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題名 大數據預測通貨膨脹率
Forecasting Inflation with Big Data
作者 廖珈燕
Liao, Jia Yan
貢獻者 林馨怡
Lin, Hsin Yi
廖珈燕
Liao, Jia Yan
關鍵詞 Google trends 關鍵字
通貨膨脹率
Google trends
Inflation
日期 2016
上傳時間 3-Aug-2016 10:27:27 (UTC+8)
摘要 本文主要是透過 Google trends 網站提供的關鍵字搜尋量資料，
探討網路資料是否能夠提供通貨膨脹率的即時資訊。
透過美國消費者物價指數的組成細項作為依據，蒐集美國2004年1月至2015年12月的 Google trends 關鍵字變數，並藉由最小絕對壓縮挑選機制(Least absolute shrinkage and selection operator)、
彈性網絡(Elastic Net)以及主成分分析法(Principal component analysis)等等變數挑選機制，有效地整合大量的關鍵字資料。實證結果發現，透過適當變數挑選後的 Google trends 關鍵字變數確實可改善美國通貨膨脹率的即時預測表現，並為美國通貨膨脹率提供額外有效的資訊。此外，我們透過台灣的關鍵字資料檢驗，也確認Google trends 關鍵字資料可以幫助台灣通貨膨脹率的即時預測。
參考文獻 Ang, A., Bekaert, G., Wei, M. 2007.
Do macro variables, asset markets, or surveys forecast inflation better?
Journal of Monetary Economics , 54(4), 1163--1212.


Askitas, N., Zimmermann, K. F. 2009.
Google econometrics and unemployment forecasting.
Applied Economics Quarterly , 55(2), 107--120.


Atkeson, A., Ohanian, L.E. 2001.
Are Phillips curves useful for forecasting inflation?
Federal Reserve Bank of Minneapolis Quarterly Review, 25, 2--11.


Bai, J., Ng, S. 2002.
Determining the Number of factors in approximate factor models.
Econometrica , 70(1), 191--221.


Bai, J., Ng, S. 2007.
Determining the number of primitive shocks in factor models.
Journal of Business \\& Economic Statistics , 25(1), 52--60.


Basu, S., Michailidis, G. 2015.
Regularized estimation in sparse high-dimensional time series models.
The Annals of Statistics , 43(4), 1535--1567.


Bernanke, B. S., Boivin, J., Eliasz, P. 2005.
Measuring the effects of monetary policy: A factor augmented vector autoregressive (FAVAR) approach.
Quarterly Journal of Economics , 120, 387--422.


Carriere-Swallow, Y., Labbe, F. 2013.
Nowcasting with Google trends in an emerging market.
Journal of Forecasting , 32(4), 289--298.


Cavallo, A. 2013.
Online and official price indexes: Measuring Argentina’s inflation.
Journal of Monetary Economics , 60(2), 152--165.



Cecchetti, S., Chu, R., Steindel, C., 2000.
The unreliability of inflation indicators.
Federal Reserve Bank of New York Current Issues in Economics and Finance , 6, 1--6.


Chen, Y., Turnovsky, S. J., Zivot, E. 2014.
Forecasting inflation using commodity price aggregates.
Journal of Econometrics , 183(1), 117--134.


Cheung, C. 2009.
Are commodity prices useful leading indicators of inflation??
Bank of Canada Discussion Paper .


Choi, H., Varian, H. 2012.
Predicting the present with Google Trends.
Economic Record , 88(SUPPL.1), 2--9.


Clark, T. E., Mccracken, M. W. 2001.
Tests of equal forecast accuracy and encompassing for nested models.
Journal of Econometrics , 105, 85--110.


Clark, T. E., West, K. D. 2007.
Approximately normal tests for equal predictive accuracy in nested models.
Journal of Econometrics , 138(1), 291--311.


Diebold, F. X., Mariano, R. S. 1995.
Comparing predictive accuracy.
Journal of Business \\& Economic Statistics , 13(3), 253--263


Fama, E.F., Gibbons, M.R. 1984.
A comparison of inflation forecasts.
Journal of Monetary Economics , 13, 327--348.


Fisher, J.D.M., Liu, C.T., Zhou, R. 2002.
When can we forecast inflation?
Federal Reserve Bank of Chicago Economic Perspectives , 1, 30--42.


Furlong, F., Ingenito, R. 1996.
Commodity prices and inflation.
Federal Reserve Bank of San Francisco Economic Review , 27--47.


Guzman, G. 2011.
Internet search behavior as an economic forecasting tool: The case of inflation expectations.
Journal of Economic and Social Measurement , 36(3), 119--167.


Hoerl, A.E. 1962.
Application of ridge analysis to regression problems.
Chemical Engineering Progress , 58, 54--59.



Hoerl, A.E., Kennard, R.W. 1970.
Ridge regression: Biased estimation for nonorthogonal problems.
Technometrics , 12(1), 55--67.


Knotek, E. S., Zaman, S. 2014.
Nowcasting U.S. headline and core inflation.
Cleveland Fed Working Paper, No.14-03R


Mahdavi, S., Zhou, S. 1997.
Gold and commodity prices as leading indicators of inflation: Tests of long-run relationship and predictive performance.
Journal of Economics and Business , 49(5), 475--489.


Seabold, S. 2015.
Nowcasting prices using Google Trends: An application to central America.
World Bank Policy Research Working Paper, No. 7398


Stock, J., Watson, M. W. 1998.
Diffusion indexes.
NBER Working Paper, No. 6702


Stock, J., Watson, M. W.
Forecasting inflation.
Journal of Monetary Economics , 44, 293--335.


Stock, J. H., Watson, M. W. 2002.
Macroeconomic forecasting using diffusion indexes.
Journal of Business \\& Economic Statistics , 20(2), 147--162.


Stock, J. H., Watson, M. W. 2003.
Forecasting output and inflation: The role of asset prices.
Journal of Economic Literature , 41, 788--829.


Stockton, D., Glassman, J. 1987.
An evaluation of the forecast performance of alternative models of inflation.
Review of Economics and Statistics , 69, 108--117.


Tibshirani, R. 1996.
Regression shrinkage and selection via the Lasso.
Royal Statistical Society , 58(1), 267--288.


Varian, H. 2014.
Big data: New tricks for econometrics.
The Journal of Economic Perspectives , 1--36.


Vosen, S., Schmidt, T. 2011.
Forecasting private consumption: Survey-based indicators vs. Google trends.
Journal of Forecasting , 30(6), 565--578.


Zou, H., Hastie,T. 2005.
Regularization and variable selection via the elastic net.
Royal Statistical Society , 67(2), 301--320.
描述 碩士
國立政治大學
經濟學系
103258016
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103258016
資料類型 thesis
dc.contributor.advisor 林馨怡zh_TW
dc.contributor.advisor Lin, Hsin Yien_US
dc.contributor.author (Authors) 廖珈燕zh_TW
dc.contributor.author (Authors) Liao, Jia Yanen_US
dc.creator (作者) 廖珈燕zh_TW
dc.creator (作者) Liao, Jia Yanen_US
dc.date (日期) 2016en_US
dc.date.accessioned 3-Aug-2016 10:27:27 (UTC+8)-
dc.date.available 3-Aug-2016 10:27:27 (UTC+8)-
dc.date.issued (上傳時間) 3-Aug-2016 10:27:27 (UTC+8)-
dc.identifier (Other Identifiers) G0103258016en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/99637-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟學系zh_TW
dc.description (描述) 103258016zh_TW
dc.description.abstract (摘要) 本文主要是透過 Google trends 網站提供的關鍵字搜尋量資料，
探討網路資料是否能夠提供通貨膨脹率的即時資訊。
透過美國消費者物價指數的組成細項作為依據，蒐集美國2004年1月至2015年12月的 Google trends 關鍵字變數，並藉由最小絕對壓縮挑選機制(Least absolute shrinkage and selection operator)、
彈性網絡(Elastic Net)以及主成分分析法(Principal component analysis)等等變數挑選機制，有效地整合大量的關鍵字資料。實證結果發現，透過適當變數挑選後的 Google trends 關鍵字變數確實可改善美國通貨膨脹率的即時預測表現，並為美國通貨膨脹率提供額外有效的資訊。此外，我們透過台灣的關鍵字資料檢驗，也確認Google trends 關鍵字資料可以幫助台灣通貨膨脹率的即時預測。		zh_TW
dc.description.tableofcontents 1.緒論 1
2 大數據 3
2.1 資料探勘 3
2.2 高維度問題 4
3 文獻回顧 8
3.1 當下量測 8
3.2 預測模型建立及評估 12
3.3 通膨預測 16
4 美國實證結果 21
4.1 美國通貨膨脹資料 21
4.2 Google trends 關鍵字指標建構 21
4.3 模型估計結果 24
4.4 樣本外預測能力評估 25
4.5 日資料 33
5 台灣實證結果 38
5.1 台灣通貨膨脹資料 38
5.2 Google trends 關鍵字指標建構 38
5.3 模型估計結果 39
5.4 樣本外預測能力評估 39
5.5 日資料 45
6 結論 50
參考文獻 51
附錄 54		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103258016en_US
dc.subject (關鍵詞) Google trends 關鍵字zh_TW
dc.subject (關鍵詞) 通貨膨脹率zh_TW
dc.subject (關鍵詞) Google trendsen_US
dc.subject (關鍵詞) Inflationen_US
dc.title (題名) 大數據預測通貨膨脹率zh_TW
dc.title (題名) Forecasting Inflation with Big Dataen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ang, A., Bekaert, G., Wei, M. 2007.
Do macro variables, asset markets, or surveys forecast inflation better?
Journal of Monetary Economics , 54(4), 1163--1212.


Askitas, N., Zimmermann, K. F. 2009.
Google econometrics and unemployment forecasting.
Applied Economics Quarterly , 55(2), 107--120.


Atkeson, A., Ohanian, L.E. 2001.
Are Phillips curves useful for forecasting inflation?
Federal Reserve Bank of Minneapolis Quarterly Review, 25, 2--11.


Bai, J., Ng, S. 2002.
Determining the Number of factors in approximate factor models.
Econometrica , 70(1), 191--221.


Bai, J., Ng, S. 2007.
Determining the number of primitive shocks in factor models.
Journal of Business \\& Economic Statistics , 25(1), 52--60.


Basu, S., Michailidis, G. 2015.
Regularized estimation in sparse high-dimensional time series models.
The Annals of Statistics , 43(4), 1535--1567.


Bernanke, B. S., Boivin, J., Eliasz, P. 2005.
Measuring the effects of monetary policy: A factor augmented vector autoregressive (FAVAR) approach.
Quarterly Journal of Economics , 120, 387--422.


Carriere-Swallow, Y., Labbe, F. 2013.
Nowcasting with Google trends in an emerging market.
Journal of Forecasting , 32(4), 289--298.


Cavallo, A. 2013.
Online and official price indexes: Measuring Argentina’s inflation.
Journal of Monetary Economics , 60(2), 152--165.



Cecchetti, S., Chu, R., Steindel, C., 2000.
The unreliability of inflation indicators.
Federal Reserve Bank of New York Current Issues in Economics and Finance , 6, 1--6.


Chen, Y., Turnovsky, S. J., Zivot, E. 2014.
Forecasting inflation using commodity price aggregates.
Journal of Econometrics , 183(1), 117--134.


Cheung, C. 2009.
Are commodity prices useful leading indicators of inflation??
Bank of Canada Discussion Paper .


Choi, H., Varian, H. 2012.
Predicting the present with Google Trends.
Economic Record , 88(SUPPL.1), 2--9.


Clark, T. E., Mccracken, M. W. 2001.
Tests of equal forecast accuracy and encompassing for nested models.
Journal of Econometrics , 105, 85--110.


Clark, T. E., West, K. D. 2007.
Approximately normal tests for equal predictive accuracy in nested models.
Journal of Econometrics , 138(1), 291--311.


Diebold, F. X., Mariano, R. S. 1995.
Comparing predictive accuracy.
Journal of Business \\& Economic Statistics , 13(3), 253--263


Fama, E.F., Gibbons, M.R. 1984.
A comparison of inflation forecasts.
Journal of Monetary Economics , 13, 327--348.


Fisher, J.D.M., Liu, C.T., Zhou, R. 2002.
When can we forecast inflation?
Federal Reserve Bank of Chicago Economic Perspectives , 1, 30--42.


Furlong, F., Ingenito, R. 1996.
Commodity prices and inflation.
Federal Reserve Bank of San Francisco Economic Review , 27--47.


Guzman, G. 2011.
Internet search behavior as an economic forecasting tool: The case of inflation expectations.
Journal of Economic and Social Measurement , 36(3), 119--167.


Hoerl, A.E. 1962.
Application of ridge analysis to regression problems.
Chemical Engineering Progress , 58, 54--59.



Hoerl, A.E., Kennard, R.W. 1970.
Ridge regression: Biased estimation for nonorthogonal problems.
Technometrics , 12(1), 55--67.


Knotek, E. S., Zaman, S. 2014.
Nowcasting U.S. headline and core inflation.
Cleveland Fed Working Paper, No.14-03R


Mahdavi, S., Zhou, S. 1997.
Gold and commodity prices as leading indicators of inflation: Tests of long-run relationship and predictive performance.
Journal of Economics and Business , 49(5), 475--489.


Seabold, S. 2015.
Nowcasting prices using Google Trends: An application to central America.
World Bank Policy Research Working Paper, No. 7398


Stock, J., Watson, M. W. 1998.
Diffusion indexes.
NBER Working Paper, No. 6702


Stock, J., Watson, M. W.
Forecasting inflation.
Journal of Monetary Economics , 44, 293--335.


Stock, J. H., Watson, M. W. 2002.
Macroeconomic forecasting using diffusion indexes.
Journal of Business \\& Economic Statistics , 20(2), 147--162.


Stock, J. H., Watson, M. W. 2003.
Forecasting output and inflation: The role of asset prices.
Journal of Economic Literature , 41, 788--829.


Stockton, D., Glassman, J. 1987.
An evaluation of the forecast performance of alternative models of inflation.
Review of Economics and Statistics , 69, 108--117.


Tibshirani, R. 1996.
Regression shrinkage and selection via the Lasso.
Royal Statistical Society , 58(1), 267--288.


Varian, H. 2014.
Big data: New tricks for econometrics.
The Journal of Economic Perspectives , 1--36.


Vosen, S., Schmidt, T. 2011.
Forecasting private consumption: Survey-based indicators vs. Google trends.
Journal of Forecasting , 30(6), 565--578.


Zou, H., Hastie,T. 2005.
Regularization and variable selection via the elastic net.
Royal Statistical Society , 67(2), 301--320.		zh_TW