Blockchain in financial services: Regulatory landscape and future challenges
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- Graeme Chamberlin, 2010. "Googling the present," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 4(12), pages 59-95, December.
- Nikolaos Askitas & Klaus F. Zimmermann, 2015.
"The internet as a data source for advancement in social sciences,"
International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
- Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The Internet as a Data Source for Advancement in Social Sciences," RatSWD Working Papers 248, German Data Forum (RatSWD).
- Askitas, Nikos & Zimmermann, Klaus F., 2015. "The Internet as a Data Source for Advancement in Social Sciences," IZA Discussion Papers 8899, Institute of Labor Economics (IZA).
- Karolien Lenaerts & Miroslav Beblavý & Brian Fabo, 2016.
"Prospects for utilisation of non-vacancy Internet data in labour market analysis—an overview,"
IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-18, December.
- Karolien Lenaerts & Miroslav Beblavý & Brian Fabo, 2016. "Prospects for utilisation of non-vacancy Internet data in labour market analysis—an overview," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-18, December.
- Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
- Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
- McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005.
"The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting,"
Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
- Lippi, Marco & Reichlin, Lucrezia & Hallin, Marc & Forni, Mario, 2002. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," CEPR Discussion Papers 3432, C.E.P.R. Discussion Papers.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2005. "The generalised dynamic factor model: one sided estimation and forecasting," ULB Institutional Repository 2013/10129, ULB -- Universite Libre de Bruxelles.
- Forni M. & Hallin M., 2003. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Computing in Economics and Finance 2003 143, Society for Computational Economics.
- Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
- Simeon Vosen & Torsten Schmidt, 2011.
"Forecasting private consumption: survey‐based indicators vs. Google trends,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
- Schmidt, Torsten & Vosen, Simeon, 2009. "Forecasting Private Consumption: Survey-based Indicators vs. Google Trends," Ruhr Economic Papers 155, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
- Concha Artola & Enrique Galán, 2012. "Tracking the future on the web: construction of leading indicators using internet searches," Occasional Papers 1203, Banco de España.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
- Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
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- Lütkepohl, Helmut, 2014. "Structural vector autoregressive analysis in a data rich environment: A survey," SFB 649 Discussion Papers 2014-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
More about this item
Keywords
Digital economy ; Global ; Working Paper;All these keywords.
JEL classification:
- K24 - Law and Economics - - Regulation and Business Law - - - Cyber Law
- O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
NEP fields
This paper has been announced in the following NEP Reports:- NEP-MAC-2017-03-05 (Macroeconomics)
- NEP-TUR-2017-03-05 (Tourism Economics)
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