傳統大型總體計量模型多依賴經濟理論來設定變數間的相互影響，然而正是因為經濟理論本身架構十分嚴謹，反而使模型設定時更加繁雜且耗時。隨著電腦科技進步，從理論轉為資料驅動的研究開始蓬勃發展，擬合不足的問題也在演算法進步的狀況下影響日益減少。因此，本文主要目標在於透過資料驅動方式，簡化大型總體計量模型在結構方程式的繁雜過程，同時提升預測模型的準確度。本文採用具有神諭性質的 Adaptive Lasso 作為正則方法，以及蒙地卡羅交叉驗證來避免模型過度擬合。使用具有神諭性質的正則方法，可以隨著樣本增加真正的捕捉與被解釋變數相關的潛在因子，而蒙地卡羅交叉驗正則是無論變數是否定態的狀況下皆能使用，從而提升模型的一般化能力。不僅止於方法的改善，本文相較於傳統研究多數著重於金融市場變數，還另行加入大量產業變數以及產業股價資料於變數集合之中，透過神諭性質的正則方法，改以資料驅動方式篩選出實際上與被解釋變數預測高度相關的解釋變數。
實證結果可以發現，本文使用上述方法可以使名目變數的預測更加精準，而且能改善逐步迴歸法所造成樣本內擬合過度，樣本外卻存有較大偏誤，所造成的一般化能力不佳之狀況。本文提出一種資料驅動的模型設定方法，並非針對傳統經濟理論提出質疑，而是期望透過此一方式，協助經濟理論發現過往可能未被人們所查知的重要潛在變數，以提升日後計量模型改善預測能力的可能性。

Traditional large-scale macroeconometric models heavily rely on economic theories for model specification, which can be complex and time-consuming. This study aims to simplify the model setup process and improve prediction accuracy through a data-driven approach. The Adaptive Lasso method is employed as a regularization technique, while Monte Carlo cross-validation is used to prevent overfitting. Additionally, a significant number of industry variables and stock price data are included in the analysis to select relevant explanatory variables using a data-driven approach. Empirical results demonstrate that the proposed method improves prediction accuracy, avoids overfitting, and enhances generalization ability. It is important to note that this approach does not challenge economic theories but rather aims to identify important latent variables to enhance the predictive capability of econometric models.

Ball, R., Boatwright, B., Burns, T., Lobban, P., & Miller, G. (1975). The London Business School quarterly econometric model of the United Kingdom, w: GA Renton (red.), Modelling the economy.
Bautista, R. M. (1988). Macroeconomic models for east Asian developing countries. Asian‐Pacific Economic Literature, 2(2), 1-25.
Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213.
Bergmeir, C., Costantini, M., & Benítez, J. M. (2014). On the usefulness of cross-validation for directional forecast evaluation. Computational Statistics & Data Analysis, 76, 132-143.
Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70-83.
Capros, P., Karadeloglou, P., & Mentzas, G. (1990). An empirical assessment of macroeconometric and CGE approaches in policy modeling. Journal of Policy Modeling, 12(3), 557-585.
Cerqueira, V., Torgo, L., & Mozetič, I. (2020). Evaluating time series forecasting models: An empirical study on performance estimation methods. Machine Learning, 109, 1997-2028.
Chun, H., & Keleş, S. (2010). Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(1), 3-25.
Cooley, T. F. (1995). Frontiers of Business Cycle Research. Princeton University Press.
Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348-1360.
Friedman, J., Hastie, T., & Tibshirani, R. (2010). A note on the group lasso and a sparse group lasso. arXiv preprint arXiv:1001.0736.
Fromm, G., & Klein, L. R. (1965). The Brookings-SSRC quarterly econometric model of the United States: model properties. American Economic Review, 55(1/2), 348-361.
Hickman, B. (1972). Econometric models of cyclical behaviour. Studies in Income and Wealth(36).
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
Huang, J., Ma, S., & Zhang, C.-H. (2008). Adaptive Lasso for sparse high-dimensional regression models. Statistica Sinica, 1603-1618.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai:International Joint Conference on Artificial Intelligence, 2, 1137-1145.
Kydland, F. E., & Prescott, E. C. (1982). Time to build and aggregate fluctuations. Econometrica, 1345-1370.
Larson, S. C. (1931). The shrinkage of the coefficient of multiple correlation. Journal of Educational Psychology, 22(1), 45.
McCracken, M., & Ng, S. (2020). “FRED-QD:: A Quarterly Database for Macroeconomic Research,” NBER working paper, No. 26872.
McCracken, M. W., & Ng, S. (2016). “FRED-MD: A Monthly Database for Macroeconomic Research,” Journal of Business & Economic Statistics, 34(4), 574-589.
McQuarrie, A. D., & Tsai, C.-L. (1998). Regression and Time Series Model Selection. World Scientific.
Mendez-Civieta, A., Aguilera-Morillo, M. C., & Lillo, R. E. (2021). Adaptive sparse group LASSO in quantile regression. Advances in Data Analysis and Classification, 15(3), 547-573.
Mosteller, F., & Tukey, J. W. (1968). Data analysis, including statistics. Handbook of social psychology, 2, 80-203.
Poignard, B. (2020). Asymptotic theory of the adaptive Sparse Group Lasso. Annals of the Institute of Statistical Mathematics, 72, 297-328.
Racine, J. (2000). Consistent cross-validatory model-selection for dependent data: hv-block cross-validation. Journal of Econometrics, 99(1), 39-61.
Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2013). A sparse-group lasso. Journal of Computational and Graphical Statistics, 22(2), 231-245.
Snijders, T. A. B. (1988). On Cross-Validation for Predictor Evaluation in Time Series. In T. K. Dijkstra, On Model Uncertainty and its Statistical Implications Berlin, Heidelberg.
Stekler, H. (1970). The Brookings Model: Some Further Results. Amsterdam:North-holland
Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147-162.
Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133.
Tashman, L. J. (2000). Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting, 16(4), 437-450.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288.
Tinbergen, J. (1936). Prae-adviezen voor de Vereeniging voor de Staathuishoudkunde en de Statistiek. The Hague: Nijhoff, 62-108.
Treasury, G. B. (1980). H.M. Treasury Macroeconomic Model [technical Manual 1979]: Equation and Variable Listing (May 1980 Version).
Wang, H., & Leng, C. (2008). A note on adaptive group lasso. Computational Statistics & Data Analysis, 52(12), 5277-5286.
Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1), 49-67.
Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418-1429.
Zou, H., Hastie, T., & Tibshirani, R. (2006). Sparse principal component analysis. Journal of Computational and Graphical Statistics, 15(2), 265-286.
吳中書 (1996)，「台灣總體經濟年模型」，《台灣總體經濟計量模型研討會論文集》，57-97。
吳中書、林金龍與陳建福 (2008)，《台灣總體經濟季模型與政策分析》，臺北市：行政院經濟建設委員會。
吳中書、單易、鄭淑如、梅家瑗、蘇文瑩、高志祥、羅雅惠、黃純宜與王淑娟 (2001)，「臺灣總體經濟計量動態季模型」，《臺灣經濟預測與政策》，31(1)，111-159。
林建甫 (2006)，「台灣總體經濟金融模型之建立」，《中央銀行季刊》，28(1)，5-54。
林建甫 (2010)，「總體經濟計量模型的建立與應用」，《經濟論文叢刊》，38(1)，1-64。
高齊廷 (2022)，「混頻資料下高維度總體經濟模型的變數選取與準確性精進」，國立成功大學研究所碩士論文。
張永隆 (2010)，「最適貨幣政策之制定——考量存貨投資的小型開放經濟新興凱因斯DSGE模型」，《中央銀行季刊》，32(1)，3-24。
梁國源 (1995)，「臺灣兩個主要總體經濟季模型預測能力之評估」，《經濟論文叢刊》，23(1)，43-82。
陳宜廷、徐士勛、劉瑞文與莊額嘉 (2011)，「經濟成長率預測之評估與更新」，《經濟論文叢刊》，39(1)，1-44。
黃俞寧 (2013)，「動態隨機一般均衡架構在台灣貨幣政策制定上之應用」，《中央銀行季刊》，35(1)，3-33。
管中閔、印永翔、姚睿、黃朝熙、徐之強與陳宜廷 (2010)，《臺灣動態隨機一般均衡模型 (DSGE) 建立與政策評估》，臺北市：行政院經濟建設委員會。
蔡宜展、姚睿、秦國軒與林世揚 (2022)，「建構中大型DSGE-VAR模型－台灣中長期經濟成長率預測」，《中央銀行季刊》，44(1)，27-70。
盧奕盛 (2021)，高維度變數下總體經濟預測模型的建立，國立成功大學研究所碩士論文。