bootCI: Calculating confidence intervals for WTP using a...

In DCchoice: Analyzing Dichotomous Choice Contingent Valuation Data

Calculating confidence intervals for WTP using a nonparametric bootstrap method

Description

This function calculates confidence intervals for WTP using the nonparametric bootstrap method.

Usage

bootCI(obj, nboot = 1000, CI = 0.95, individual = NULL)

## S3 method for class 'bootCI'
print(x, ...)

Arguments

obj	an S3 class object "dbchoice" or "sbchoice".
nboot	the number of bootstrap resampling.
CI	a percentile of the confidence intervals to be estimated.
individual	a data frame containing covariates that show an individual of which to estimate WTP.
x	an object of class "bootCI".
...	optional arguments. Currently not in use.

Details

The bootstrap method resamples the data at our hands and repeatedly estimates the model with the bootstrapped data to formulate an empirical distribution of the associated WTP. This is a clear contrast with the method of Krinsky and Robb (1986, 1990) where the parameters are directly drawn from the multivariate normal distribution.

The upper and the lower bound of the interval is determined similarly to the case of the function krCI.

Hole (2007) conducted simulation experiments to compare the performance of the method of Krinsky and Robb (1986, 1990) with the bootstrap one.

A WTP of a specific individual (e.g., a representative respondent) can be estimated when assigning covariates to individual. See Example for details.

Value

The function bootCI() returns an object of S3 class "bootCI". An object of "bootCI" is a list with the following components.

out	the output table with simulated confidence intervals as well as the four type of WTP estimates (mean, truncated mean, truncated mean with adjustment and median) from the ML estimation.
mWTP	a vector of simulated mean WTP. When |beta| < 1, this item is set to -999.
tr.mWTP	a vector of simulated mean WTP truncated at the maximum bid.
adj.tr.mWTP	a vector of simulated mean WTP truncated at the maximum bid with the adjustment.
medWTP	a vector of simulated median WTP.

When the parameter estimate on the bid does not satisfy the condition for the existence of the finite mean WTP (|beta|>1), the values of the lower and the upper bound of the confidence interval are coerced to set to -999.

The generic function print() is available for the object of class "bootCI" and displays the table of simulated confidence intervals.

The table contains the confidence intervals for the four types (mean, truncated mean, truncated mean with adjustment and median) of WTP from the ML estimation. The adjustment for the truncated mean WTP is implemented by the method of Boyle et~al.(1988).

Warning

It is time consuming (usually takes several minutes) to implement this function.

References

Boyle KJ, Welsh MP, Bishop RC (1988). “Validation of Empirical Measures of Welfare Change: Comment.” Land Economics, 64(1), 94–98.

Hole AR (2007). “A Comparison of Approaches to Estimating Confidence Intervals for Willingness to Pay Measure.” Health Economics, 16, 827–840.

Krinsky I, Robb AL (1986). “On Approximating the Statistical Properties of Elasticities.” The Review of Economics and Statistics, 68, 715–719.

Krinsky I, Robb AL (1990). “On Approximating the Statistical Properties of Elasticities: A Correction.” The Review of Economics and Statistics, 72, 189–190.

See Also

krCI, dbchoice, sbchoice

Examples

## See Examples in dbchoice and sbchoice.

DCchoice documentation built on July 26, 2023, 6:11 p.m.