CobbDouglas_boot: Bootstrap confidence intervals for a Cobb-Douglas frontier

In alessandromagrini/CobbDouglas: Efficiency Analysis using the Cobb-Douglas Production Frontier

Description

Boostrap resampling to approximate confidence intervals for parameters and technical efficiencies of a Cobb-Douglas production frontier.

Usage

CobbDouglas_boot(x, nboot=500, conf=0.95)

Arguments

x	An object of class CobbDouglas.
nboot	The number of bootstrap replications. It must be at least 50.
conf	The confidence level. Default is 0.95.

Details

Non-parameteric bootstrap resampling is performed (Efron, 1979) and bias-corrected boostrap confidence intervals are computed (Efron, 1987).

Value

An object of class CobbDouglas_boot, that is a list with three components: parameters, y.side and x.side. Each component is a data.frame containing the estimated value, the estimated standard error and the bootstrap confidence interval at level conf for each parameter (component parameters), output-side technical efficiencies (component y.side) and input-side technical efficiencies (component x.side).

References

B. Efron (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397): 171-185.

B. Efron (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1): 1-26.

See Also

CobbDouglas.

Examples

data(production)

m2 <- CobbDouglas("output", c("labour","capital"), data=production)
set.seed(123)
CobbDouglas_boot(m2, nboot=150)

m2c <- CobbDouglas("output", c("labour","capital"), data=production, beta.sum=1)
set.seed(123)
CobbDouglas_boot(m2c, nboot=150)

m2d <- CobbDouglas("output", c("labour","capital"), data=production, beta.sum=0.7)
set.seed(123)
CobbDouglas_boot(m2d, nboot=150)

alessandromagrini/CobbDouglas documentation built on July 4, 2021, 1:21 a.m.