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題名 結合變數挑選和混頻方法當下預測通膨
Nowcasting Inflation by Combining Variable Selection and Mixed Frequency Methods作者 袁瑋成
Yuan, Wei-Cheng貢獻者 林馨怡
Lin, Hsin-Yi
袁瑋成
Yuan, Wei-Cheng關鍵詞 混頻
變數挑選
通貨膨脹率日期 2018 上傳時間 4-Jan-2019 16:58:02 (UTC+8) 摘要 本文結合變數挑選與混頻(Mixed frequency)方法，提出兩步驟預測模型，並考慮大量且不同頻率的經濟變數當下預測美國通貨膨脹率。以美國1998年7月到2018年5月的實證結果顯示，加上變數挑選後的混頻模型，其預測表現顯著比無變數挑選的混頻模型好，且僅用少數個挑選出的變數組合預測可以更近一步改善模型的預測表現。而使用不同變數個數組合預測的混頻模型，其預測表現顯著比無混頻模型好，這表示以混頻方法將高頻率變數的資訊納入模型中確實能改善當下預測通膨的預測表現。我們亦發現僅使用少數重要的變數組合預測時，高頻率重要變數對預測表現的影響遠大於低頻率重要變數。此外，考慮不同的穩健性檢驗的結果顯示，本文所提之方法具有穩健性。 參考文獻 林馨怡與廖珈燕 (2017), ”大數據預測通貨膨脹率”, working paper.廖珈燕 (2016), ”大數據預測通貨膨脹率”, 政治大學經濟系碩士論文.吳若瑋 (2015), ”通貨膨脹率之預測”, 經濟論文 43(2): 253-285.Ang, A., Bekaert, G., and Wei, M., 2007. Do macro variables, asset markets, or surveys forecast inflation better? Journal of Monetary Economics, 54(4): 1163-1212.Aparicio, D., and Bertolotto, M., 2017. Forecasting Inflation with Online Prices,working paper. Aron, J., and Muellbauer, J., 2013. New methods for forecasting inflation: Applied to the US. Oxford Bulletin of Economics and Statistics 75(5): 637-661. Banbura, M., and Modugno, M., 2010. Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data. European Central Bank Working paper,1189.Breitung, J., and Roling, C., 2015. Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting 34(7): 588-603. Carriere-Swallow, Y., and Labbe, F. 2013. Nowcasting with Google trends in an emerging market. Journal of Forecasting 32(4): 289-298.Carriero, A., Clark, T.E., and Marcellino, M., 2015. Realtime nowcasting with a bayesian mixed frequency model with stochastic volatility. Journal of the Royal Statistical Society 178(4): 837-862.Clements, M.P., and Galva o, A.B., 2008. Macroeconomic forecasting with mixed frequency data: Forecasting output growth in the United States. Journal of Business and Economic Statistics 26(4): 546–554.Cristadoro, R., Saporito, G.,and Venditti, F. 2008. Forecasting inflation and tracking monetary policy in the euro area: Does national information help? ECB Working Paper, No.900.Diebold, F. X., and Mariano, R. S., 1995. Comparing predictive accuracy. Journal of Business & Economic Statistics 13(3): 253–263.Filippo, G.D. 2015. Dynamic model averaging and CPI inflation forecasts: A comparison between the Euro area and the United States. Journal of Forecasting 34: 619-648.Garcia, M.G.P., Medeiros, M.C., and Vasconcelos, G.F.R. 2017. Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting 33: 679-693.Giannone, D., Lenza, M., Momferatou, D., and Onorante, L. 2014. Short-term inflation projections: A bayesian vector autoregressive approach. International Journal of Forecasting 30(3): 635- 644.Ghysels, E., Santa-Clara, P.,and Valkanov, R. 2004. The MIDAS touch: Mixed data sampling regression models. CIRANO Working Papers 2004s-20, CIRANO.Lenza, M. and Thomas, T. 2011. A factor model for Euro-area short-term inflation analysis Journal of Economics and Statistics 231(1): 50-62.Modugno, M. 2013. Now-casting inflation using high frequency data. International Journal of Forecasting 29(4): 664-675.Monteforte, L., and Moretti, G. 2013. Real time forecasts of inflation: The role of financial variables. Journal of Forecasting 32(1): 51-61.Higgins, P., Zha, T.,and Zhong, W. 2016. Forecasting China’s economic growth and inflation. China Economic Review 41: 46-61.Seabold, S. 2015. Nowcasting prices using Google trends: An application to central America. World Bank Policy Research Working Paper, No.7398.Smith,P. 2016. Google’s MIDAS touch: Predicting UK unemployment with internet search data. Journal of Forecasting 35(3): 263-284.Stock, J. H., and Watson, M. W. 1999. Forecasting inflation. Journal of Monetary Economics 44: 293-335.Tibshirani, R. 1996. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society Series B (Statistical Methodology), 58: 267-288.Zou, H. and Hastie,T. Regularization and variable selection via the elastic net Journal of the Royal Statistical Society Series B(Statistical Methodology), 67(2): 301-320. 描述 碩士
國立政治大學
經濟學系
105258019資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105258019 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) 袁瑋成 zh_TW dc.contributor.author (Authors) Yuan, Wei-Cheng en_US dc.creator (作者) 袁瑋成 zh_TW dc.creator (作者) Yuan, Wei-Cheng en_US dc.date (日期) 2018 en_US dc.date.accessioned 4-Jan-2019 16:58:02 (UTC+8) - dc.date.available 4-Jan-2019 16:58:02 (UTC+8) - dc.date.issued (上傳時間) 4-Jan-2019 16:58:02 (UTC+8) - dc.identifier (Other Identifiers) G0105258019 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/121742 - 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) 105258019 zh_TW dc.description.abstract (摘要) 本文結合變數挑選與混頻(Mixed frequency)方法，提出兩步驟預測模型，並考慮大量且不同頻率的經濟變數當下預測美國通貨膨脹率。以美國1998年7月到2018年5月的實證結果顯示，加上變數挑選後的混頻模型，其預測表現顯著比無變數挑選的混頻模型好，且僅用少數個挑選出的變數組合預測可以更近一步改善模型的預測表現。而使用不同變數個數組合預測的混頻模型，其預測表現顯著比無混頻模型好，這表示以混頻方法將高頻率變數的資訊納入模型中確實能改善當下預測通膨的預測表現。我們亦發現僅使用少數重要的變數組合預測時，高頻率重要變數對預測表現的影響遠大於低頻率重要變數。此外，考慮不同的穩健性檢驗的結果顯示，本文所提之方法具有穩健性。 zh_TW dc.description.tableofcontents 1 緒論 12 文獻回顧 43 計量方法與預測模型 93.1 變數挑選 93.2 混合頻率方法 103.3 預測模型 124 實證結果 174.1 資料和模型評估 174.2 變數挑選對預測能力之影響 214.3 考慮混合頻率 284.4 不同組合預測方法 304.5 穩健性檢驗 315 結論 40參考文獻 41 zh_TW dc.format.extent 1372963 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105258019 en_US dc.subject (關鍵詞) 混頻 zh_TW dc.subject (關鍵詞) 變數挑選 zh_TW dc.subject (關鍵詞) 通貨膨脹率 zh_TW dc.title (題名) 結合變數挑選和混頻方法當下預測通膨 zh_TW dc.title (題名) Nowcasting Inflation by Combining Variable Selection and Mixed Frequency Methods en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 林馨怡與廖珈燕 (2017), ”大數據預測通貨膨脹率”, working paper.廖珈燕 (2016), ”大數據預測通貨膨脹率”, 政治大學經濟系碩士論文.吳若瑋 (2015), ”通貨膨脹率之預測”, 經濟論文 43(2): 253-285.Ang, A., Bekaert, G., and Wei, M., 2007. Do macro variables, asset markets, or surveys forecast inflation better? Journal of Monetary Economics, 54(4): 1163-1212.Aparicio, D., and Bertolotto, M., 2017. Forecasting Inflation with Online Prices,working paper. Aron, J., and Muellbauer, J., 2013. New methods for forecasting inflation: Applied to the US. Oxford Bulletin of Economics and Statistics 75(5): 637-661. Banbura, M., and Modugno, M., 2010. Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data. European Central Bank Working paper,1189.Breitung, J., and Roling, C., 2015. Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting 34(7): 588-603. Carriere-Swallow, Y., and Labbe, F. 2013. Nowcasting with Google trends in an emerging market. Journal of Forecasting 32(4): 289-298.Carriero, A., Clark, T.E., and Marcellino, M., 2015. Realtime nowcasting with a bayesian mixed frequency model with stochastic volatility. Journal of the Royal Statistical Society 178(4): 837-862.Clements, M.P., and Galva o, A.B., 2008. Macroeconomic forecasting with mixed frequency data: Forecasting output growth in the United States. Journal of Business and Economic Statistics 26(4): 546–554.Cristadoro, R., Saporito, G.,and Venditti, F. 2008. Forecasting inflation and tracking monetary policy in the euro area: Does national information help? ECB Working Paper, No.900.Diebold, F. X., and Mariano, R. S., 1995. Comparing predictive accuracy. Journal of Business & Economic Statistics 13(3): 253–263.Filippo, G.D. 2015. Dynamic model averaging and CPI inflation forecasts: A comparison between the Euro area and the United States. Journal of Forecasting 34: 619-648.Garcia, M.G.P., Medeiros, M.C., and Vasconcelos, G.F.R. 2017. Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting 33: 679-693.Giannone, D., Lenza, M., Momferatou, D., and Onorante, L. 2014. Short-term inflation projections: A bayesian vector autoregressive approach. International Journal of Forecasting 30(3): 635- 644.Ghysels, E., Santa-Clara, P.,and Valkanov, R. 2004. The MIDAS touch: Mixed data sampling regression models. CIRANO Working Papers 2004s-20, CIRANO.Lenza, M. and Thomas, T. 2011. A factor model for Euro-area short-term inflation analysis Journal of Economics and Statistics 231(1): 50-62.Modugno, M. 2013. Now-casting inflation using high frequency data. International Journal of Forecasting 29(4): 664-675.Monteforte, L., and Moretti, G. 2013. Real time forecasts of inflation: The role of financial variables. Journal of Forecasting 32(1): 51-61.Higgins, P., Zha, T.,and Zhong, W. 2016. Forecasting China’s economic growth and inflation. China Economic Review 41: 46-61.Seabold, S. 2015. Nowcasting prices using Google trends: An application to central America. World Bank Policy Research Working Paper, No.7398.Smith,P. 2016. Google’s MIDAS touch: Predicting UK unemployment with internet search data. Journal of Forecasting 35(3): 263-284.Stock, J. H., and Watson, M. W. 1999. Forecasting inflation. Journal of Monetary Economics 44: 293-335.Tibshirani, R. 1996. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society Series B (Statistical Methodology), 58: 267-288.Zou, H. and Hastie,T. Regularization and variable selection via the elastic net Journal of the Royal Statistical Society Series B(Statistical Methodology), 67(2): 301-320. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.ECONO.024.2018.F06 en_US
