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Ms. Camelia Minoiu and Sanjay Reddy
We analyze the performance of kernel density methods applied to grouped data to estimate poverty (as applied in Sala-i-Martin, 2006, QJE). Using Monte Carlo simulations and household surveys, we find that the technique gives rise to biases in poverty estimates, the sign and magnitude of which vary with the bandwidth, the kernel, the number of datapoints, and across poverty lines. Depending on the chosen bandwidth, the $1/day poverty rate in 2000 varies by a factor of 1.8, while the $2/day headcount in 2000 varies by 287 million people. Our findings challenge the validity and robustness of poverty estimates derived through kernel density estimation on grouped data.
Ms. Camelia Minoiu and Sanjay Reddy

I. M otivation Several recent studies have employed nonparametric smoothing techniques, and in particular kernel density estimation (henceforth, ‘KDE’) on grouped data to obtain poverty estimates ( Sala-i-Martin 2002a , 2002b , 2004 , 2006 ; Ackland, Dowrick, and Freyens, forthcoming ; Fuentes, 2005 ). 1 World poverty and inequality assessments—especially over longer timehorizons—require the use of grouped data (usually expressed as income averages for a small number of population quantiles) because representative household surveys are not available

Mr. Francesco Caselli, Mr. Francesco Grigoli, Romain Lafarguette, and Changchun Wang

-dependencies: (i) other economies depend on large economies and their respective regional leaders, (ii) regional leaders depend on large economies, and (iii) regional leaders and other economies have no impact on large economies. Our approach consists of a four-step probabilistic model for global GDP growth that mixes parametric and non-parametric features. It first generates the joint predictive density via a multivariate conditional kernel density estimation for a set of large economies (see Li and Racine 2007 ). 4 5 This means that a certain probability is assigned to all

Mr. Francesco Caselli, Mr. Francesco Grigoli, Romain Lafarguette, and Changchun Wang
In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive GDP growth densities, taking into account cross-country interdependencies. Speci?cally, we model non-parametrically the contemporaneous interdependencies across the United States, the euro area, and China via a conditional kernel density estimation of a joint distribution. Then, we characterize the potential ampli?cation e?ects stemming from other large economies in each region—also with kernel density estimations—and the reaction of all other economies with para-metric assumptions. Importantly, each economy’s predictive density also depends on a set of observable country-speci?c factors. Finally, the use of sampling techniques allows us to aggregate individual countries’ densities into a world aggregate while preserving the non-i.i.d. nature of the global GDP growth distribution. Out-of-sample metrics con?rm the accuracy of our approach.
Ms. Camelia Minoiu and Ms. Shatakshee Dhongde
Current estimates of global poverty vary substantially across studies. In this paper we undertake a novel sensitivity analysis to highlight the importance of methodological choices in estimating global poverty. We measure global poverty using different data sources, parametric and nonparametric estimation methods, and multiple poverty lines. Our results indicate that estimates of global poverty vary significantly when they are based alternately on data from household surveys versus national accounts but are relatively consistent across different estimation methods. The decline in poverty over the past decade is found to be robust across methodological choices.