English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113318/144297 (79%)
Visitors : 51090093      Online Users : 961
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • 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


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111192


    Title: 近似因子模型的有效估計-經由懲罰最小平方法
    Other Titles: Efficient Estimations of Approximate Factor Models via Penalized Leases Squares
    Authors: 顏佑銘
    Contributors: 國際經營與貿易學系
    Keywords: 近似因子模型;主成分分析法;懲罰最小平方估計法;預測;隱性變量;Approximate Factor Model;PCA;Penalized Least Squares;Forecast;Latent Factors
    Date: 2016
    Issue Date: 2017-07-14 09:16:49 (UTC+8)
    Abstract: 近似因子模型及由其衍生出來的各種計量方法，目前被廣泛地應用在各種預測及經濟 分析上。究其原因，乃是近似因子模型可以幫助研究者有效地從大量相關變量中提取 對研究有用的訊息。在近似因子模型中，我們通常假設預測因子之間要有一定的共同 性。在這個計畫裏，我們將著重於有效地估計一種近似因子模型，其中的預測因子除 了受到共同性因素的影響之外，另外也受到一些非共同性因素，如不尋常的巨大異常 值的影響。以下我們列出本計畫會從事的工作項目:(1)我們將發展一個可行的計量方 法來估計這種近似因子模型，而該計量法方法將基於以下的假設:預測因子間的非共 同性因素的出現頻率非常的低; (2)在此假設下，我們將提出了一種懲罰最小平方估 計法(penalized least squares) 來同時分解並估計預測因子的共同及非共同性因 素; (3)為了解決這個估計問題，我們將會開發一個有效率及具彈性的演算程序，而 這項工作將有賴於一些最近提出的優化方法; (4)之後我們會經由大量的蒙地卡羅模 擬，來比較我們所提出的方法和傳統的主成分分析法，在有限樣本下，何者比較能有 效地估計這種近似因子模型; (5)最後我們會將我們所提出的方法用於預測重要總體 經濟指標的年成長率及探討隱性變量如何影響橫斷面預期資產收益率。 Approximate factor models and their extensions are widely used in forecasting and economic analysis due to their ability to extracting useful information from a large number of relevant variables. In these models, candidate predictors are typically subject to some common components. In this project, we will focus on e ciently estimating an approximate factor model in which the candidate predic- tors are additionally subject to idiosyncratic large uncommon components such as jumps or outliers. We outline our plan for the project as follows: (1) We will de- velop a viable econometric method to estimate such an approximate factor model. The econometric method will be based on the assumption that occurrences of the uncommon components are rare; (2) Under this assumption, we will propose a penalized least squares estimation procedure to simultaneously disentangle and estimate the common and uncommon components; (3) To solve the estimation problem, we will develop an e cient and exible algorithm, which will rely on some recently developed optimization methods; (4) We will conduct an intensive Monte-Carlo simulation study to compare nite-sample e ciency of the proposed method and traditional PCA method; (5) We also will demonstrate performances of the proposed method with empirical applications on forecasting yearly growths of important macroeconomic indicators and investigating how latent factors a ect cross sectional expected asset returns.
    Relation: 科技部
    103-2410-H-004-213
    Data Type: report
    Appears in Collections:[國際經營與貿易學系 ] 國科會研究計畫

    Files in This Item:

    File Description SizeFormat
    103-2410-H-004-213.pdf982KbAdobe PDF2238View/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