Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/154013
Authors: 
Granziera, Eleonora
Hubrich, Kirstin
Moon, Hyungsik Roger
Year of Publication: 
2013
Series/Report no.: 
ECB Working Paper 1580
Abstract: 
In this paper we introduce Quasi Likelihood Ratio tests for one sided multivariate hypotheses to evaluate the null that a parsimonious model performs equally well as a small number of models which nest the benchmark. We show that the limiting distributions of the test statistics are non standard. For critical values we consider two approaches: (i) bootstrapping and (ii) simulations assuming normality of the mean square prediction error (MSPE) difference. The size and the power performance of the tests are compared via Monte Carlo experiments with existing equal and superior predictive ability tests for multiple model comparison. We find that our proposed tests are well sized for one step ahead as well as for multi-step ahead forecasts when critical values are bootstrapped. The experiments on the power reveal that the superior predictive ability test performs last while the ranking between the quasi likelihood-ratio test and the other equal predictive ability tests depends on the simulation settings. Last, we apply our test to draw conclusions about the predictive ability of a Phillips type curve for the US core inflation.
Subjects: 
direct multi-step forecasts
fixed regressors bootstrap.
multi-model comparison
Out-of sample
point-forecast evaluation
predictive ability
Document Type: 
Working Paper

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