A Decision-Support Framework for Economic Growth Forecasting under Smart Finance: Integrating Dynamic GMM, Threshold Regression, and Machine Learning Optimization - Xiaoning Lan (Decision Making: Applications in Management and Engineering, 2025)
Forecasting economic growth in the context of smart finance necessitates decision-support systems that can synthesise diverse, high-frequency, and nonlinear financial indicators. This research introduces a hybrid decision-support frame-work that integrates Dynamic System GMM, threshold re-gression, and a machine learning optimisation procedure em-ploying Lasso–EM algorithms, aiming to improve both the precision and interpretability of macroeconomic forecasts. Employing panel data from Chinese provinces spanning 2010 to 2023, the proposed model addresses endogeneity con-cerns, identifies structural thresholds within the smart fi-nance–growth relationship, and performs rigorous variable selection to enhance decision-making efficacy. Empirical results indicate that smart finance substantially fosters eco-nomic growth; however, its effects display nonlinear thresh-old behaviour contingent on the extent of digital infrastruc-ture and the capacity for financial innovation. The incorpora-tion of Lasso–EM optimisation further reinforces predictive robustness, mitigating overfitting and stabilising model per-formance. Consequently, the framework provides an ad-vanced tool for policymakers and economic planners, facili-tating evidence-based resource allocation, early detection of economic fluctuations, and the optimisation of fiscal and financial strategies within smart finance contexts. This study contributes to decision science by linking econometric ap-proaches with machine learning optimisation, offering a scal-able methodology for economic forecasting and policy de-sign in digitalised economies.