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博碩士論文 etd-0615117-174742 詳細資訊
Title page for etd-0615117-174742
論文名稱
Title
以混合頻率誤差修正模型預測台灣通貨膨脹
Forecasting Taiwan’s Inflation with Mixed Frequency Error Correction Model
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
46
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2017-07-05
繳交日期
Date of Submission
2017-07-15
關鍵字
Keywords
預測、通貨膨脹、共整合、混合頻率誤差修正模型、混和數據抽樣
prediction, mixed data sampling, inflation, cointegration, Mixed frequency error correction model
統計
Statistics
本論文已被瀏覽 5819 次,被下載 840
The thesis/dissertation has been browsed 5819 times, has been downloaded 840 times.
中文摘要
本文主要探討的議題是預測台灣通貨膨脹,常見的預測模型 為誤差修正模型 (Error Correction Model, ECM),資料必須在相同 頻率下進行,由於省略了高頻變數觀察,可能導致訊息丟失,因 此,本文採用 Götz et al. (2014) 所提出的混合頻率誤差修正模型 (mixed frequency error correction model, MF-ECM),引入混合數據抽 樣 (mixed data sampling),用於不同頻率下抽樣,能同時保有低頻資 料和高頻資料的訊息且能探討非定態資料間的長短期關係,不僅擁 有共整合 (cointegration) 與誤差修正的優點,也改善了喪失高頻資料 的缺點,提高預測的能力。
研究資料期間為 2006 年 1 月至 2016 年 12 月,月資料 132 筆、 週資料 528 筆,主要變數為躉售物價指數 (WPI)、消費者物價指數 (CPI) 及原油價格,採用混合頻率誤差修正模型來預測 WPI 及 CPI, 並探討 MF-ECM 是否會比 ECM 的預測力好,而研究的實證結果根 據三項預測指標的評估顯示 MF-ECM 預測力優於 ECM。
Abstract
The main topic of this paper is to forecast the inflation of Taiwan. As a most common prediction model, Error Correction Model (ECM) uses data that must be processed at the same frequency. The omission of high-frequency variables for observation may lead to loss of information. Therefore, in this paper, the Mixed Frequency Error Correction Model (MF- ECM) presented by Götz et al. (2014) is adopted along with Mixed Data Sampling. Sampling at different frequencies can obtain information at the low-frequency and high-frequency as well as explore the short and long-term relationship between the non-stationary data. It not only has the advantages of cointegration and error correction, but also improves both the problem of the high-frequency data loss and the performance of predictability.
The period for data collection was from January 2006 to December 2016, with 132 pieces of the monthly data and 528 pieces of the weekly data, the main variables being the Wholesale Price Index (WPI), Consumer Price Index (CPI) and crude oil prices. The Mixed Frequency Error Correction Model is used to predict WPI and CPI, and whether MF-ECM is better than ECM in predictability is also explored. According to the assessment at the three indexes of power of prediction, the empirical result of the study shows that MF-ECM is better than ECM in predictability.
目次 Table of Contents
口試委員會審定書................................................................................. . i
摘要........................................................................................................ ii
ABSTRACT............................................................................................ iii
圖目錄.................................................................................................... vi
表目錄 ................................................................................................... vii
第一章 緒論............................................................................................ 1
1.1 研究動機與目的................................................................................ 1
1.2 研究架構........................................................................................... 2
第二章 文獻回顧..................................................................................... 3
2.1 通貨膨脹相關理論回顧 .................................................................... 3
2.2 計量模型文獻回顧............................................................................ 6
2.3 實證文獻回顧 ................................................................................... 7
第三章 研究方法..................................................................................... 9
3.1 單根檢定........................................................................................... 9
3.2 共整合檢定........................................................................................ 11
3.2.1 Engle-Granger兩階段共整合檢定 .................................................. 11
3.2.2 Johansen共整合檢定 ..................................................................... 12
3.3 最適落後期數 .................................................................................... 15
3.4 混合頻率誤差修正模型 ...................................................................... 16
3.4.1 混合頻率自迴歸分佈滯後模型
(Mixed frequency autoregression distributed lag model, MF-ADL) ......... 16
3.4.2 混合頻率動態共整合 ....................................................................... 17
3.4.3 非限制(Unrestricted)之短期動態預測模型 ....................................... 19
3.5 評估預測表現 ..................................................................................... 19
3.6 Diebold-Mariano檢定 .......................................................................... 20
第四章 實證分析與結果.............................................................................. 21
4.1 資料來源與處理.................................................................................... 21
4.2 決定落後期數 ....................................................................................... 22
4.3 共整合檢定............................................................................................ 23
4.4 混合頻率誤差修正模型 ......................................................................... 24
4.4.1 估計共整合關係誤差修正項 ............................................................... 25
4.4.2 估計混合頻率誤差修正模型 ............................................................... 25
4.5 樣本內預測力比較................................................................................. 26
4.6 樣本外預測............................................................................................ 28
第五章 結論................................................................................................. 30
參考文獻..................................................................................................... 32
附錄A.......................................................................................................... 35
參考文獻 References
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