本研究以期望最大化變數選取（expectation-maximization variable selection， EMVS）架構為基礎，提出三種貝氏變數選取方法，用以辨識矩陣自迴歸（matrix autoregressive， MAR）模型之係數矩陣中具有影響力的係數。所提出的方法分別為 EMVS+、EMVS$^{*}$ 與自適應期望最大化變數選取（adaptive EMVS，簡記為 AEMVS），三者採用不同的係數選取與估計策略。本研究透過廣泛的模擬實驗，評估各方法在變數選取與預測方面的表現。此外，本文亦將這些方法應用於 OECD 資料庫中 1996 年至 2019 年的總體經濟季資料，以及 2022 年至 2024 年經同步處理的多國金融報酬日資料。數值結果顯示，三種所提出的方法在不同的 MAR 模型設定下各自展現不同的優勢，並可作為稀疏 MAR 模型估計之穩健替代方法。;This study proposes three Bayesian variable selection procedures for identifying influential coefficients in the coefficient matrices of matrix autoregressive (MAR) models based on the expectation-maximization variable selection (EMVS) framework. The proposed procedures， namely EMVS+， EMVS$^*$， and AEMVS (adaptive EMVS)， differ in their strategies for coefficient selection and estimation. Extensive simulation studies are conducted to evaluate their variable-selection and predictive performance. The methods are further applied to quarterly macroeconomic data obtained from the OECD database from 1996 to 2019 and synchronized multi-country financial daily return data from 2022 to 2024. The numerical results show that the three proposed procedures exhibit different strengths across different MAR settings and provide robust alternatives for sparse MAR estimation.
