Forecasting Stationary Processes under Asymmetric Loss
نویسندگان
چکیده
This paper addresses estimation of general linear processes under the relevant loss function using autoregressive approximations. The estimators of the autoregressive parameters are shown to be consistent in the sense that the L1 norm of the distance to the respective true values converges in probability to zero. The assumptions on the order of the approximation and the moment requirements for the innovations of the general linear process are fairly mild. The L1 consistency is shown to be a sufficient condition for asymptotic validity of the forecasts based on such autoregressive approximations. Monte Carlo simulations illustrate the asymptotic results.
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