Civil Engineering Reference
In-Depth Information
design model is much better than parametric model not only in statistical accuracy
but also in the ability of forecasting in short term. At last, a situation simulation is
put forward according to the structure of the model as an application.
References
A. Ang, G. Bekaert, M. Wei, Do macro variables, asset markets, or surveys forecast inflation better?
J. Monet. Econ. 54 , 1163-1212 (2007)
A. Atkeson, L. Ohanian, Are phillips curves useful for forecasting inflation? Federal Reserve Bank
Minneapolis Q. Rev. 25 , 2-11 (2001)
J.Q. Fan, T. Huang, Profile likelihood inferences on semiparametric varying-coefficient partially
linear models. Bernoulli 11 , 1031-1057 (2005)
S.C. Huang, T.K. Wu, Combining wavelet-based feature extractions with relevance vector ma-
chines for stock index forecasting. Expert Syst. 25 , 133-149 (2008)
P. Hall, Q. Li, J. Racine, Nonparametric estimation of regression functions in the presence of
irrelevant regressors. Rev. Econ. Stat. 89 , 784-789 (2007)
G. Koop, D. Korobilis, Forecasting inflation using dynamic model averaging. Int. Econ. Rev. 53 ,
867-886 (2012)
U. Lee, Forecasting inflation for inflation-targeted countries: A comparison of the predictive
performance of alternative inflation forecasting models. J. Bus. Econ. Stud. 18 , 75-95 (2012)
F.R. Roodposhti, M.F. Shams, H. Kordlouie, Forecasting stock price manipulation in capital
market. World Acad. Sci. Eng. Technol. 80 , 151-162 (2011)
Y.G. Shin, S.S. Park, D.S. Jang, A comparison of forecasting the index of the korean stock market.
Comput. Methods Sci. Eng. Adv. Comput. Sci. 2 , 225-228 (2009)
J. Stock, M. Watson, Forecasting inflation. J. Monetary Econ. 44 , 293-335 (1999)
J. Stock, M. Watson, Phillips Curve Inflation Forecasts. NBER Working Paper No. 14322 (2008)
K.S. Vaisla, A.K. Bhatt, An analysis of the performance of artificial neural network technique for
stock market forecasting. Int. J. Comput. Sci. Eng. 2 , 2104-2109 (2010)
Search WWH ::




Custom Search