Unemployment Rate Forecasting: A Hybrid Approach

19 Pages Posted: 16 Aug 2019 Last revised: 1 Jun 2020

See all articles by Tanujit Chakraborty

Tanujit Chakraborty

Indian Statistical Institute, Kolkata

Ashis Kumar Chakraborty

Indian Statistical Institute, Kolkata - Economic Research Unit

M Biswas

West Bengal State University

Sayak Banerjee

International Institute for Population Sciences (IIPS)

Shramana Bhattacharya

Deemed Universities of India - International Institute for Population Sciences

Date Written: August 15, 2019

Abstract

Unemployment has always been a very focused issue causing the 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 refecting 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, Neural Networks, Hybrid Model

JEL Classification: C22, C45, C53

Suggested Citation

Chakraborty, Tanujit and Chakraborty, Ashis Kumar and Biswas, Munmun and Banerjee, Sayak and Bhattacharya, Shramana, Unemployment Rate Forecasting: A Hybrid Approach (August 15, 2019). Available at SSRN: https://ssrn.com/abstract=3437820 or http://dx.doi.org/10.2139/ssrn.3437820

Tanujit Chakraborty (Contact Author)

Indian Statistical Institute, Kolkata ( email )

203 B.T. Road
Kolkata, West Bengal 700108
India

Ashis Kumar Chakraborty

Indian Statistical Institute, Kolkata - Economic Research Unit ( email )

205 B.T. Road Indian Statistical Institute
Economic Research Unit
Kolkata, WA
India

Munmun Biswas

West Bengal State University ( email )

Berunan Pukuria, Barasat
North 24 Parganas
Kolkata, West Bengal 700126
India

Sayak Banerjee

International Institute for Population Sciences (IIPS) ( email )

Govandi Station Road
mumbai, Maharashtra 400088
India

Shramana Bhattacharya

Deemed Universities of India - International Institute for Population Sciences ( email )

India

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