Pooling forecasts in linear rational expectations models
Gregor Smith
Journal of Economic Dynamics and Control, 2009, vol. 33, issue 11, 1858-1866
Abstract:
Estimating linear rational expectations models in a limited-information setting requires replacing the expectations of future, endogenous variables either with instrumented, actual values or with forecast survey data. Applying the method of Gottfries and Persson [Empirical examinations of the information sets of economic agents. Quarterly Journal of Economics 103, 251-259], I show how to augment these methods with actual, future values of the endogenous variables to improve statistical efficiency. The method is illustrated with an application to the US hybrid new Keynesian Phillips curve, where traditional, lagged instruments and the median forecast from the Survey of Professional Forecasters both appear to miss significant information used by price-setters, so that forecast pooling with actual values improves the statistical fit to inflation.
Keywords: Forecast; pooling; Recursive; projection; New; Keynesian; Phillips; curve (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165-1889(09)00103-1
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Pooling Forecasts In Linear Rational Expectations Models (2007) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:33:y:2009:i:11:p:1858-1866
Access Statistics for this article
Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok
More articles in Journal of Economic Dynamics and Control from Elsevier
Bibliographic data for series maintained by Catherine Liu ().