Mixed-frequency data-driven forecasting the important economies' performance in a smart city: a novel RUMIDAS-SVR model

Abstract

Purpose

The purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.

Design/methodology/approach

This study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.

Findings

The empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.

Practical implications

Smart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.

Originality/value

This study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.

Citation

Wang, W., Zhang, Z., Wang, L., Zhang, X. and Zhang, Z. (2022), "Mixed-frequency data-driven forecasting the important economies' performance in a smart city: a novel RUMIDAS-SVR model", Industrial Management & Data Systems, Vol. 122 No. 10, pp. 2175-2198. https://doi.org/10.1108/IMDS-01-2022-0014