Unemployment has always been a very focused issue causing a nation as a whole to lose its economic and financial contribution. Unemployment rate prediction of a country is a crucial factor for the country’s economic and financial growth planning and a challenging job for policymakers. Traditional stochastic time series models, as well as modern nonlinear time series techniques, were employed for unemployment rate forecasting previously. These macroeconomic data sets are mostly nonstationary and nonlinear in nature. Thus, it is atypical to assume that an individual time series forecasting model can generate a white noise error. This paper proposes an integrated approach based on linear and nonlinear models that can predict the unemployment rates more accurately. The proposed hybrid model of the unemployment rate can improve their forecasts by reflecting the unemployment rate’s asymmetry. The model’s applications are shown using seven unemployment rate data sets from various countries, namely, Canada, Germany, Japan, Netherlands, New Zealand, Sweden, and Switzerland. The results of computational tests are very promising in comparison with other conventional methods. The results for asymptotic stationarity of the proposed hybrid approach using Markov chains and nonlinear time series analysis techniques are given in this paper which guarantees that the proposed model cannot show ‘explosive’ behavior or growing variance over time.
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Chakraborty, T., Chakraborty, A.K., Biswas, M. et al. Unemployment Rate Forecasting: A Hybrid Approach. Comput Econ 57, 183–201 (2021). https://doi.org/10.1007/s10614-020-10040-2
- Unemployment rate
- ARIMA model
- Autoregressive neural networks
- Hybrid model
- Asymptotic stationarity