IDEAS home Printed from https://ideas.repec.org/a/cpn/umkdem/v17y2017p41-57.html
   My bibliography  Save this article

Microeconometric Analysis of Telecommunication Services Market with the Use of SARIMA Models

Author

Listed:
  • Pawel Kaczmarczyk

    (The State University of Applied Sciences in Plock)

Abstract

The paper presents the results of testing the effectiveness of the multi sectional model in the short-term forecasting of hourly demand for telephone services. The model was based on the integration of the linear regression model with dichotomous independent variables and the SARIMA model. The regression was used as a filter of modelled variability of the demand. The SARIMA was applied to model residual variability. The research shows that the proposed integration provides a greater possibility of approximation and prediction in comparison to the non-supported linear regression model. The results of the study provide support for operational planning of telecommunications operator.

Suggested Citation

  • Pawel Kaczmarczyk, 2017. "Microeconometric Analysis of Telecommunication Services Market with the Use of SARIMA Models," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 17, pages 41-57.
  • Handle: RePEc:cpn:umkdem:v:17:y:2017:p:41-57
    as

    Download full text from publisher

    File URL: https://apcz.umk.pl/DEM/article/view/DEM.2017.003/13737
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guy Melard, 1984. "Algorithm AS197: A fast algorithm for the exact likelihood of autoregressive-moving average models," ULB Institutional Repository 2013/13692, ULB -- Universite Libre de Bruxelles.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kaczmarczyk Paweł, 2021. "Econometric Modelling of Compound Cyclicality of using Telecommunication Services," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 25(2), pages 27-45, June.
    2. Kaczmarczyk Paweł, 2018. "Neural Network Application to Support Regression Model in Forecasting Single-Sectional Demand for Telecommunications Services," Folia Oeconomica Stetinensia, Sciendo, vol. 18(2), pages 159-177, December.

    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. André Klein & Guy Melard & Toufik Zahaf, 1998. "Computation of the exact information matrix of Gaussian dynamic regression time series models," ULB Institutional Repository 2013/13738, ULB -- Universite Libre de Bruxelles.
    2. Vicente Martínez, Eva, 2006. "Properties of two U.S. inflation measures (1985-2005)," DES - Working Papers. Statistics and Econometrics. WS ws066818, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Masoud, Alaa A., 2022. "On the Nile Fan's wave power potential and controlling factors integrating spectral and geostatistical techniques," Renewable Energy, Elsevier, vol. 196(C), pages 921-945.
    4. Guy Melard, 1994. "Modèles linéaires et non linéaires," ULB Institutional Repository 2013/13804, ULB -- Universite Libre de Bruxelles.
    5. Gómez, Víctor & Maravall, Agustín, 1993. "Computing missing values in time series," DES - Working Papers. Statistics and Econometrics. WS 3737, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Rajae Azrak & Guy Melard, 1998. "The exact quasi-likelihood of time dependent ARMA models," ULB Institutional Repository 2013/13740, ULB -- Universite Libre de Bruxelles.
    7. Rajae Azrak & Guy Melard, 1993. "Exact maximum likelihood estimation for extended ARIMA models," ULB Institutional Repository 2013/13802, ULB -- Universite Libre de Bruxelles.

    More about this item

    Keywords

    Decision Support System; dichotomous regression; SARIMA model; forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications

    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:cpn:umkdem:v:17:y:2017:p:41-57. 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: Miroslawa Buczynska (email available below). General contact details of provider: http://www.wydawnictwoumk.pl .

    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.