric model used in performance analysis of the company," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(10), pages 33-40, October.
  • Handle: RePEc:rsr:supplm:v:64:y:2016:i:10:p:33-40
    as

    Download full text from publisher

    File URL: http://www.revistadestatistica.ro/supliment/wp-content/uploads/2016/10/RRSS_10_2016_A02_en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Omesh Kini & Shehzad Mian & Michael Rebello & Anand Venkateswaran, 2009. "On the Structure of Analyst Research Portfolios and Forecast Accuracy," Journal of Accounting Research, Wiley Blackwell, vol. 47(4), pages 867-909, September.
    2. Constantin Anghelache & Mario G.R. Pagliacci & Constantin Mitrut, 2015. "Statistical-Econometric Models used in Economic Analysis," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 63(4), pages 9-15, April.
    3. James D. Hamilton & Daniel F. Waggoner & Tao Zha, 2007. "Normalization in Econometrics," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 221-252.
    4. Madalina Gabriela ANGHEL, 2014. "The System of Financial Analysis Indicators Applying to the Activity Run by an Economic Agent," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 62(7), pages 75-83, July.
    5. Georgi N. Boshnakov & Bisher M. Iqelan, 2009. "Generation Of Time Series Models With Given Spectral Properties," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(3), pages 349-368, May.
    6. Francis X. Diebold & Lutz Kilian & Marc Nerlove, 2006. "Time Series Analysis," PIER Working Paper Archive 06-019, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
      • Diebold, F.X. & Kilian, L. & Nerlove, Marc, 2006. "Time Series Analysis," Working Papers 28556, University of Maryland, Department of Agricultural and Resource Economics.
    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. Radu Titus MARINESCU & Aurelian DIACONU & Alexandru BADIU & Alexandru BADIU, 2016. "Analyzing the correlation between GDP and import using a statistical-econometric model," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(10), pages 98-102, October.
    2. Perron, Pierre & Wada, Tatsuma, 2016. "Measuring business cycles with structural breaks and outliers: Applications to international data," Research in Economics, Elsevier, vol. 70(2), pages 281-303.
    3. Thomas Sargent & Noah Williams & Tao Zha, 2006. "Shocks and Government Beliefs: The Rise and Fall of American Inflation," American Economic Review, American Economic Association, vol. 96(4), pages 1193-1224, September.
    4. Hope, Ole-Kristian & Su, Xijiang, 2021. "Peer-level analyst transitions," Journal of Corporate Finance, Elsevier, vol. 70(C).
    5. Ozcan, Mustafa, 2018. "The role of renewables in increasing Turkey's self-sufficiency in electrical energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2629-2639.
    6. Daniel Grabowski & Anna Staszewska-Bystrova & Peter Winker, 2020. "Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 5-32, March.
    7. Leeb, Hannes & Pötscher, Benedikt M., 2008. "Can One Estimate The Unconditional Distribution Of Post-Model-Selection Estimators?," Econometric Theory, Cambridge University Press, vol. 24(2), pages 338-376, April.
    8. Yi Dong & Nan Hu & Xu Li & Ling Liu, 2017. "Analyst Firm Coverage and Forecast Accuracy: The Effect of Regulation Fair Disclosure," Abacus, Accounting Foundation, University of Sydney, vol. 53(4), pages 450-484, December.
    9. Kaufmann, Sylvia, 2015. "K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation?," Journal of Econometrics, Elsevier, vol. 187(1), pages 82-94.
    10. Christopher A. Sims & Tao Zha, 2004. "MCMC method for Markov mixture simultaneous-equation models: a note," FRB Atlanta Working Paper 2004-15, Federal Reserve Bank of Atlanta.
    11. Galanti, Sébastien & Leroy, Aurélien & Vaubourg, Anne-Gaël, 2022. "Investment and access to external finance in Europe: Does analyst coverage matter?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    12. Xing, Cunyu & Li, Yanglei, 2019. "The cost of speaking in two tongues," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 465-475.
    13. Rakos (Boca) Ileana-Sorina & Solomon Alina-Georgiana & Stolojescu Bogdan Nicolae & Muntean Emil, 2023. "The Results Analysis Of An Economic Entity Of Water Distribution," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 178-183, December.
    14. Peter M. Summers & Penelope A. Smith, 2005. "How well do Markov switching models describe actual business cycles? The case of synchronization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 253-274.
    15. Bey, M. & Hamidat, A. & Benyoucef, B. & Nacer, T., 2016. "Viability study of the use of grid connected photovoltaic system in agriculture: Case of Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 333-345.
    16. Perron, Pierre & Wada, Tatsuma, 2009. "Let's take a break: Trends and cycles in US real GDP," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 749-765, September.
    17. Xiaomeng Chen & Meiting Lu & Yaowen Shan & Yizhou Zhang, 2021. "Australian evidence on analysts' cash flow forecasts: issuance, accuracy and usefulness," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(1), pages 3-50, March.
    18. Huang, Allen H. & Lin, An-Ping & Zang, Amy Y., 2022. "Cross-industry information sharing among colleagues and analyst research," Journal of Accounting and Economics, Elsevier, vol. 74(1).
    19. Xiaoshan Chen & Ronald Macdonald, 2012. "Realized and Optimal Monetary Policy Rules in an Estimated Markov-Switching DSGE Model of the United Kingdom," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(6), pages 1091-1116, September.
    20. Zheng, Yuhua & Luo, Dongkun, 2013. "Industrial structure and oil consumption growth path of China: Empirical evidence," Energy, Elsevier, vol. 57(C), pages 336-343.

    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:rsr:supplm:v:64:y:2016:i:10:p:33-40. 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: Adrian Visoiu (email available below). General contact details of provider: https://edirc.repec.org/data/stagvro.html .

    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.