This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range.  The ACPS is of general use in any situation where the decision maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry  are illustrated. Then, the proposed score is applied to assess and compare density forecasts of macroeconomic relevant datasets ( unemployment rate ) and of commodity prices (oil and electricity prices) with a particular focus on the recent COVID crisis period.
This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range. A test is also introduced to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets ( US employment growth ) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.
A
A test is also introduced to statistically compare the predictive ability of different forecasts.
A
in the ACPS
R
is
and test are
R
unemployment rate
US employment growth
D
a
R
COVID
COVID-19