台灣領先指標對景氣循環預測能力的探討

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：16

、訪客IP：3.133.145.211

姓名

黃聿銘(Yu-Ming Huang) 查詢紙本館藏

畢業系所

經濟學系

論文名稱

台灣領先指標對景氣循環預測能力的探討
(The Research of Business Cycle forecastability of Taiwan Leading Indicator)

相關論文

★ 我國LED照明產業技術進步之研究:可計算一般均衡模型之應用	★ 亞洲國家石油消費之實證研究
★ 1999-2010年影響臺灣家庭貧窮現象之因素	★ 台灣區域GDP估計與都會區發展研究
★ 從產業關聯表檢視美國再工業化成效	★ 從產業關聯表檢視中國大陸進口替代之情形
★ 事業廢棄物清除處理之分析：綠色國民所得帳與CGE模型之應用	★ 空氣污染防制政策與綠色國民所得之一般均衡分析
★ 台灣水污染防治效果的一般均衡分析	★ 開放體系下之資本移動性與貨幣政策目標效果
★ 解構證券交易所成功要素--跨國性研究，兼論台灣與香港證交所之競爭	★ 臺指期貨選擇權套利效率性之研究
★ 制度與經濟成長	★ 公共資本與台灣經濟成長
★ 國外波動對台灣經濟的影響：動態隨機一般均衡模型之應用	★ 三篇能源經濟的應用

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

為驗證現行景氣領先指標構成項目是否能有效預測景氣循環，本文針對行政院經濟建設委員會發佈的七項景氣領先指標構成項目進行實證分析。
為避免景氣領先指標構成項目受到短期偶發事件，或無法預測景氣循環之因素的影響，本文使用由黃鍔所提出的Hilbert-Huang Transform (HHT)方法中的經驗模態分析法 (Empirical Mode Decomposition)，將最短期波動從變數中抽離出來，以構成新的景氣領先指標內容。
實證結果發現，在落後期數較短VAR模型下，七個領先指標構成項目當中，只有製造業存貨量指數與核發建照面積 (住宅、商、辦及工業倉儲)兩項領先指標構成項目在Granger因果關係檢定上，屬於有效的領先指標。在落後期數較長VAR模型下，七項領先指標構成項目在Granger因果關係檢定的表現很差，均無法做為景氣循環領先指標。
本文修正後的七個領先指標構成項目，在落後期數較短VAR模型下，外銷訂單指數、製造業存貨量指數及股價指數等領先指標構成項目在Granger因果關係檢定上，屬於有效的領先指標，在落後期數較長VAR模型下，本文修正後的七個領先指標構成項目，在Granger因果關係檢定表現良好，均能當作景氣循環的領先指標。
本文最後使用樣本外預測力來進行修正後領先指標構成項目的預測評估，研究結果支持本文建議的新景氣領先指標構成項目，其對景氣循環的預測準確性較佳。

摘要(英)

In this paper, we examine whether the current business leading indicator components can forecast business cycle effectively. This study proceeds with empirical analysis about seven business leading indicator components and business cycle published by CEPD (Council for Economic Planning and Development).
In order to prevent business leading indicator components from the influence of short-term incidents and factors which can not forecast business cycle, this paper uses the Empirical Mode Decomposition, the primary method of Hilbert-Huang Transform developed by Huang, extracts the shortest-term fluctuations from the variables, and then compose new business leading indicator components. As a result, we find that there are only two leading indicator components, Index of Producer’s Inventory and Building Permit (including housing, mercantile, business and service, industrial warehousing), in empirical analysis under shorter lag periods VAR model, are effective leading indicators in Granger causality test. While under longer lag periods VAR model, all of the seven leading indicator components behave poor in Granger causality test.
However, under shorter lag periods VAR model, there are three leading indicator components (including Index of Export Orders, Index of Producer’s Inventory, and Stock Price Index) are effective leading indicators in Granger causality test within seven modified leading indicator components recommended by this paper. Under longer lag periods VAR model, all of the seven leading indicator components behave well in Granger causality test, which can be business leading indicators.
Finally, this paper uses out-of-sample test to evaluate the modified leading indicator components. This result proves that new business leading indicator components suggested by this paper forecast business cycle more accurately.

關鍵字(中)

★ 景氣循環
★ 經驗模態分析法
★ 預測評估
★ 景氣領先指標
★ Granger 因果關係檢定

關鍵字(英)

★ Business cycle
★ Granger causality test
★ Empirical mode decomposition
★ Business leading indicator
★ forecast evaluation.

論文目次

目　　　　錄
目錄-----------------------------------------------------Ⅰ
表次----------------------------------------------------Ⅱ
圖次----------------------------------------------------Ⅲ
第一章緒論----------------------------------------------01
第一節研究背景與研究動機-------------------------01
第二節研究目的-----------------------------------04
第二章文獻回顧------------------------------------------05
第三章研究方法------------------------------------------12
第一節 ADF單根檢定--------------------------------13
第二節 KPSS檢定-----------------------------------16
第三節 VAR (Vector autoregression) 向量自我迴歸模型--19
第四節 Granger causality test因果關係檢定----------------20
第五節經驗模態分析法 (Empirical Mode Decomposition)-----22
第六節預測評估 (forecast evaluation)--------------------25
第七節 Diebold-Mariano 檢定------------------------------27
第四章實證分析------------------------------------------29
第一節資料來源與變數說明--------------------------------29
第二節單根與定態檢定------------------------------------33
第三節向量自我迴歸模型與因果關係檢定--------------------36
第四節領先指標綜合指數構成項目修正----------------------39
第五節預測評估------------------------------------------42
第五章結論----------------------------------------------46
參考文獻-------------------------------------------------48

參考文獻

一、中文部分：
1.行政院經濟建設委員會經濟研究處 (2007a)，《景氣指標及對策信號修正－新聞稿及簡報》。
2.行政院經濟建設委員會經濟研究處 (2007b)，《景氣指標及對策信號修正－新景氣指標編制說明》。
3.利秀蘭、陳惠薇 (2005)，台灣景氣領先及同時指標之探討，《經濟研究》93.12，27-54。
4.林大超 (2003)，《台灣與美國兩地景氣循環指標關聯性之研究》，未出版碩士論文，國立成功大學企業管理學系。
5.胡經芳 (2002)，景氣動向指標檢討及修訂之研究，《台灣銀行研究月刊》，79-103。
6.陳旭昇 (2007)，《時間序列分析－總體經濟與財務金融之應用》，台北：東華書局。
7.黃台心 (2002)，出口與經濟成長的因果關係：台灣的實證研究，《經濟論文叢刊》30卷4期，465-490。
8.楊奕農 (2005)，《時間序列分析－經濟與財務上之應用》，台北：雙葉書廊。
9.劉茂亮 (2003) ，《金融變數與經濟成長之關係》，未出版碩士論文，輔仁大學金融研究所。
二、英文部分：
1.Akaike, H. (1973), “ Information Theory and an Extension of the Maximum Likelihood Principle”, In Petrov, B. N. and Csaki, F, eds. Second International Symposium on Information Theory. Budapest: Akademiai Kiado, 267–281.
2.Banerjee, A., M. Marcellino and I. Masten (2006), “Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth?”, International Journal of Forecasting , 22: 137-151..
3.Banerjee, A., M. Marcellino and I. Masten (2005), “Leading Indicators for Euro-area Inflation and GDP Growth”, Oxford Bulletin of Economics and Statistics 67: 785-813.
4.Camacho, M. and G. Perez-Quiros (2002), “This is What the Leading Indicators Lead”, Journal of Applied Econometrics 17: 61-80.
5.Camba-Mendez, G., G. Kapetanios, R. J. Smith and M. R. Weale (2001), “An Automatic Leading Indicator of Economic Activity: Forecasting GDP Growth for European Countries”, Econometrics Journal 4: 56-90.
6.Canova, F. (1998), “Detrending and Business Cycle Facts”, Journal of Monetary Economics 41: 475- 512.
7.Cecchetti, S. G., R. S. Chu and C. Steindel (2000), “The Unreliability of Inflation Indicators”, Current Issues in Economics and Finance 6: 1-6.
8.Cheung, Y.-W. and M. D. Chinn (1996), “Further Investigation of the Uncertain Unit Root in GNP”, Journal of Business & Economic Statistics 15: 68-73.
9.DeJong, D. N., J.C. Nankervis, N.E. Savin and C. H. Whiteman (1992), “Integration Versus Trend Stationary in Time Series ”, Econometrica 60: 423-433.
10.Dickey, D. and W. Fuller (1979), “ Distribution of The Estimators for Autoregressive Time Series”, Journal of the American Statistical Association 74: 427–431.
11.Dickey, D. A. and W. A. Fuller (1981), “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root”, Econometrica 49: 1057-1072.
12.Diebold, F. X. and G. D. Rudebusch (1991), “On the Power of Dickey-Fuller Tests Against Fractional Alternatives”, Economics Letters 35: 115-160.
13.Diebold, F. X. and R. S. Mariano (1995), “Comparing Predictive Accuracy”, Journal of Business and Economic Statistics 13: 253-265.
14.Granger, C. W. J. (1969), “Investigating Causal Relations by Econometric Model and Cross-spectral Methods”, Econometrica 37: 424-438.
15.Granger, C. and P. Newbold (1974), “Superious Regression in Econometrics”, Journal of Econometrics 2: 111-120.
16.Hsiao, C. (1981),”Autoregressive Modelling and Money-Income Causality Detection”, Journal of Monetary Economics 7: 85–106.
17.Huang, N. E., Z. Shen, S. R. Long, M.C. Wu, E. H. Shih,Q. Zheng, C. C. Tung and H. H. Liu (1998), “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis”, Proceedings: Mathematical, Physical and Engineering Sciences 454: 903-995.
18.Huang, N. E., M. L. Wu, W. Qu, S. R. Long and S. S. P. Shen (2003), “Applications of Hilbert-Huang Transform to Non-Stationary Financial Time Series Analysis”, Applied Stochastic Models in Business and Industry 19: 245-268.
19.Kawitkowski, D., P. C. B. Phillips, P. Schimidt and Y. Shin (1992), “Testing the Null Hypothesis of Stationary against the Alternative of a Unit Root”, Journal of Econometrics 54: 159-178.
20.Kim, K. and Y.-Y. Choi (1997), “Business Cycle in Korea is There Any Stylized Feature？”, Journal of Economics Studies 24: 275-293.
21.Liu, G. D., R. Gupta and E. Schaling (2008), “Forecasting the South African Economy: A DSGE-VAR Approach”, CentER Discussion Paper, No. 2008-32.
22.Lui, X. and C. Shu (2004), “Consumption and Stock Markets in Greater China”, Applied Economics Letters 11: 365-368.
23.Marcellino, M., J. H. Stock, and M. W. Watson (2003), “Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information”, European Economic Review 47: 1-18.
24.Monetary Authority of Singapore (2004), “Using Leading Indicators to Forecast the Singapore Electronics Industry”, MAS Staff Paper No. 30.
25.Mookerjee, R. and Q. Yu (1997), “Macroeconomic Variables and Stock Prices in a Small Open Economy: The Case of Singapore”, Pacific-Basin Finance Journal 5: 377-388.
26.Nabeya, S. and K. Tanaka (1988), “ Asymptotic Theory of a Test for the Constancy of Regression Coefficient Against the Random Walk Alternative” , Annals of Statistics 16: 218-235.
27.Nelson, C. R. and C.I. Plosser (1982), “Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications”, Journal of Monetary Economics 10: 139-162.
28.Newey, W. K. and K. D. West (1987), “A Simple-Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix”, Econometrica 55: 703-708.
29.Phillips, P. C. B. and P. Perron (1988), “ Testing for a Unit Root in Time Series Regression”, Biometrika 75: 335-346.
30.Schwarz, G.(1978), “Estimating the Dimension of a Model”, The Annals of Statistics 6: 461-464.
31.Sims, C. A. (1980), “Macroeconomics and Reality”, Econometrica 48: 1-48.
32.Stock, J. H. and M. W. Watson (1989), “New Indexes of Coincident and Leading Economic Indicators”, NBER macroeconomics annual : 351-394.
33.Thornton, J. (1993), “Money, Output and Stock Price in the UK: Evidence on Some Non-Relationships”, Applied Financial Economics 3: 335-338.
34.Toda, H. Y. and T. Yamamoto (1995), “Statistical Inference in Vector Autoregressions with Possibly Integrated Processes”, Journal of Econometrics 66: 225-250.
35.Toda, H. Y. and P. C. B. Phillips (1993), “Vector Autoregressions and Causality”, Econometrica 61: 1367-1393.
36.Toda, H. Y. and P. C. B. Phillips (1994), “Vector Autoregressions and Causality: a Theoretical Overview and Simulation Study”, Econometric Reviews 13: 259-285.
37.Walter, E. (2004), Applied Econometric Time Series, 2nd ed., New York: Wiley.
38.Wongbangpo, P. and S. C. Sharma (2002), “Stock Market and Macroeconomic Fundamental Dynamic Interations：ASEAN-5 Countries”, Journal of Asian Economics 13: 27-51.

指導教授

朱雲鵬(Yun-Peng Chu)

審核日期

2008-7-20

推文