A Hybrid Grey Bayesian Rolling Model for Forecasting Annual Rural Unemployment Rate in West Bengal
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Authors
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Pavithra V
Uttar Banga Krishi Viswavidyalaya, Cooch Behar
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Pradip Basak
Uttar Banga Krishi Viswavidyalaya, Cooch Behar
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Deb Sankar Gupta
Uttar Banga Krishi Viswavidyalaya, Cooch Behar
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Gobinda Mula
Uttar Banga Krishi Viswavidyalaya, Cooch Behar
Keywords:
Unemployment rate; Grey systems; Rolling grey model; Bayesian grey model; Forecasting; PLFS.Abstract
In this article, a reliable forecasting framework is developed for predicting the annual rural unemployment rate (UR) in West Bengal, India, where
time series data are scarce and noisy. Accurate unemployment forecasts are crucial for targeted policymaking, especially in rural regions facing
socio-economic vulnerabilities. The paper evaluates the performance of advanced grey system models in addressing data limitations and improving
predictive accuracy. The study utilizes annual rural UR estimates for various age groups and gender from 2017–18 to 2023–24, derived from the
Periodic Labour Force Survey (PLFS). Three forecasting models are developed: Grey Bayesian Model, Rolling Grey Model, and a hybrid Rolling
Grey Bayesian Model. The models are compared against traditional moving averages using Relative Mean Absolute Percentage Error (RMAPE).
Parameter estimation in the Bayesian models is implemented through MCMC simulations using WinBUGS. The results indicate that the Rolling Grey
and Rolling Grey Bayesian models consistently outperform traditional and standalone grey models, with RMAPE values as low as 8.40%. These
models show superior predictive capability, particularly for the 15–29 age group and female workforce categories. The dynamic updating mechanism
of the rolling models effectively handles short time series and chaotic labour market data. This study demonstrates the novel integration of grey
modelling with Bayesian inference and rolling mechanisms for rural labour market forecasting. It contributes a data-efficient, adaptive forecasting
approach suited for regions with limited labour statistics, offering practical value for policymakers in developing economies.
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