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研究生:王柏皓
研究生(外文):Bo-Hao Wang
論文名稱:以總體、廠商及高頻資料所進行之經濟預測
論文名稱(外文):Economic Forecasts by Macro-level, Firm-level and High-frequency Data
指導教授:陳宜廷陳宜廷引用關係殷壽鏞殷壽鏞引用關係
指導教授(外文):Yi-Ting ChenShou-Yung Yin
口試委員:許育進劉祝安
口試委員(外文):Yu-Chin HsuChu-An Liu
口試日期:2019-07-19
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:經濟學研究所
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:37
中文關鍵詞:經濟預測廠商資料高頻資料MIDAS迴歸因子模型
DOI:10.6342/NTU201903326
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在本文中,我們評估總體資料、廠商資料和高頻資料能否幫助預測美國工業生產指數和通貨膨脹,並藉由動態因子模型和因子混頻抽樣迴歸模型(MIDAS)進行實證研究。研究結果顯示,除了廣泛使用於預測工業生產指數和通貨膨脹的總體資料外,廠商以及高頻資料可能也包含有助於長期預測的訊息。
In this thesis, we assess the performance of a large-dimensional set of macro-level, firm-level and daily predictors in forecasting the industrial production and inflation of the U.S. We base this empirical study on the dynamic factor model and the factor mixed data sampling regression (MIDAS). The empirical study shows that the firm-level and high-frequency predictors may contain useful information in addition to the widely used macro-level predictors in the long-term forecast of the industrial production and inflation.
論文口試委員審定書 i
摘要 ii
Abstract iii
Contents iv
List of Figures v
List of Tables vi
1 Introduction 1
2 Econometric Models 4
2.1 Dynamic factor model 4
2.2 Factor MIDAS 5
3 Empirical Analysis 7
3.1 Data 7
3.2 Empirical Design 8
3.3 Empirical Findings 10
4 Conclusion 21
References 22
A Appendix 25
A.1 Macro-level data 25
A.2 Firm-level data 25
A.3 Daily data 30
A.4 Model selection 34
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