Dec 1, 2023
Exponential smoothing techniques are pivotal in forecasting within economic, financial, and operational management domains. The evolution from Holt's initial model to the Single Source of Error (SSOE) and subsequently the Multiple Source of Error (MSOE) frameworks reflects significant advancements in handling the dynamic aspects of time series data. Our research focuses on the MSOE model, specifically its application through Random Coefficient Markov Chain Monte Carlo (RC-MCMC) methods. This method leverages banded precision matrices to enhance the estimation efficiency of model parameters. Our simulations, alongside empirical applications using quarterly credit-to-GDP data from the Bank for International Settlements, demonstrate the RC-MCMC's superior accuracy in parameter estimation compared to the direct RC-SSPACE method. This study underscores the RC-MCMC's practical relevance and robustness in economic time series analysis.
The Fintech evolution has reshaped commercial banks in China, necessitating a study of its influence on their operational efficiency. Using data from 57 Chinese commercial banks between 2011 and 2020, operational efficiency was gauged via Data Envelopment Analysis (DEA) and further decomposed with the Malmquist index method. A Generalized Moment Estimation model (GMM) assessed Fintech's impact on efficiency. Findings indicated that technological advancements primarily boost bank efficiency. While Fintech aids in enhancing efficiency, its assimilation varies across different bank types.
