quantiles: Identification function for state-dependent quantiles

In PointFore: Interpretation of Point Forecasts as State-Dependent Quantiles and Expectiles

Description

Main alternative to estimating state-dependent quantiles based on the quantile identification function are state-dependent expectiles.

Usage

quantiles(x, y, stateVariable, theta, model, ...)

Arguments

x	forecast
y	realization
stateVariable	state variable
theta	model parameter to be estimated
model	model function
...	...

See Also

Other identification functions: expectiles

Examples

### estimate expectation of identification function for quantile forecasts

set.seed(1)
y <- rnorm(1000)
x <- qnorm(0.6)
# expectation of identification with quantile level 0.6 is zero
mean(quantiles(x,y,0,0.6,constant))
# expectation of identification function with different quantile level
# (0.5 is the median) is not zero
mean(quantiles(x,y,0,0.5, constant))

PointFore documentation built on May 2, 2019, 9:42 a.m.