政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152697

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152697

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113318/144297 (79%)
Visitors : 51075740 Online Users : 939

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Social Science > Department of Economics > Theses > Item 140.119/152697

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152697

Title:	結合機器學習與混合頻率方法即時預測美國通膨率 Nowcasting of U.S. Inflation Rates Using Machine Learning and Mixed-Frequency Approaches
Authors:	謝錚奇 HSIEH, CHENG-CHI
Contributors:	林馨怡 Lin, Hsin-Yi 謝錚奇 HSIEH, CHENG-CHI
Keywords:	通膨率狀態空間模型 LASSO MIDAS
Date:	2024
Issue Date:	2024-08-05 13:36:10 (UTC+8)
Abstract:	本論文使用狀態空間模型以及 sparse group LASSO MIDAS (sg-LASSO- MIDAS) 模型，即時預測預測美國 1996 年 5 月至 2023 年 12 月的通貨膨脹率。實證結果顯示，使用高頻變數有助於提升美國通貨膨脹率的預測準確性，其中 sg-LASSO-MIDAS 藉由對稀疏組的係數估計限制，將 28 筆日資料視為同一組別，並在係數估計時，對同一組別的係數估計進行相同限制，能更好的利用經狀態空間模型處理過後的高頻資料變數做出通膨預測，在本論文的五個預測期間預測結果比較中，取得最好的預測表現。
Reference:	Atkeson, A., Ohanian, L. E., 2001. Are Phillips curves useful for forecasting inflation? Federal Reserve bank of Minneapolis quarterly review, 25(1), 2–11. Babii, A., Ghysels, E., Striaukas, J., 2022. Machine learning time series regressions with an application to nowcasting. Journal of Business & Economic Statistics, 40(3), 1094–1106. Barbaglia, L., Consoli, S., Manzan, S., 2023. Forecasting with economic news. Journal of Business & Economic Statistics, 41(3), 708–719. Breitung, J. O., Roling, C., 2015. Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting, 34(7), 588–603. Bybee, L., Kelly, B. T., Manela, A., Xiu, D., 2020. The structure of economic news. Cavallo, A., 2013. Online and oﬀicial price indexes: Measuring Argentina’s inflation. Journal of Monetary Economics, 60(2), 152–165. Cavallo, A., Rigobon, R., 2016. The billion prices project: Using online prices for measurement and research. Journal of Economic Perspectives, 30(2), 151–178. Durbin, J., Koopman, S. J., 2012. Time series analysis by state space methods. Foroni, C., Marcellino, M., Schumacher, C., 2015. Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society Series A: Statistics in Society, 178(1), 57–82. Funke, M., Mehrotra, A., Yu, H., 2015. Tracking Chinese CPI inflation in real time. Empirical Economics, 48, 1619–1641. Ghysels, E., 2016. MIDAS matlab toolbox. URL: http://www.unc.edu/~eghysels/papers/MIDAS_Usersguide_V1.0.pdf.Lastaccessedon, 8(16), 2016. Ghysels, E., Santa-Clara, P., Valkanov, R., 2004. The MIDAS touch: Mixed data sampling regression models. Ghysels, E., Sinko, A., Valkanov, R., 2007. MIDAS regressions: Further results and new directions. Econometric reviews, 26(1), 53–90. Giannone, D., Reichlin, L., Small, D., 2008. Nowcasting: The real-time informational content of macroeconomic data. Journal of monetary economics, 55(4), 665–676. Knotek II, E. S., Zaman, S., 2017. Nowcasting US headline and core inflation. Journal of Money Credit and Banking, 49(5), 931–968. Medeiros, M. C., Vasconcelos, G. F. R., Veiga, A., Zilberman, E., 2021. Forecasting inflation in a data-rich environment: the benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98–119. Monteforte, L., Moretti, G., 2013. Real-time forecasts of inflation: The role of financial variables. Journal of Forecasting, 32(1), 51–61. Schorfheide, F., Song, D., 2015. Real-time forecasting with a mixed-frequency VAR. Journal of Business & Economic Statistics, 33(3), 366–380. Steindel, C., Cecchetti, S. G., Chu, R., 2005. The unreliability of inflation indicators. Available at SSRN 716681. Stock, J. H., Watson, M. W., 1999. Forecasting inflation. Journal of monetary economics, 44(2), 293–335. Torrontegui, E., Ibáñez, S., Martínez-Garaot, S., Modugno, M., del Campo, A., Guéry-Odelin, D., Ruschhaupt, A., Chen, X., Muga, J. G., 2013. Shortcuts to adiabaticity. Zheng, T., Fan, X., Jin, W., Fang, K., 2024. Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data. International Journal of Forecasting, 40(2), 746–761.
Description:	碩士國立政治大學經濟學系 111258003
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111258003
Data Type:	thesis
Appears in Collections:	[Department of Economics] Theses

Files in This Item:

File	Description	Size	Format
800301.pdf		823Kb	Adobe PDF	0	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback