Otherwise, QLIKE is an asymmetric loss function
that penalizes under-prediction more heavily than over-prediction, it is more suitable for applications such as risk management and VaR forecasting, where under-prediction of volatility can be more costly than over-prediction (Sharma & Vipul, 2016).
Keywords: financial stability; financial crises; macroprudential policy; loss function
In the case of a singular loss function
, the Bayesian estimation of the hidden component can be found as a maximum a posteriori probability (MAP) estimation, which leads us to the problem of minimization of the objective function, often called the Gibbs energy function .
Kiley and Roberts derive optimal inflation targets in their two models, using three versions of a loss function
that depends on inflation and the output gap.
However, optimizing of tree regularized classifier is challenging, since both of tree regularizer and hinge loss function
in the proposed tree regularized classifier are non-differentiable.
In Bayesian statistical inference, the loss function
plays an important role, and symmetric loss function
, such as the squared error loss L([?
The expected value of the loss function
is called the risk functional R(w):
The other integral part of Bayesian inference is the choice of loss function
S)], the minimization empirical risk over loss function
l([degrees]) with parameter vector d can be expressed as follows:
Mathematically, it is equivalent to the assumption that both the ceded-loss functions
and the retained loss function
The theoretical basis of linear Taylor rule rests on two key assumptions, namely that central banks have quadratic loss function
and that the Phillips curve is linear.
t,m,h]) denotes the value of a given loss function
of the forecast error at time t of country j for maturity m = 3-month, 10-year, and forecast horizons (in months) h = 3-month, 12-month.