forecaster’s loss function is unknown but testable restrictions can be imposed on the data generating process, trading off … conditions on the data generating process against conditions on the loss function. Finally, we propose flexible parametric … estimation of the forecaster’s loss function, and obtain a test of forecast optimality via a test of over …

.e. overshooting) are weighted more in central bank"s loss function than the negative ones. It is shown that the Czech National Bank is …

bank to back out the loss function implied by the forecasts of the Federal Reserve. It finds that the cost of having …

forecaster's loss function is unknown but testable restrictions can be imposed on the data generating process, trading off … conditions on the data generating process against conditions on the loss function. Finally, we propose flexible parametric … estimation of the forecaster's loss function, and obtain a test of forecast optimality via a test of over …

loss is minimised. This approach requires a loss function, a concept closely related the notion of distance between …

parameter. A cumulative loss function is considered, which is the sum of two terms: one depends on the prior belief and the …