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A Forecasting Model for Thailand’s Unemployment Rate

Author

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  • Kanlapat Mahipan
  • Nipaporn Chutiman
  • Bungon Kumphon

Abstract

This study deals with two approaches—viz. via Box-Jenkins and artificial neuron network to forecast the unemployment rate in Thailand. The Box-Jenkins approach proves more efficient to estimate the unemployment rate in Thailand, with less MAPE compared to the second model. The forecast values are consistent with the actual values and tend to decrease.

Suggested Citation

  • Kanlapat Mahipan & Nipaporn Chutiman & Bungon Kumphon, 2013. "A Forecasting Model for Thailand’s Unemployment Rate," Modern Applied Science, Canadian Center of Science and Education, vol. 7(7), pages 1-10, July.
    • Handle: RePEc:ibn:masjnl:v:7:y:2013:i:7:p:10
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    References listed on IDEAS

    as
    1. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
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    Cited by:

    1. Aaishah Jamaludin & Fadhilah Yusof & Ibrahim Kane, 2015. "Temporal Dynamics of Trend in Relative Humidity with RH-SARIMA Model," Modern Applied Science, Canadian Center of Science and Education, vol. 9(3), pages 281-281, March.

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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