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研究生:江昀融
研究生(外文):Yun-jung Chiang
論文名稱:以混合頻率誤差修正模型預測中國國內生產毛額
論文名稱(外文):Forecasting Gross Domestic Product of China with Mixed Frequency Error Correction Model
指導教授:李慶男李慶男引用關係
指導教授(外文):Lee, Ching-nun
學位類別:碩士
校院名稱:國立中山大學
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:2017
畢業學年度:105
語文別:中文
論文頁數:43
中文關鍵詞:共整合預測中國國內生產毛額單根混合頻率誤差修正模型
外文關鍵詞:Gross Domestic Product of ChinaForecastingUnit RootCo-integrationMixed Frequency Error Correction Model
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本文採用Götz et al. (2014) 的混合頻率誤差修正模型(Mixed Frequency Error Correction Model, MF-ECM) 為架構,對中國國內生產毛額(Gross Domestic Product, GDP) 進行預測。
混合頻率誤差修正模型最大的優點是能夠改善誤差修正模型(Error Correction Model, ECM) 透過加總或平均將不同頻率的數據轉變成相同頻率而造成的資訊流失,此模型將動態混合頻率共整合關係項加進具有混合頻率數據採樣的分布滯後模型,寫成誤差修正模型的形式,以期能提高預測精準度。
本文使用的樣本資料為中國國內生產毛額1995 年第一季到2016 年第四季及中國進、出口1995 年1 月至2016 年12 月,運用混合頻率誤差修正模型和誤差修正模型,以中國進、出口資料對中國國內生產毛額進行樣本內預測,比較兩個模型的預測力指標,發現混合頻率誤差修正模型的預測精準度比誤差修正模型高,最後我們提供2017 年第一季中國國內生產毛額的樣本外預測值作為參考。
In this paper, we use the mixed frequency error correction model of Götz et al. (2014)
as a framework for forecasting the gross domestic product (GDP) of China. The biggest
advantage of the mixed frequency error correction model is its ability to improve the error
correction model (ECM).
The loss of information is caused by converting the data of different frequencies into
the same frequency by adding or averaging. This model adds the dynamic mixing frequency
co-integration relation term to the autorgressive distributed lag model with mixed
frequency data sampling and writes the form of error correction model in order to improve
prediction accuracy.
The sample data used in this paper are gross domestic product in China from the first
quarter of 1995 to the fourth quarter of 2016, and the national import and export from January
1995 to December 2016. Using the mixed frequency error correction model and the
error correction model, the prediction indicators of both models were compared by conducting
intra-sample predictions on China’s gross domestic product and import-export
data. It was discovered that the prediction accuracy of the mixed frequency error correction
model was higher than that of the error correction model.
Finally, we provided a sample prediction for the gross domestic product (GDP) for the
first quarter of 2017 as reference.
論文審定書i
謝辭ii
摘要iii
Abstract iv
目錄v
圖次vii
表次viii
1 緒論1
1.1 研究動機及目的. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 文獻回顧3
2.1 計量文獻回顧. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 理論文獻回顧. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 實證文獻回顧. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 研究方法9
3.1 ADF 單根檢定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 共整合檢定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2.1 共整合關係. . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.2 Engle - Granger 兩階段共整合檢定. . . . . . . . . . . . . . . . 11
3.2.3 Johansen 共整合檢定. . . . . . . . . . . . . . . . . . . . . . . 11
3.2.4 最適落後期數選擇. . . . . . . . . . . . . . . . . . . . . . . . 15
3.3 混合頻率誤差修正模型. . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.1 以誤差修正模型表現的混合頻率自迴歸分佈滯後模型. . . . 15
3.3.2 預測模型: 不受限制的短期動態. . . . . . . . . . . . . . . . . 17
3.3.3 進入動態混合頻率共整合關係的高頻觀察值選擇. . . . . . . 18
3.4 預測力指標. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.5 Diebold - Mariano 檢定. . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 實證分析與結果21
4.1 資料處理與來源. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 落後期數選擇. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.3 共整合檢定. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.4 估計混合頻率共整合項. . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.5 估計混合頻率誤差修正模型. . . . . . . . . . . . . . . . . . . . . . . 24
4.6 估計誤差修正模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.7 樣本內預測. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.8 樣本外預測. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5 結論30
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