The Asymmetric Loss Function and the Central Banks’ Ability in Developing Countries

2008 ◽  
Vol 37 (3) ◽  
pp. 387-403 ◽  
Author(s):  
Osama D. Sweidan
Author(s):  
Michael Frenkel ◽  
Jin-Kyu Jung ◽  
Jan-Christoph Rülke

AbstractIn this paper, we study the bias in interest rate projections of five central banks, namely the central banks of the Czech Republic, New Zealand, Norway, Sweden, and the USA. We examine whether central bank projections are based on an asymmetric loss function and report evidence that central banks perceive an overprojection of their longer-term interest rate forecasts as twice as costly as an underprojection of the same size. We find that forecast rationality is consistent with biased interest rate projections under the assumption of an asymmetric loss function, which contributes to explaining the behavior of the examined central banks and their forecasts.


2013 ◽  
Vol 28 (2) ◽  
pp. 333-343 ◽  
Author(s):  
Ulrich Fritsche ◽  
Christian Pierdzioch ◽  
Jan-Christoph Rülke ◽  
Georg Stadtmann

Author(s):  
Fuad Al-Duais ◽  
Mohammed Alhagyan

In this paper, we developed linear exponential (LINEX) loss function by emerging weights to produce weighted linear exponential (WLINEX) loss function. Then we utilized WLINEX to derive scale parameter and reliability function of the Weibull distribution based on record values when the shape parameter is known. After, we estimated scale parameter and reliability function of Weibull distribution by using maximum likelihood (ML) estimation and by several Bayes estimations.  The Bayes estimates were obtained with respect to symmetric loss function (squared error loss (SEL)), asymmetric loss function (LINEX) and asymmetric loss function (WLINEX). The ML and the different Bayes estimates are compared via a Monte Carlo simulation study. The result of simulation mentioned that the proposed WLINEX loss function is promising and can be used in real environment especially at the case of underestimate where it revealed better performance than LINEX loss function for estimating scale parameter.


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