Reference: | 林馨怡與廖珈燕 (2017), ”大數據預測通貨膨脹率”, working paper.
廖珈燕 (2016), ”大數據預測通貨膨脹率”, 政治大學經濟系碩士論文.
吳若瑋 (2015), ”通貨膨脹率之預測”, 經濟論文 43(2): 253-285.
Ang, A., Bekaert, G., and Wei, M., 2007. Do macro variables, asset markets, or surveys forecast inflation better? Journal of Monetary Economics, 54(4): 1163-1212.
Aparicio, D., and Bertolotto, M., 2017. Forecasting Inflation with Online Prices,working paper. Aron, J., and Muellbauer, J., 2013. New methods for forecasting inflation: Applied to the US. Oxford Bulletin of Economics and Statistics 75(5): 637-661.
Banbura, M., and Modugno, M., 2010. Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data. European Central Bank Working paper,1189.
Breitung, J., and Roling, C., 2015. Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting 34(7): 588-603.
Carriere-Swallow, Y., and Labbe, F. 2013. Nowcasting with Google trends in an emerging market. Journal of Forecasting 32(4): 289-298.
Carriero, A., Clark, T.E., and Marcellino, M., 2015. Realtime nowcasting with a bayesian mixed frequency model with stochastic volatility. Journal of the Royal Statistical Society 178(4): 837-862.
Clements, M.P., and Galva o, A.B., 2008. Macroeconomic forecasting with mixed frequency data: Forecasting output growth in the United States. Journal of Business and Economic Statistics 26(4): 546–554.
Cristadoro, R., Saporito, G.,and Venditti, F. 2008. Forecasting inflation and tracking monetary policy in the euro area: Does national information help? ECB Working Paper, No.900.
Diebold, F. X., and Mariano, R. S., 1995. Comparing predictive accuracy. Journal of Business & Economic Statistics 13(3): 253–263.
Filippo, G.D. 2015. Dynamic model averaging and CPI inflation forecasts: A comparison between the Euro area and the United States. Journal of Forecasting 34: 619-648.
Garcia, M.G.P., Medeiros, M.C., and Vasconcelos, G.F.R. 2017. Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting 33: 679-693.
Giannone, D., Lenza, M., Momferatou, D., and Onorante, L. 2014. Short-term inflation projections: A bayesian vector autoregressive approach. International Journal of Forecasting 30(3): 635- 644.
Ghysels, E., Santa-Clara, P.,and Valkanov, R. 2004. The MIDAS touch: Mixed data sampling regression models. CIRANO Working Papers 2004s-20, CIRANO.
Lenza, M. and Thomas, T. 2011. A factor model for Euro-area short-term inflation analysis Journal of Economics and Statistics 231(1): 50-62.
Modugno, M. 2013. Now-casting inflation using high frequency data. International Journal of Forecasting 29(4): 664-675.
Monteforte, L., and Moretti, G. 2013. Real time forecasts of inflation: The role of financial variables. Journal of Forecasting 32(1): 51-61.
Higgins, P., Zha, T.,and Zhong, W. 2016. Forecasting China’s economic growth and inflation. China Economic Review 41: 46-61.
Seabold, S. 2015. Nowcasting prices using Google trends: An application to central America. World Bank Policy Research Working Paper, No.7398.
Smith,P. 2016. Google’s MIDAS touch: Predicting UK unemployment with internet search data. Journal of Forecasting 35(3): 263-284.
Stock, J. H., and Watson, M. W. 1999. Forecasting inflation. Journal of Monetary Economics 44: 293-335.
Tibshirani, R. 1996. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society Series B (Statistical Methodology), 58: 267-288.
Zou, H. and Hastie,T. Regularization and variable selection via the elastic net Journal of the Royal Statistical Society Series B(Statistical Methodology), 67(2): 301-320. |