Template-type: ReDIF-Paper 1.0
Author-Name: Chia-Lin Chang
Author-Email: changchialin@nchu.edu.tw
Author-Person: pch286 
Author-Workplace-Name: NCHU Department of Applied Economics (Taiwan)
Author-Name: Philip Hans Franses
Author-Email: franses@few.eur.nl
Author-Person: pfr38 
Author-Workplace-Name: Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics), 
	Erasmus Universiteit
Author-Name: Michael McAleer
Author-Person: pmc90 
Author-Workplace-Name: Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics)
	Erasmus Universiteit, Tinbergen Instituut (Tinbergen Institute).
Title: Are Forecast Updates Progressive?
Abstract: Many macro-economic forecasts and forecast updates, such as those from the IMF and OECD, typically 
	involve both a model component, which is replicable, as well as intuition (namely, expert knowledge 
	possessed by a forecaster), which is non-replicable. . Learning from previous mistakes can affect 
	both the replicable component of a model as well as intuition. If learning, and hence forecast updates, 
	are progressive, forecast updates should generally become more accurate as the actual value is approached. 
	Otherwise, learning and forecast updates would be neutral. The paper proposes a methodology to test whether 
	macro-economic forecast updates are progressive, where the interaction between model and intuition is 
	explicitly taken into account. The data set for the empirical analysis is for Taiwan, where we have three 
	decades of quarterly data available of forecasts and their updates of two economic fundamentals, namely 
	the inflation rate and real GDP growth rate. The empirical results suggest that the forecast updates for 
	Taiwan are progressive, and that progress can be explained predominantly by improved intuition.
Classification-JEL: C53, C22, E27, E37.
Keywords: Macro-economic forecasts, econometric models, intuition, learning, progressive forecast updates, 
	forecast errors.
Length: 24 pages 
Creation-Date: 2011 
Number: 2011-03
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1103.txt
File-URL: https://eprints.ucm.es/id/eprint/12434/1/1103.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1103