ceRtainty (version 1.0.0)

certainty: Certainty equivalent computation

Description

Certainty equivalent computation

Usage

certainty(data, ival, fval, utility, wealth = 0)

Arguments

data

data.set with profit for each treatment/project. Each column is a treatment and each row a different profit observation.

ival

The initial value for the RAC vector to employ (scalar).

fval

The final value for the RAC vector to employ (scalar).

utility

Indicator of utility function: "ExpNeg" for the Exponential Negative utility, and "Power" for the Power utility function.

wealth

The initial agent wealth. By default is zero.

Value

This function produces three objects: CE_values is a table with treatment by columns and certainty values by row; RAC is a vector with the absolute risk aversion coefficients (ARAC) if the Power utility function was implemented, or the relative risk aversion coefficient (RRAC) if the Exponential Negative utility function was implemented. The length of this vector is the same as the number of profit observations in the original dataset; and, CE_plot is a graph using plot function, to compare the different CEs computed.

Details

This function computes the certainty equivalent values using profit as inputs. Works with data.frames with 3 or more observations. Consider each column as a different treatment or project.

References

Hardaker, J.B., Richardson, J.W., Lien, G., & Schumann, K.D. (2004). Stochastic efficiency analysis with risk aversion bounds: a simplified approach. Australian Journal of Agricultural and Resource Economics, 48(2), 253-270.

Examples

# NOT RUN {
## Example 1. Using profit data from ceRtainty package
data(profitSWG)

# Storing CE values using Power utility function
c1 <- certainty(data    = profitSWG,
                ival    = .5,
                fval    = 4,
                utility = "Power")
c1$CE_values # Table with CE values
c1$RAC       # RAC vector used in CE computation
c1$CE_plot() # Invoking the CE plot

# To use the ExpNeg function, it is required the RRAC (ARAC/wealth)
# so we can compute the mean value among all profit in the dataset.

# Mean value among all profit value
mean(sapply(profitSWG,mean)) # 5081.844

# Storing CE values using Power utility function
c1 <- certainty(data    = profitSWG,
                ival    = .5/5082,
                fval    = 4/5082,
                utility = "ExpNeg")

c1$CE_values # Table with CE values
c1$RAC       # RAC vector used in CE computation
c1$CE_plot() # Invoking the CE plot

## Example 2. Using the example values of Hardaker et al. (2004)
dt <- data.frame(treatment=c(100,125,135,142,147,150,153,158,163,175,195))
# Storing CE values using Power utility function. Hardaker use an
# unique RAC value (0.005)
c2 <- certainty(data    = dt,
                ival    = .005,
                fval    = .005,
                utility = "Power")
# or
c2 <- certainty(data    = dt,
                ival    = .005,
                fval    = .005,
                utility = "ExpNeg")

c2$CE_values
c2$RAC
c2$CE_plot()
# }