dfs_momt: Moment frontier estimator

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/dfs_momt.r

Description

This function is an implementation of the moment-type estimator developed by Daouia, Florens and Simar (2010).

Usage

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dfs_momt(xtab, ytab, x, rho, k, ci=TRUE)

Arguments

xtab

a numeric vector containing the observed inputs x1,...,xn.

ytab

a numeric vector of the same length as xtab containing the observed outputs y1,...,yn.

x

a numeric vector of evaluation points in which the estimator is to be computed.

rho

a numeric vector of the same length as x or a scalar, which determines the values of rho.

k

a numeric vector of the same length as x or a scalar, which determines the thresholds at which the moment estimator will be computed.

ci

a boolean, TRUE for computing the confidence interval.

Details

Combining ideas from Dekkers, Einmahl and de Haan (1989) with the dimensionless transformation z[i]^x={y[i]1(x[i]<=x), i=1,...,n)} of the observed sample (x[i],y[i]), i=1,...,n, the authors estimate the conditional endpoint varphi(x) by

tilde(varphi[momt])(x)= z[n-k]^x + z[n-k]^x M[n]^(1)(1+rho[x])

where M[n]^(1)=(1/k)Sum[i=0]^(k-1)(log(z[(n-i)]^x)-log(z[(n-k)]^x)), z[(1)]^x<=...<=z[(n)]^x are the ascending order statistics corresponding to the transformed sample z[i]^x, i=1,...,n) and rho[x]>0 is referred to as the extreme-value index and has the following interpretation: when rho[x]>2, the joint density of data decays smoothly to zero at a speed of power rho[x]-2 of the distance from the frontier; when rho[x]=2, the density has sudden jumps at the frontier; when rho[x]<2, the density increases toward infinity at a speed of power rho[x]-2 of the distance from the frontier. Most of the contributions to econometric literature on frontier analysis assume that the joint density is strictly positive at its support boundary, or equivalently, rho[x]=2 for all x. When rho[x] is unknown, Daouia et al. (2010) suggest to use the following two-step estimator: First, estimate rho[x] by the moment estimator tilde(rho)[x] implemented in the function rho_momt_pick by utilizing the option method="moment", or by the Pickands estimator hat(rho)[x] by using the option method="pickands". Second, use the estimator tilde(varphi[momt])(x), as if rho[x] were known, by substituting the estimated value tilde(rho)[x] or hat(rho)[x] in place of rho[x]. The 95\% confidence interval of varphi(x) derived from the asymptotic normality of tilde(varphi[momt])(x) is given by

[tilde(varphi)[momt](x) +- 1.96 sqrt(V(rho[x])/k) z^x[(n-k)] M^(1)[n](1 + 1/rho[x]) ]

where V(rho[x]) = rho^2[x] (1+2/rho[x])^(-1). The sample fraction k=k[n](x) plays here the role of the smoothing parameter and varies between 1 and N[x]-1, with N[x]=Sum[i=1]^n1(x[i]<=x) being the number of observations (x[i],y[i]) with x[i]<=x. See kopt_momt_pick for an automatic data-driven rule for selecting k.

Value

Returns a numeric vector with the same length as x.

Note

As it is common in extreme-value theory, good results require a large sample size N[x] at each evaluation point x. See also the note in kopt_momt_pick.

Author(s)

Abdelaati Daouia and Thibault Laurent (converted from Leopold Simar's Matlab code).

References

Daouia, A., Florens, J.P. and Simar, L. (2010). Frontier Estimation and Extreme Value Theory, Bernoulli, 16, 1039-1063.

Dekkers, A.L.M., Einmahl, J.H.J. and L. de Haan (1989), A moment estimator for the index of an extreme-value distribution, nnals of Statistics, 17, 1833-1855.

See Also

dfs_pick, kopt_momt_pick.

Examples

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data("post")
x.post <- seq(post$xinput[100], max(post$xinput), 
 length.out = 100) 
# 1. When rho[x] is known and equal to 2, we set:
rho <- 2
# To determine the sample fraction k=k[n](x) 
# in tilde(varphi[momt])(x).
best_kn.1 <- kopt_momt_pick(post$xinput, post$yprod, 
 x.post, rho = rho)
# To compute the frontier estimates and confidence intervals:  
res.momt.1 <- dfs_momt(post$xinput, post$yprod, x.post, 
 rho = rho, k = best_kn.1)
# Representation
plot(yprod~xinput, data = post, xlab = "Quantity of labor", 
 ylab = "Volume of delivered mail")
lines(x.post, res.momt.1[,1], lty = 1, col = "cyan")  
lines(x.post, res.momt.1[,2], lty = 3, col = "magenta")  
lines(x.post, res.momt.1[,3], lty = 3, col = "magenta")  

## Not run: 
# 2. rho[x] is unknown and estimated by 
# the Pickands estimator tilde(rho[x])
rho_momt <- rho_momt_pick(post$xinput, post$yprod, 
 x.post)
best_kn.2 <- kopt_momt_pick(post$xinput, post$yprod,
  x.post, rho = rho_momt)
res.momt.2 <- dfs_momt(post$xinput, post$yprod, x.post, 
 rho = rho_momt, k = best_kn.2)  
# 3. rho[x] is unknown independent of x and estimated
# by the (trimmed) mean of tilde(rho[x])
rho_trimmean <- mean(rho_momt, trim=0.00)
best_kn.3 <- kopt_momt_pick(post$xinput, post$yprod,
  x.post, rho = rho_trimmean)   
res.momt.3 <- dfs_momt(post$xinput, post$yprod, x.post, 
 rho = rho_trimmean, k = best_kn.3)  

# Representation 
plot(yprod~xinput, data = post, col = "grey", 
 xlab = "Quantity of labor", ylab = "Volume of delivered mail")
lines(x.post, res.momt.2[,1], lty = 1, lwd = 2, col = "cyan")  
lines(x.post, res.momt.2[,2], lty = 3, lwd = 4, col = "magenta")  
lines(x.post, res.momt.2[,3], lty = 3, lwd = 4, col = "magenta")  
plot(yprod~xinput, data = post, col = "grey", 
 xlab = "Quantity of labor", ylab = "Volume of delivered mail")
lines(x.post, res.momt.3[,1], lty = 1, lwd = 2, col = "cyan")  
lines(x.post, res.momt.3[,2], lty = 3, lwd = 4, col = "magenta")  
lines(x.post, res.momt.3[,3], lty = 3, lwd = 4, col = "magenta") 

## End(Not run)

npbr documentation built on May 2, 2019, 2:50 p.m.