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2017 | vol. 17, iss. 1 | 7--19
Tytuł artykułu

Forecasting Randomly Distributed Zero-Inflated Time Series

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main aim of the article is to propose a forecasting procedure that could be useful in the case of randomly distributed zero-inflated time series. Many economic time series are randomly distributed, so it is not possible to estimate any kind of statistical or econometric models such as, for example, count data regression models. This is why in the article a new forecasting procedure based on the stochastic simulation is proposed. Before it is used, the randomness of the times series should be considered. The hypothesis stating the randomness of the times series with regard to both sales sequences or sales levels is verified. Moreover, in the article the ex post forecast error that could be computed also for a zero-inflated time series is proposed. All of the above mentioned parts were invented by the author. In the empirical example, the described procedure was applied to forecast the sales of products in a company located in the vicinity of Szczecin (Poland), so real data were analysed. The accuracy of the forecast was verified as well.(original abstract)
Rocznik
Strony
7--19
Opis fizyczny
Twórcy
  • University of Szczecin, Poland
Bibliografia
  • Asmussen, S., Glynn, P.W. (2007). Stochastic Simulation: Algorithms and Analysis. New York: Springer-Verlag.
  • Biswas, A., Song, P. (2009). Discrete-valued ARMA processes. Statistics and Probability Letters 79, 1841-1889.
  • Cameron, A.C., Trivedi, P.K. (2001). Essentials of Count Data Regression. In: A companion to theoretical econometrics (pp. 331-348). Oxford: Blackwell.
  • Cameron, A.C., Trivedi, P.K. (2005). Microeconometrics. Methods and Applications. Cambridge University Press.
  • Cameron, A.C., Trivedi. P.K. (1998). Regression Analysis of Count Data. Econometric Society Monograph No. 30. Cambridge University Press.
  • Domański, C. (1990). Testy statystyczne. Warszawa: PWE.
  • Hilbe, J.M. (2011). Negative Binomial Regression. Second Edition. Cambridge University Press.
  • Hilbe, J.M. (2014). Modeling count data. Cambridge University Press.
  • Taleb, N.N. (2001). Fooled by Randomness. The Hidden Role of Chance in the Markets and in Life. New York-London: Texere.
  • Shukur, G., Doszyń, M., Dmytrów, K. (2017). Comparison of the Effectiveness of Forecasts Obtained by Means of Selected Probability Functions with Respect to Forecast Error Distributions. Communications in Statistics. Simulation and Computation, 46 (5), 3667-3679. DOI: 10.1080/03610918.2015.1100734. [Crossref]
  • Winkelmann, R. (2008). Econometric Analysis of Count Data. Berlin, Heidelberg: Springer-Verlag.
  • Yang, M. (2012). Statistical models for count time series with excess zeros. University of Iowa.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171470283

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