Unemployment Rate Forecasting: A Hybrid Approach

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Unemployment Rate Forecasting: A Hybrid Approach Tanujit Chakraborty1   · Ashis Kumar Chakraborty1 · Munmun Biswas2 · Sayak Banerjee3 · Shramana Bhattacharya3 Accepted: 10 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020

Abstract 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. Keywords  Unemployment rate · ARIMA model · Autoregressive neural networks · Hybrid model · Asymptotic stationarity

1 Introduction Economic indicators such as GDP and labor statistics are used by investors to forecast economic trends and decide on the appropriate investment policies. In particular, the unemployment rate for any country represents one of the most important * Tanujit Chakraborty [email protected] Extended author information available on the last page of the article

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economic indicators for financial market participants due to its correlation with the country’s business cycle and its influence on the monetary policy (Blanchard and Leigh 2013). Accurate forecasting of the unemployment rate is central to economic decision-making and the design of policy-making to recognize early the socio-economic problems so that reduction of the same can be planned. The study of unemployment rates and macroeconomic forecasting started blooming in the middle of the 1990s. Many time series models have been employed extensively for the prediction of macroeconomic variables, including unemployment. Previous studies on unemployment rate suggested an asymmetry in the unemployment rate data for various European countries (Milas and Rothman 2008). One of the primary time-series implications o