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題名 台灣大型總體經濟研究資料庫及應用
A large Taiwan database for macroeconomic analysis作者 陶芷敏
Tao, Chih-Min貢獻者 林馨怡
Lin, Hsin-Yi
陶芷敏
Tao, Chih-Min關鍵詞 大數據
機器學習
景氣循環
預測日期 2024 上傳時間 5-Aug-2024 13:36:33 (UTC+8) 摘要 本論文選取118個台灣具有代表性的月度總體經濟變數，建置台灣大型總體 經濟資料庫(TW-MD)，並以一站式窗口方式提供資料，以減輕研究資料蒐集與處理的負擔，增進研究成果比較與複製的效率。除建置資料庫外，本論文將該資料庫應用於以下總體經濟實證議題：首先，我們透過該資料庫的因子結構，得以識別出驅動台灣經濟的重要變數。再者，我們發現利用該資料庫豐富的資訊，結合不同大數據分析方法，除有助於判定景氣循環的轉折點，並藉由模型的變數選擇機制，發掘對判定景氣循環轉折點較重要的變數，亦有助於預測實質及名目活動相關變數時，提升預測準確性。 參考文獻 吳懿娟 2007，「我國殖利率曲線與經濟活動間關係之實證分析」，中央銀行季刊，第二十九卷第三期，頁23–64。 徐之強與黃裕烈 2005，「運用領先指標預測景氣變化之研究」，行政院經濟建設委員會委託研究報告。 徐之強與葉錦徽 2009，「臺灣消費者信心指數與景氣循環關係之探討」，行政院經濟建設委員會委託研究報告。 鄭漢亮 2020，「台灣金融變數是否能預測經濟成長？混頻模型之運用」，中央銀行季刊，第四十二卷第四期，頁5–-29。 蕭宇翔與林依伶 2020，「臺灣景氣狀態之預測」，台灣經濟預測與政策，第五十一期第一卷，頁1--56。 蕭宇翔與繆維正 2021，「以高頻物價數據進行通膨預測」，經濟論文叢刊，第四十九卷第三期，頁371--414。 Ahn, S. C., Horenstein, A. R., 2013, Eigenvalue Ratio Test for the Number of Factors, Econometrica, 81(3), 1203-1227. Alessi, L., Barigozzi, M., Capasso, M., 2010, Improved Penalization for Determining the Number of Factors in Approximate Factor Models, Statistics and Probability Letters, 80(23--24), 1806–13. Bai, J., Ng, S., 2002, Determining the Number of Factors in Approximate Factor Models, Econometrica, 70(1), 191--221. Bai, J., Ng, S., 2008, Forecasting Economic Time Series Using Targeted Predictors, Journal of Econometrics, 146, 304--317. Bedock, N., Stevanovic, D., 2017, An Empirical Study of Credit Shock Transmission in a Small Open Economy, Canadian Journal of Economics, 50(2), 541--70. Berge, T. J., 2015, Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle, Journal of Forecasting, 34(6), 455--471. Bernanke, B. S., Boivin, J., 2003, Monetary Policy in a Data Rich Environment, Journal of Monetary Economics, 50(3), 525--546. Bernanke, B. S., Boivin, J., Eliasz, P., 2005, Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach, Quarterly Journal of Economics, 120(1), 387--422. Boivin, J., Giannoni, M., 2006, DSGE Models in a Data Rich Environment, NBER Working Paper 12272. Boivin, J., Giannoni, M., Stevanovic, D., 2010, Monetary Transmission in a Small Open Economy: More Data, Fewer Puzzles, Technical Report, Columbia Business School, Columbia University. Breiman, L., 2001, Random Forests, Machine Learning, 45(1), 5--32. Champagne, J., Poulin-Bellisle, G., Sekkel, R., 2018, The Real-Time Properties of the Bank of Canada’s Staff Output Gap Estimates, Journal of Money, Credit and Banking, 50(6), 1167--88. Champagne, J., Poulin-Bellisle, G., Sekkel, R., 2019, Introducing the Bank of Canada’s Staff Projections Database, Journal of Applied Econometrics, 35(1), 114--29. Chen, Z., Iqbal, A., Lai, H., 2011, Forecasting the Probability of US Recessions: A Probit and Dynamic Factor Modelling Approach, Canadian Journal of Economics, 44(2), 651--672. Christiansen, C., Eriksen, J. N., Møller, S. V., 2014, Forecasting US Recessions: The Role of Sentiment, Journal of Banking & Finance, 49, 459--468. Cook, S., 2001, Finite-Sample Critical Values of the Augmented Dickey-Fuller Statistic: A Note on Lag Order, Economic Issues, 6, 31--38. Del Negro, M., Hasegawa, R. B., Schorfheide, F., 2016, Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance. Journal of Econometrics, 192(2), 391–-405. Diebold, F. X., Mariano, R. S., 2002, Comparing Predictive Accuracy, Journal of Business & economic statistics, 20(1), 134--144. Dueker, M. J., 1997, Strengthening the Case for the Yield Curve as a Predictor of US Recessions, Federal Reserve Bank of St. Louis Review, (Mar), 41--51. Ellington, M., Florackis, C., Milas, C., 2017, Liquidity Shocks and Real GDP Growth: Evidence from a Bayesian Time-Varying Parameter VAR, Journal of International Money and Finance, 72, 93--117. Elliott, G., Gargano, A., Timmermann, A., 2013, Complete Subset Regressions, Journal of Econometrics, 177(2), 357--73. Elliott, G. Timmermann, A., 2005, Optimal Forecast Combination under Regime Switching, International Economic Review, 46(4), 1081--1102. Estrella, A., 1998, A New Measure of Fit for Equations with Dichotomous Dependent Variables, Journal of Business and Economic Statistics, 16(2), 198–-205. Estrella, A., Mishkin, F. S., 1998, Predicting US Recessions: Financial Variables as Leading Indicators, Review of Economics and Statistics, 80(1), 45--61. Fornaro, P., 2016, Forecasting US Recessions with a Large Set of Predictors, Journal of Forecasting, 35(6), 477--492. Fortin‐Gagnon, O., Leroux, M., Stevanovic, D., Surprenant, S., 2022, A Large Canadian Database for Macroeconomic Analysis, Canadian Journal of Economics, 55(4), 1799--1833. Galbraith, J. W., Tkacz, G., 2007, How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables, Bank of Canada working paper no. 2007-1. Gosselin, M. A., Tkacz, G., 2001, Evaluating Factor Models: An Application to Forecasting Inflation in Canada, Bank of Canada working paper no. 2001-18. Goulet Coulombe, P., Leroux, M., Stevanovic, D., Surprenant, S., 2019, How is Machine Learning Useful for Macroeconomic Forecasting?, CIRANO working paper no. 2019--22. Goulet Coulombe, P., Marcellino, M., Stevanović, D., 2021, Can Machine Learning Catch the Covid-19 Recession?, National Institute Economic Review, 256, 71--109. Hoerl, A. E., Kennard R. W., 1970, Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 12(1), 55--67. Jurado, K., Ludvigson, S., Ng, S., 2015, Measuring Macroeconomic Uncertainty, American Economic Review, 105(3), 1177--1216. Kotchoni, R., Leroux, M., Stevanovic, D., 2019, Macroeconomic Forecast Accuracy in a Data-Rich Environment, Journal of Applied Econometrics, 34, 1050--72. Kruskal, W. H., Wallis, W. A., 1952, Use of Ranks in One-Criterion Variance Analysis, Journal of the American statistical Association, 47(260), 583--621. Ludvigson, S., Ma, S., Ng, S., 2021, Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?, American Economic Journal: Macroeconomics, 13(4), 369--410. Ludvigson, S., Ng, S., 2011, A Factor Analysis of Bond Risk Premia, in D. Gilles and A. Ullah (eds), Handbook of Empirical Economics and Finance, Chapman and Hall, pp. 313--372. McCracken, M. W., Ng, S., 2016, FRED-MD: A Monthly Database for Macroeconomic Research, Journal of Business and Economic Statistics, 34(4), 574--89. McCracken, M. W., Ng, S., 2020, FRED-QD: A Quarterly Database for Macroeconomic Research, NBER Working Paper 26872. McFadden, D., 1974, Conditional Logit Analysis of Qualitative Choice Behavior. Medeiros, M. C., Vasconcelos, G. F., Veiga, Á., Zilberman, E., 2021, Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods, Journal of Business and Economic Statistics, 39(1), 98--119. Miranda-Agrippino, S., Ricco, G., 2021, The Transmission of Monetary Policy Shocks, American Economic Journal: Macroeconomics, 13(3), 74--107. Onatski, A., 2010, Determining the Number of Factors from Empirical Distribution of Eigenvalues, Review of Economics and Statistics, 92(4), 1004–16. Prados de la Escosura, L., 2016, Mismeasuring Long Run Growth: The Bias from Splicing National Accounts – the Case of Spain, Cliometrica, 10(3), 251--275. Sties, M., 2017, Forecasting Recessions in a Big Data Environment, technical report, Department of Economics, University of Alberta. Stock, J. H., Watson, M. W., 1996, Evidence on Structural Instability in Macroeconomic Time Series Relations, Journal of Business and Economic Statistics, 14, 11--30. Stock, J. H., Watson, M. W., 1998, Diffusion Indexes, NBER Working Paper 6702. Stock, J. H., Watson, M. W., 2002a, Forecasting Using Principal Components from a Large Number of Predictors, Journal of the American Statistical Association, 97, 1167--79. Stock, J. H., Watson, M. W., 2002b, Macroeconomic Forecasting Using Diffusion Indexes, Journal of Business and Economic Statistics, 20(2), 147--62. Stock, J. H., Watson, M. W., 2003, How Did Leading Indicator Forecasts Perform during the 2001 Recession?, FRB Richmond Economic Quarterly, 89(3), 71-90. Stock, J. H., Watson, M. W., 2005, Implications of Dynamic Factor Models for VAR Analysis, NBER Working Paper 11467. Stock, J. H., Watson, M. W., 2006, Forecasting with Many Predictors, Handbook of Forecasting, North Holland. Stock, J. H., Watson, M. W., 2014, Estimating Turning Points Using Large Data Sets, Journal of Econometrics, 178, 368--381. Stock, J. H., Watson, M. W., 2016, Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics, Handbook of Macroeconomics, Vol. 2, ed. JB Taylor, H Uhlig, pp. 415--525. Amsterdam: Elsevier. Tibshirani, R., 1996, Regression Shrinkage and Selection Via the Lasso, Journal of the Royal Statistical Society: Series B, 58(1), 267--88. Uematsu, Y., Yamagata, T., 2022, Inference in Sparsity-Induced Weak Factor Models, Journal of Business and Economic Statistics, 41(1), 126--139. Zou, H., Hastie T., 2004, Regularization and Variable Selection via the Elastic Net,” Journal of the Royal Statistical Society: Series B, 67(2), 301--20. Zou, H., Hastie T., 2006, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, 101(476), 1418--29. 描述 碩士
國立政治大學
經濟學系
111258010資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111258010 資料類型 thesis dc.contributor.advisor 林馨怡 zh_TW dc.contributor.advisor Lin, Hsin-Yi en_US dc.contributor.author (Authors) 陶芷敏 zh_TW dc.contributor.author (Authors) Tao, Chih-Min en_US dc.creator (作者) 陶芷敏 zh_TW dc.creator (作者) Tao, Chih-Min en_US dc.date (日期) 2024 en_US dc.date.accessioned 5-Aug-2024 13:36:33 (UTC+8) - dc.date.available 5-Aug-2024 13:36:33 (UTC+8) - dc.date.issued (上傳時間) 5-Aug-2024 13:36:33 (UTC+8) - dc.identifier (Other Identifiers) G0111258010 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152699 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 111258010 zh_TW dc.description.abstract (摘要) 本論文選取118個台灣具有代表性的月度總體經濟變數，建置台灣大型總體 經濟資料庫(TW-MD)，並以一站式窗口方式提供資料，以減輕研究資料蒐集與處理的負擔，增進研究成果比較與複製的效率。除建置資料庫外，本論文將該資料庫應用於以下總體經濟實證議題：首先，我們透過該資料庫的因子結構，得以識別出驅動台灣經濟的重要變數。再者，我們發現利用該資料庫豐富的資訊，結合不同大數據分析方法，除有助於判定景氣循環的轉折點，並藉由模型的變數選擇機制，發掘對判定景氣循環轉折點較重要的變數，亦有助於預測實質及名目活動相關變數時，提升預測準確性。 zh_TW dc.description.tableofcontents 第一章 緒論 1 第二章 文獻回顧 4 第一節 國際 4 第二節 台灣 10 第三章 資料庫建置 12 第一節 資料 12 第二節 因子模型分析 14 第四章 資料庫應用一：景氣循環判定 27 第一節 模型設定 29 第二節 實證結果 33 第五章 資料庫應用二：經濟活動預測 37 第一節 預測模型 38 第二節 樣本外預測 44 第三節 實證結果 45 第六章 結論 50 參考文獻 51 A 台灣大型總體經濟資料庫TW-MD變數列表 55 B ADF檢定結果 62 zh_TW dc.format.extent 1383326 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111258010 en_US dc.subject (關鍵詞) 大數據 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 景氣循環 zh_TW dc.subject (關鍵詞) 預測 zh_TW dc.title (題名) 台灣大型總體經濟研究資料庫及應用 zh_TW dc.title (題名) A large Taiwan database for macroeconomic analysis en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 吳懿娟 2007，「我國殖利率曲線與經濟活動間關係之實證分析」，中央銀行季刊，第二十九卷第三期，頁23–64。 徐之強與黃裕烈 2005，「運用領先指標預測景氣變化之研究」，行政院經濟建設委員會委託研究報告。 徐之強與葉錦徽 2009，「臺灣消費者信心指數與景氣循環關係之探討」，行政院經濟建設委員會委託研究報告。 鄭漢亮 2020，「台灣金融變數是否能預測經濟成長？混頻模型之運用」，中央銀行季刊，第四十二卷第四期，頁5–-29。 蕭宇翔與林依伶 2020，「臺灣景氣狀態之預測」，台灣經濟預測與政策，第五十一期第一卷，頁1--56。 蕭宇翔與繆維正 2021，「以高頻物價數據進行通膨預測」，經濟論文叢刊，第四十九卷第三期，頁371--414。 Ahn, S. C., Horenstein, A. R., 2013, Eigenvalue Ratio Test for the Number of Factors, Econometrica, 81(3), 1203-1227. Alessi, L., Barigozzi, M., Capasso, M., 2010, Improved Penalization for Determining the Number of Factors in Approximate Factor Models, Statistics and Probability Letters, 80(23--24), 1806–13. Bai, J., Ng, S., 2002, Determining the Number of Factors in Approximate Factor Models, Econometrica, 70(1), 191--221. Bai, J., Ng, S., 2008, Forecasting Economic Time Series Using Targeted Predictors, Journal of Econometrics, 146, 304--317. Bedock, N., Stevanovic, D., 2017, An Empirical Study of Credit Shock Transmission in a Small Open Economy, Canadian Journal of Economics, 50(2), 541--70. Berge, T. J., 2015, Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle, Journal of Forecasting, 34(6), 455--471. Bernanke, B. S., Boivin, J., 2003, Monetary Policy in a Data Rich Environment, Journal of Monetary Economics, 50(3), 525--546. Bernanke, B. S., Boivin, J., Eliasz, P., 2005, Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach, Quarterly Journal of Economics, 120(1), 387--422. Boivin, J., Giannoni, M., 2006, DSGE Models in a Data Rich Environment, NBER Working Paper 12272. Boivin, J., Giannoni, M., Stevanovic, D., 2010, Monetary Transmission in a Small Open Economy: More Data, Fewer Puzzles, Technical Report, Columbia Business School, Columbia University. Breiman, L., 2001, Random Forests, Machine Learning, 45(1), 5--32. Champagne, J., Poulin-Bellisle, G., Sekkel, R., 2018, The Real-Time Properties of the Bank of Canada’s Staff Output Gap Estimates, Journal of Money, Credit and Banking, 50(6), 1167--88. Champagne, J., Poulin-Bellisle, G., Sekkel, R., 2019, Introducing the Bank of Canada’s Staff Projections Database, Journal of Applied Econometrics, 35(1), 114--29. Chen, Z., Iqbal, A., Lai, H., 2011, Forecasting the Probability of US Recessions: A Probit and Dynamic Factor Modelling Approach, Canadian Journal of Economics, 44(2), 651--672. Christiansen, C., Eriksen, J. N., Møller, S. V., 2014, Forecasting US Recessions: The Role of Sentiment, Journal of Banking & Finance, 49, 459--468. Cook, S., 2001, Finite-Sample Critical Values of the Augmented Dickey-Fuller Statistic: A Note on Lag Order, Economic Issues, 6, 31--38. Del Negro, M., Hasegawa, R. B., Schorfheide, F., 2016, Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance. Journal of Econometrics, 192(2), 391–-405. Diebold, F. X., Mariano, R. S., 2002, Comparing Predictive Accuracy, Journal of Business & economic statistics, 20(1), 134--144. Dueker, M. J., 1997, Strengthening the Case for the Yield Curve as a Predictor of US Recessions, Federal Reserve Bank of St. Louis Review, (Mar), 41--51. Ellington, M., Florackis, C., Milas, C., 2017, Liquidity Shocks and Real GDP Growth: Evidence from a Bayesian Time-Varying Parameter VAR, Journal of International Money and Finance, 72, 93--117. Elliott, G., Gargano, A., Timmermann, A., 2013, Complete Subset Regressions, Journal of Econometrics, 177(2), 357--73. Elliott, G. Timmermann, A., 2005, Optimal Forecast Combination under Regime Switching, International Economic Review, 46(4), 1081--1102. Estrella, A., 1998, A New Measure of Fit for Equations with Dichotomous Dependent Variables, Journal of Business and Economic Statistics, 16(2), 198–-205. Estrella, A., Mishkin, F. S., 1998, Predicting US Recessions: Financial Variables as Leading Indicators, Review of Economics and Statistics, 80(1), 45--61. Fornaro, P., 2016, Forecasting US Recessions with a Large Set of Predictors, Journal of Forecasting, 35(6), 477--492. Fortin‐Gagnon, O., Leroux, M., Stevanovic, D., Surprenant, S., 2022, A Large Canadian Database for Macroeconomic Analysis, Canadian Journal of Economics, 55(4), 1799--1833. Galbraith, J. W., Tkacz, G., 2007, How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables, Bank of Canada working paper no. 2007-1. Gosselin, M. A., Tkacz, G., 2001, Evaluating Factor Models: An Application to Forecasting Inflation in Canada, Bank of Canada working paper no. 2001-18. Goulet Coulombe, P., Leroux, M., Stevanovic, D., Surprenant, S., 2019, How is Machine Learning Useful for Macroeconomic Forecasting?, CIRANO working paper no. 2019--22. Goulet Coulombe, P., Marcellino, M., Stevanović, D., 2021, Can Machine Learning Catch the Covid-19 Recession?, National Institute Economic Review, 256, 71--109. Hoerl, A. E., Kennard R. W., 1970, Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 12(1), 55--67. Jurado, K., Ludvigson, S., Ng, S., 2015, Measuring Macroeconomic Uncertainty, American Economic Review, 105(3), 1177--1216. Kotchoni, R., Leroux, M., Stevanovic, D., 2019, Macroeconomic Forecast Accuracy in a Data-Rich Environment, Journal of Applied Econometrics, 34, 1050--72. Kruskal, W. H., Wallis, W. A., 1952, Use of Ranks in One-Criterion Variance Analysis, Journal of the American statistical Association, 47(260), 583--621. Ludvigson, S., Ma, S., Ng, S., 2021, Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?, American Economic Journal: Macroeconomics, 13(4), 369--410. Ludvigson, S., Ng, S., 2011, A Factor Analysis of Bond Risk Premia, in D. Gilles and A. Ullah (eds), Handbook of Empirical Economics and Finance, Chapman and Hall, pp. 313--372. McCracken, M. W., Ng, S., 2016, FRED-MD: A Monthly Database for Macroeconomic Research, Journal of Business and Economic Statistics, 34(4), 574--89. McCracken, M. W., Ng, S., 2020, FRED-QD: A Quarterly Database for Macroeconomic Research, NBER Working Paper 26872. McFadden, D., 1974, Conditional Logit Analysis of Qualitative Choice Behavior. Medeiros, M. C., Vasconcelos, G. F., Veiga, Á., Zilberman, E., 2021, Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods, Journal of Business and Economic Statistics, 39(1), 98--119. Miranda-Agrippino, S., Ricco, G., 2021, The Transmission of Monetary Policy Shocks, American Economic Journal: Macroeconomics, 13(3), 74--107. Onatski, A., 2010, Determining the Number of Factors from Empirical Distribution of Eigenvalues, Review of Economics and Statistics, 92(4), 1004–16. Prados de la Escosura, L., 2016, Mismeasuring Long Run Growth: The Bias from Splicing National Accounts – the Case of Spain, Cliometrica, 10(3), 251--275. Sties, M., 2017, Forecasting Recessions in a Big Data Environment, technical report, Department of Economics, University of Alberta. Stock, J. H., Watson, M. W., 1996, Evidence on Structural Instability in Macroeconomic Time Series Relations, Journal of Business and Economic Statistics, 14, 11--30. Stock, J. H., Watson, M. W., 1998, Diffusion Indexes, NBER Working Paper 6702. Stock, J. H., Watson, M. W., 2002a, Forecasting Using Principal Components from a Large Number of Predictors, Journal of the American Statistical Association, 97, 1167--79. Stock, J. H., Watson, M. W., 2002b, Macroeconomic Forecasting Using Diffusion Indexes, Journal of Business and Economic Statistics, 20(2), 147--62. Stock, J. H., Watson, M. W., 2003, How Did Leading Indicator Forecasts Perform during the 2001 Recession?, FRB Richmond Economic Quarterly, 89(3), 71-90. Stock, J. H., Watson, M. W., 2005, Implications of Dynamic Factor Models for VAR Analysis, NBER Working Paper 11467. Stock, J. H., Watson, M. W., 2006, Forecasting with Many Predictors, Handbook of Forecasting, North Holland. Stock, J. H., Watson, M. W., 2014, Estimating Turning Points Using Large Data Sets, Journal of Econometrics, 178, 368--381. Stock, J. H., Watson, M. W., 2016, Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics, Handbook of Macroeconomics, Vol. 2, ed. JB Taylor, H Uhlig, pp. 415--525. Amsterdam: Elsevier. Tibshirani, R., 1996, Regression Shrinkage and Selection Via the Lasso, Journal of the Royal Statistical Society: Series B, 58(1), 267--88. Uematsu, Y., Yamagata, T., 2022, Inference in Sparsity-Induced Weak Factor Models, Journal of Business and Economic Statistics, 41(1), 126--139. Zou, H., Hastie T., 2004, Regularization and Variable Selection via the Elastic Net,” Journal of the Royal Statistical Society: Series B, 67(2), 301--20. Zou, H., Hastie T., 2006, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, 101(476), 1418--29. zh_TW
