NON-EQUIDISTANT “BASIC FORM”-FOCUSED GREY VERHULST MODELS (NBFGVMS) FOR ILL-STRUCTURED SOCIO-ECONOMIC FORECASTING PROBLEMS

Mohammad Hashem-Nazari,Akbar Esfahanipour,S.M.T Fatemi Ghomi

doi:10.3846/16111699.2017.1337045

Mohammad Hashem-Nazari, Akbar Esfahanipour + Show 1 more

Open Access

https://doi.org/10.3846/16111699.2017.1337045

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Abstract

Multiple uncertainties complicate socio-economic forecasting problems, especially when relying on ill-conditioned limited data. Such problems are best addressed by grey prediction models such as Grey Verhulst Model (GVM). This paper resolves the incompatibility between GVM’s estimation and prediction by taking its basic form equation as the basis of both. The resultant “Basic Form”-focused GVM (BFGVM) is also further developed to create Direct Non-equidistant BFGVM (DNBFGVM) and, in turn, DNBFGVM with Recursive simulation (DNBFGVMR). Experimental analyses comprise 19 socio-economic time series with an emphasis on Iranian population, a low-frequency non-equidistant time series with remarkable strategic importance. Promisingly, the proposed DNBFGVM and DNBFGVMR provide accurate in-sample and out-of-sample socio-economic forecasts, show highly significant improvements over the best traditional GVM, and offer cost-effective intelligent support of decision-making. Final results suggest future trends of studied socio-economic time series. Specifically, they reveal Iranian population to grow even slower than anticipated, demanding an urgent consideration of policy-makers.

Highlights

Conventional techniques have become increasingly ineffective in forecasting complex socio-economic phenomena (Esfahanipour et al 2016)
Revising forecasting functions to form recursive simulation, we develop NBFGVMR and DNBFGVMR
Traditional Direct NGVM (DNGVM) is totally outperformed by proposed Direct Non-equidistant BFGVM (NBFGVM) (DNBFGVM) and DNBFGVMR in predicting the population

Summary

Introduction

Conventional techniques have become increasingly ineffective in forecasting complex socio-economic phenomena (Esfahanipour et al 2016). Socio-economic researches in less-developed countries often rely on insufficient observations, i.e. lowfrequency time series as well as irregular data collection, i.e. non-equidistant time series. Such ill-structured forecasting problems are best addressed by grey prediction. A major drawback in grey prediction is inherent incompatibility between continuous prediction (simulation) and discrete estimation. It builds on basic form equation to equip GVM with cohesive discrete forecasting procedure and to establish “Basic Form”-focused GVM (BFGVM). Considering irregularities prevalent in socio-economic time series, we create Non-equidistant BFGVM (NBFGVM). Revising forecasting functions to form recursive simulation, we develop NBFGVMR and DNBFGVMR

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