Template-type: ReDIF-Paper 1.0
Author-Name: Francisco Javier Eransus
Author-Workplace-Name: Facultad de Ciencias Económicas y Empresariales. Universidad Complutense de Madrid.
Author-Name: Alfonso Novales Cinca
Author-Email: anovales@ccee.ucm.es
Author-Person: pno7 
Author-Homepage: https://www.ucm.es/fundamentos-analisis-economico2/novales-cinca,-alfonso
Author-Workplace-Name: Departamento de Fundamentos del Análisis Económico II (Economía Cuantitativa). Universidad Complutense de 
	Madrid.
Author-Workplace-Homepage: https://www.ucm.es/fundamentos-analisis-economico2
Title: Parameter Estimation Error in Tests of Predictive Performance under Discrete Loss Functions 
Abstract: We analyze the effect of parameter estimation error on the size of unconditional population level tests of predictive 
	ability when they are implemented under a class of loss functions we refer to as ‘discrete functions’. The analysis is 
	restricted to linear models in stationary variables. We obtain analytical results for no nested models guaranteeing 
	asymptotic irrelevance of parameter estimation error under a plausible predictive environment and three subsets of 
	discrete loss functions that seem quite appropriate for many economic applications. For nested models, we provide some 
	Monte Carlo evidence suggesting that the asymptotic distribution of the Diebold and Mariano (1995) test is relatively 
	robust to parameter estimation error in many cases if it is implemented under discrete loss functions, unlike what happens 
	under the squared forecast error or the absolute value error loss functions.
Classification-JEL: C12, C52, C53
Keywords: Parameter uncertainty; Forecast accuracy; Discrete loss function.
Length: 30 pages 
Creation-Date: 2014 
Number: 2014-22
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1422.txt
File-URL: https://eprints.ucm.es/id/eprint/26397/1/1422.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1422