1 Introduction
Let and
be two independent random variables with cumulative distribution functions
and , respectively. The corresponding quantile functions are given by , . In many applications it is of interest to compare quantiles of two random variables at a given , which can be done by consideringFor example, if is income in some population at time and is income at time , then reports the proportion by which the quantile of income changed from to , with indicating income growth. In medical research one can compare quantiles of some measures obtained in treatment and control groups and then shows the effect of the treatment on the quantile.
In applications is either considered and interpreted at a fixed or the curve , is reduced to some number. For example, Cheng and Wu (2010) as well as Wu (2010) studied the effect of cancer treatment measured by the ratio of the cancer volumes in the treatment and the control group, the socalled ratio. The ratio can be formed for the mean cancer volume or for a certain quantile of the volume in the treatment and the control group, but typically is not considered as a function of . Dominici et al. (2005) and Dominici and Zeger (2005) used the whole curve , but only to calculate the mean difference
which is known as the average treatment effect (ATE). To obtain , is estimated by a smooth function. This approach has been applied to estimate the difference in medical expenditures between persons suffering from diseases attributable to smoking and persons without these diseases.
However, it is clearly more advantageous to view as a function of . To the best of our knowledge, this has been done only in the poverty research context. In particular, Ravallion and Chen (2003) used the curve
for the analysis of income distributions in developing countries at times and called the growth incidence curve (GIC). Poverty reduction can be understood as increasing the incomes of the poor. In this sense poverty is reduced from period to , if takes positive values for all small quantiles up the quantile where the poverty line was located in the first period. Such growth that increases the incomes of poor quantiles has been called “weak absolute” propoor growth, i.e. growth that is accompanied by absolute poverty reduction without making any statement about the distributional pattern of growth, see Klasen (2008). On the other hand, if has a negative slope, growth was propoor in the relative sense, i.e. the poor benefited (proportionately) more from growth than the nonpoor. This means that such growth episodes led to a decrease in inequality and relative poverty. For a detailed discussion of different notions of propoor growth we refer to Ravallion (2004) and Klasen (2008). Growth incidence curves were also applied to nonincome data in Grosse et al. (2008).
Hence, considering the whole curves or , provides more informative comparison of two distributions and can be applied not only in the poverty research context. The goal of this work is to derive the asymptotic distribution of an estimator of and build simultaneous confidence bands for . Estimation and inference for is then straightforward.
Dominici et al. (2005) proposed an estimator for using smoothing splines. Venturini et al. (2015) extend the work by Dominici et al. (2005), employing a Bayesian approach to get a smooth estimator of , for some known monotone differentiable function . A much simpler approach, which we pursue, would be to replace the unknown in by some estimator , to get . There are several quantile estimators available (see e.g. Harrell and Davis, 1982; Kaigh and Lachenbruch, 1982; Cheng, 1985). In this work we employ the classical empirical quantile function.
Apparently, simultaneous inference about the curve , is crucial in applications, but has not been considered so far, to the best of our knowledge. Dominici et al. (2005) rather focused on estimation of the average treatment effect with the help of and do not discuss inference about . Cheng and Wu (2010) consider estimation of at a given
and build a confidence interval for
using asymptotic normality arguments and several estimators for the variance of
. The Worldbank Poverty Analysis Toolkit (can be found at http://go.worldbank.org/YF9PVNXJY0) provides also only pointwise confidence intervals for growth incidence curves, similar in spirit to that of Cheng and Wu (2010). More specifically, the confidence statement in this toolkit is constructed for a discretization of by . For every , expectation and variance for some estimator of are estimated with a bootstrap. Critical values and are then taken from the corresponding distribution for some level . This implicitly assumes that is asymptotically normal. The resulting confidence statement has the formwhere is some prespecified confidence level. Obviously, these confidence intervals provide inference only at a given . For example, if we would like to test significance of the poverty reduction (or treatment effect) at the median, it is enough to build a pointwise confidence interval for (or for ) and check if it includes zero (or one). However, for the test if the growth was propoor in the relative sense, a confidence statement about the slope of has to be made and, hence, simultaneous confidence bands should be considered. That is, the goal is to find such and that
The difference to the pointwise intervals is that holds not only separately for every , but simultaneously for all .
To build simultaneous confidence bands for or , the analysis of the asymptotic distribution of the function is necessary. This involves the theory of empirical processes which goes back to Glivenko (1933), Cantelli (1933), Donsker (1952), and Komlós et al. (1975). Our analysis builds on results for empirical quantile processes and its simultaneous confidence bands developed in Csörgő and Révész (1978), Csörgő and Révész (1984), and Csörgő (1983). The main benefit of this approach is that it allows for faster computation of the confidence bands without resampling techniques.
The paper is organized as follows. In Section 2 we introduce a simple sample counterpart estimator and analyse its asymptotic distribution. This estimator is also used by the World Bank Toolkit. The results about the asymptotic distribution motivates two constructions for asymptotic simultaneous confidence bands presented in Section 3. Section 4 evaluates the small sample properties of our confidence bands by Monte Carlo simulations. Expenditure data from Uganda are analysed with our confidence bands in Section 5 before we conclude in Section 6.
2 Estimation and asymptotic distribution
Throughout this section we assume that we have i.i.d. samples of and of . Furthermore, we assume that the samples are stochastically independent of each other. This assumption is justified if the data are collected in two independent groups (e.g. treatment and control) or in repeated crosssections. Note that there is a related concept of nonanonymous growth incidence curves proposed for panel data in Grimm (2007) and Bourguignon (2011). Nonanonymous growth incidence curves are built based on two dependent samples and are not treated in this work.
2.1 Quantile ratio estimator
We start by presenting a simple sample estimator for and . For we denote the th order statistic of the sample by . The sample quantile function is the inverse of the right continuous empirical distribution function, which is known to be
(1) 
We now define estimators of and as
(2) 
It is wellknown that the quantile function and its empirical version are equivariant under strictly monotone transformations. Let us denote by and the cumulative distribution and quantile functions of , , respectively. Also, let be the empirical quantile function of the logtransformed sample , , . Then, , as well as , . Consequently,
(3) 
Hence, a simultaneous confidence band for can be obtained observing that
Note that the difference of two quantile functions is known as quantile treatment effect (QTE), sometimes also named the percentilespecific effect between two populations, see Dominici et al. (2006). To the best of our knowledge, the inference for QTE is usually done at a fixed , rather than simultaneously.
2.2 Pointwise asymptotic distribution
We first characterizes the asymptotic distribution of at a fixed . The following assumption usually holds for data on income, expenditure, or cancer volume, etc.
Assumption 1.
Two independent random variables and
with finite second moments and cumulative distribution functions
and are given together with random samples , . The logtransformed has the cumulative distribution function and density , . The corresponding quantile function has the quantile density , , .Theorem 1.
Let Assumption 1 hold and be fixed. Moreover, assume and are continuously differentiable at some and , respectively, such that and .

For the estimator is asymptotically lognormal with the parameters and

If in addition and are continuously differentiable at some and , respectively, such that , for some , and , then the asymptotic distribution of is bivariate lognormal with the parameters and
Corollary 1.
Under the assumptions of Theorem 1 we have asymptotic normality for in the sense that
converges in distribution to a standard normal random variable for and for any fixed .
The World Bank Toolkit and Cheng and Wu (2010) implicitly employ the asymptotic normality of and to build pointwise confidence intervals, but use different variance estimators, based either on bootstrap or on certain approximations. To the best of our knowledge, the result of Corollary 1 is new. Note also that depends on unknown , , which have to be consistently estimated in practice.
Theorem 1 and Corollary 1 provide two different ways for deriving pointwise confidence statements about (or about by setting ). We can approximate the distribution of for a fixed
either by a lognormal or by a normal distribution. However, the lognormal approximation is preferable for positive random variables. Indeed,
, implies for all . Hence, a normal approximation of the distribution ofputs probability mass outside of
. This can cause confidence intervals to take impossible values, in particular in small samples, and affect the actual coverage of the band. Taking a lognormal approximation helps to avoid this. We use the lognormal approximation implicitly in our constructions of simultaneous confidence bands in Section 3.2.3 Approximation by Brownian bridges
In the previous Section 2.2 derivation of the confidence statements about or at one or at a finite number of points reduces to finding the limiting distribution of at a fixed . To obtain confidence statements about or that hold for all simultaneously, we need to find the limiting distribution of , which is treated as a stochastic process indexed in .
Let us define the following stochastic process
where is a fixed scaling parameter independent of needed later for technical reasons. For the analysis of this process we need the following set of assumptions on and .
Assumption 2.
The cumulative distribution functions of the logtransformed , are twice differentiable on , where , , and on . In addition, there exists some such that
(4) 
Assumption 3.
For and , one of the following conditions hold


If , then is nondecreasing on an interval to the right of and nonincreasing on an interval to the left of .
If and are lognormal, as typically the case for income, expenditure and similar positive random variables, then is the density of a normal distribution. Hence, existence, positivity and differentiability of on are trivially fulfilled. The supremum in (4) is for normally distributed random variables independent of expectation and variance. The property in Assumption 3 is called tailmonotonicity. For normal distributions and Assumption 3 (ii) obviously holds.
The following result shows that converges uniformly to a Brownian bridge . Recall that a Brownian bridge is a standard Wiener process with , i.e. , . In particular, and for all .
Theorem 2.
For example, if are approximately lognormal in a way that has the tail behavior of a normal variable, then according to Theorem 2 the process converges to a Brownian bridge simultaneously on with the rate .
Constructing confidence sets for or requires knowledge of the asymptotic distribution of , while in Theorem 2 contains instead. Therefore, let us consider
and discuss the choice of . First, introduce the following assumption.
Assumption 4.
There exists a constant such that the quantile densities satisfy , .
Obviously, under Assumption 4 we have that
and Theorem 2 can be applied to get the asymptotic distribution of and hence the simultaneous confidence bands for or .
It is shown in the Appendix, that if Assumption 4 is true, then
(5) 
Moreover, if the have distribution from the locationscale family of distributions with locations and scales , , then Assumption 4 implies that . This can be seen directly from (5) applying the change of variable . Also, let denote the quantile function of and the corresponding quantile density. Then, and therefore , , . In particular, Assumption 4 implies that and thus the distributions of and differ only in location and scale parameters.
For example, if
are both lognormally distributed with arbitrary location parameters and scale parameters
, then , are normally distributed and . In applications, to check if distributions of and differ only in the location and scale, one can inspect the QQplot of standardised logtransformed data.If the quantile densities are not proportional, that is, Assumption 4 is not fulfilled, we have to handle the term
Note that the bound on the right hand side is always smaller are equal for every . Since and are usually similar functions in applications, much smaller bounds can be expected.
3 Simultaneous confidence bands
Based on the results of the previous section, we can derive simultaneous confidence bands for and transform them into simultaneous confidence bands for or . Note that simultaneous confidence bands for the quantile treatment effect follow immediately. We make use of Theorem 2 and Lemma 1 from the last section, as well as the Kolmogorov distribution
(6) 
Throughout this section we assume a confidence level and denote the corresponding critical value for the Brownian bridge by such that . In addition, we denote by an asymptotically almost sure upper bound from Lemma 1
with some .
In the following, we present two ways of using the approximation by Brownian bridges for the construction of simultaneous confidence band for . Similar approaches for the quantile function have been explored in Csörgő and Révész (1984).
3.1 Confidence bands with quantile density estimation
The first approach to the construction of confidence bands relies on the following argument
The quantities , are unknown and have to be estimated. Various nonparametric methods for the estimation of
have been proposed, typically based on kernel density estimation, see e.g.
Csörgő et al. (1991), Jones (1992), Cheng (1995), Cheng and Parzen (1997), Soni et al. (2012), and Chesneau et al. (2016). We make the following assumption on the densities.Assumption 5.
The densities , fulfill
Now we can get the simultaneous confidence bands for the difference of two quantile functions.
Theorem 3.
3.2 Direct confidence bands
The confidence band above depends on nonparametric estimation of quantile densities. Two smoothing parameters , have to be chosen, which might be unfavourable in applications. This can be avoided with the alternative construction of confidence bands given in the following theorem.
4 Simulation study
We evaluate the properties of the confidence bands by using synthetic data and building confidence bands for growth incidence curves . Confidence bands for the quantile treatment effect and are equivalent. We consider two settings and in both of them fix . In the first setting and are drawn from lognormal distributions. Thereby, has location parameter and scale parameter , while has location parameter and scale parameter . As already discussed, Assumption 4 holds in this example with . This value is estimated in the simulations, while is set to zero. In the second setting, is as in the first setting, while
is drawn from the gamma distribution with the shape parameter
and scale parameter . In this setting Assumption 4 does not hold and is estimated for the plugin confidence bands.We considered four sample sizes . For probability values we used an equidistant grid of length to build the confidence bands; setting the grid length to does not change the results significantly, but increases the computation time in Monte Carlo simulations. The results are based on the Monte Carlo samples of size . The following Table 1 summarizes the actual coverage probability with simulated data for . The results are given in both settings for the confidence bands with plugin estimators, for the direct confidence bands and for the confidence bands built with the World Bank algorithm.
Setting 1  Setting 2  

Sample size  Plugin  Direct  World Bank  Plugin  Direct  World Bank 
0.888  0.965  0.460  0.893  0.964  0.386  
0.975  0.960  0.286  0.958  0.960  0.177  
0.980  0.959  0.343  0.969  0.960  0.267  
0.984  0.960  0.425  0.973  0.964  0.390 
First of all, the coverage of the confidence bands obtained with the World Bank algorithm is way too small. The reason is that we tested simultaneous coverage, while the World Bank algorithm constructs only pointwise confidence bands.
The actual coverage probability of all our constructions (about ) is slightly larger than the theoretical probability , except for the plugin confidence bands for , where the coverage is lower than the nominal. This can be attributed to the quality of the nonparametric estimates of the quantile densities in small samples, as also expected from Theorem 3. Once the sample size is large, both confidence bands perform very similar, even with the estimated correction for the plugin bands in the second setting.
The plots in Figure 1 show typical estimates from the first setting together with plugin and direct confidence bands for (left) and (right). The true growth incidence curve is the dashed line, while its estimate is the solid line. Plugin confidence bands are shown as a grey area, while direct confidence bands are solid lines enveloping the growth incidence curve. In accordance with the simulation results, plugin confidence bands are somewhat narrower for small , while for both confidence bands are nearly indistinguishable. As stated in Theorem 3 and Theorem 4 the confidence bands are not defined for close to and close to . The plots show the bands for probabilities between and .
5 Application to household data
Our work is motivated by the application of growth incidence curves to the evaluation of propoorness of growth in developing countries. Absolute poverty is reduced if the growth incidence curve is positive for all income quantiles below the poverty line and such growth is called propoor using the weak absolute definition mentioned in the introduction. In this case, there is some income growth for the poor and absolute poverty is reduced. In addition, relative poverty is reduced if has a negative slope, such growth is called propoor using the relative definition as it is associated with declining inequality and declining relative poverty.
We analyse data from the Uganda National Household Survey for the years , , and . This is a standard multipurpose household survey that is regularly conducted to monitor trends in poverty and inequality and its most important correlates. The sample sizes are , , and . We measure welfare by household expenditure per adult equivalent in prices and compute the related growth incidence curves.
First, we consider the growth incidence curve for the time from to . Inspecting in Figure 2
QQplots of the standardised logtransformed data (left and middle), we can deduce that both samples show slight departures from the lognormal distribution, but differ from each other only in location and scale, up to four outliers. Hence, we can estimate
according to (5) and set .The estimated growth incidence curve shown in Figure 3 is close to on the whole interval . It takes positive values up to the quantile and negative values for higher incomes. The slope tends to be negative. This might suggest that absolute poverty and relative poverty was reduced, and growth was propoor according to the weak absolute and relative definition. Both simultaneous confidence bands are shown in the left panel; the grey area corresponds to the plugin confidence bands, while bold lines are the direct confidence bands. As in simulations for large samples, both approaches lead to nearly the same bands. Simultaneous confidence bands include the zero line, which suggests that none of the discussed effects is in fact significant. In contrast, the considerably tighter confidence bands of the World Bank Toolkit, shown in the right plot, would wrongly suggest otherwise, overinterpreting the nonsignificant poverty reduction and propoor growth.
Let us now consider the expenditure data from and
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