On the fit and forecasting performance of grey prediction models for China’s labor formation

Abstract

Motivated by the high probability of inconsistent and insufficient information from official statistics in China, this research paper conducted an out-of-sample forecasting competition between grey prediction and straightforward extrapolation for quantity and quality of laborers in China under the assumption that the performance of grey prediction is superior in dealing with uncertain and insufficient data inputs. In line with previous comparative studies on time-series forecasting techniques, the purpose of this study was to verify that the GM(1,1), GM(1,1) rolling and FGM(1,1) models derived from grey system theory would provide forecasts that are at least as accurate as the straightforward extrapolation approach for China’s labor variables. The findings revealed that the forecasting efficiency of GM(1,1) and GM(1,1) rolling models were superior to straightforward extrapolation and FGM(1,1). The results can offer valuable insights and provide a basis for further research in model building for short-term estimation under the circumstances of data incompleteness or information inconsistency.