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Forecast the Gross Value Added in Construction Sector of Bulgaria with SARIMA Model

Author

Listed:
  • Plamen Yankov

    (University of Economics - Varna, Varna, Bulgaria)

  • Julian Vasilev

    (University of Economics - Varna, Varna, Bulgaria)

  • Pavel Petrov

    (University of Economics - Varna, Varna, Bulgaria)

  • Liliya Mileva

    (University of Economics - Varna, Varna, Bulgaria)

  • Svetlana Todorova

    (University of Economics - Varna, Varna, Bulgaria)

Abstract

Construction is an important sector for national economies because it contributes with relatively high gross value added (GVA). The purpose of this study is to forecast GVA in a short-term period based on seasonal ARIMA models. Quarterly time series data from 2010 to 2020 are used for modelling and forecasting. Stationarity is achieved after differencing both - seasonal and non-seasonal component of the data. Based on autocorrelation plots SARIMA model is selected as most accurate. Ljung-Box test for the absence of autocorrelation confirms that the model is adequate and suitable to forecast. The current study is conducted as part of the research project BG05M2OP001-1.002-0002-C02 "Digitalization of Economy in a Big Data Environment".

Suggested Citation

  • Plamen Yankov & Julian Vasilev & Pavel Petrov & Liliya Mileva & Svetlana Todorova, 2021. "Forecast the Gross Value Added in Construction Sector of Bulgaria with SARIMA Model," Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, Union of Scientists - Varna, Economic Sciences Section, vol. 10(1), pages 45-54, April.
    • Handle: RePEc:vra:journl:v:10:y:2021:i:1:p:45-54
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    More about this item

    Keywords

    SARIMA; gross value added; forecast; construction;
    All these keywords.

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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