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Out-of-sample forecasting of the Canadian unemployment rates using univariate models

Author

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  • Zameelah Rifkha Khan Jaffur
  • Noor-Ul-Hacq Sookia
  • Preethee Nunkoo Gonpot
  • Boopendra Seetanah

Abstract

This article investigates the out-of-sample forecasting performance of some linear and nonlinear univariate time series models on the monthly seasonally adjusted Canadian unemployment rates during the 1980–2013 period. The findings reveal that nonlinear time series models better capture the asymmetry present in the unemployment rate series at short and long forecast horizons.

Suggested Citation

  • Zameelah Rifkha Khan Jaffur & Noor-Ul-Hacq Sookia & Preethee Nunkoo Gonpot & Boopendra Seetanah, 2017. "Out-of-sample forecasting of the Canadian unemployment rates using univariate models," Applied Economics Letters, Taylor & Francis Journals, vol. 24(15), pages 1097-1101, September.
    • Handle: RePEc:taf:apeclt:v:24:y:2017:i:15:p:1097-1101
    DOI: 10.1080/13504851.2016.1257208
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    Cited by:

    1. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    2. Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Liviu Adrian Stoica, 2021. "Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023," Sustainability, MDPI, vol. 13(13), pages 1-22, June.

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