Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/94239
Authors: 
Kihwan, Kim
Swanson, Norman
Year of Publication: 
2013
Series/Report no.: 
Working Papers, Department of Economics, Rutgers, The State University of New Jersey 2013-15
Abstract: 
In this chapter, we discuss the use of mixed frequency models and diffusion index approximation methods in the context of prediction. In particular, select recent specification and estimation methods are outlined, and an empirical illustration is provided wherein U.S. unemployment forecasts are constructed using both classical principal components based diffusion indexes as well as using a combination of diffusion indexes and factors formed using small mixed frequency datasets. Preliminary evidence that mixed frequency based forecasting models yield improvements over standard fixed frequency models is presented.
Subjects: 
forecasting
diffusion index
mixed frequency
recursive estimation
Kalman filter
JEL: 
C22
C51
Document Type: 
Working Paper

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