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Forecasting unemployment rate in Poland with dynamic model averaging and internet searches

Author

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  • Krzysztof Drachal

Abstract

The aim of this research is to estimate dynamic model averaging (DMA) model for unemployment rate in Poland. One can find multiple potential factors influencing unemployment rate. They can be significantly affecting unemployment rate only in certain periods. Therefore, a method incorporating time-varying parameters as well as the model uncertainty itself seems desirable. Additional aim of this research is to incorporate the Google search data into the econometric model. In this research, DMA is not able to significantly beat ARIMA model in case of forecast accuracy. Despite DMA success in other fields, for unemployment forecasting, this method seems vulnerable.

Suggested Citation

  • Krzysztof Drachal, 2020. "Forecasting unemployment rate in Poland with dynamic model averaging and internet searches," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 23(4), pages 368-389.
    • Handle: RePEc:ids:gbusec:v:23:y:2020:i:4:p:368-389
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    Citations

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    Cited by:

    1. Taşkın DİRSEHAN & Jörg HENSELER, 2023. "Modeling indices using partial least squares: How to determine the optimum weights?," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 521-535, December.
    2. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    3. Khuderchuluun Batsukh & Nicolas Groshenny & Naveed Javed, 2025. "Monetary policy transmission and household indebtedness in Australia," TEPP Working Paper 2025-02, TEPP.

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