RT Report
T1 Parameter Estimation Error in Tests of Predictive Performance under Discrete Loss Functions
A1 Eransus, Francisco Javier
A1 Novales Cinca, Alfonso Santiago
AB 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.
SN 2341-2356
YR 2014
FD 2014
LK https://hdl.handle.net/20.500.14352/41595
UL https://hdl.handle.net/20.500.14352/41595
LA eng
DS Docta Complutense
RD 29 abr 2025