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Bayesian spatial quantile modeling applied to the incidence of extreme poverty in Lima-Peru
(Springer Science+Business Media, 2022-06-04)
Peru is an emerging nation with a nonuniform development where the growth is focused on some specific cities and districts, as a result there is serious economic inequalities across the country. Despite the poverty in Peru has declined in the last decades, there is still poor districts in risk to become extremely poor, even in its capital, Lima. In this context, it is relevant to study the incidence of extreme poverty at district levels. In this paper, we propose to estimate the quantiles of the incidence of extreme poverty of districts in Lima by using spatial quantile models based on the Kumaraswamy distribution and spatial random effects for areal data. Furthermore, in order to deal with spatial confounding random effects we used the Spatial Orthogonal Centroid “K”orrection approach. Bayesian inference for these hierarchical models is conveniently performed based on the Hamiltonian Monte Carlo method. Our modeling is flexible and able to describe the quantiles of incidence of extreme poverty in Lima.
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The asymmetric inflation dynamics in LATAM: Evidence from a quantile regression approach
(RELX Group (Netherlands), 2024-01-01)
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Empirical analysis of money demand: Inflation targeting effects and heterogeneous behavior in Pacific Alliance Countries (PAC)
(Economists' Association of Vojvodina, 2025-01-01)
This study aims to estimate a microfounded money demand for Pacific Alliance Countries (PAC) and evaluate whether the elasticities of income, interest rates, inflation expectations, exchange rate, and U.S. rates have changed after the adoption of inflation targeting (IT). As a consequence, we study the role the interest rate has played in these emerging economies under the complementary hypothesis of McKinnon (1973). Furthermore, we analyze the heteregeneous behavior of the demand for money during the IT period using a quantile regression approach. This study suggests that there is statistical evidence that the elasticities of the demand for money have changed after the adoption of IT. Also, the findings indicate that the demand for money has exhibited heterogeneous behavior for all the PAC during the IT period. Generally, interest rate elasticity tends to be smaller in magnitude when real balances are high, while income elasticity demonstrates heterogeneous behavior across countries.
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Bayesian quantile regression models for heavy tailed bounded variables using the No-U-Turn sampler
(Springer Science and Business Media Deutschland GmbH, 2025-07-01)
When we are interested in knowing how covariates impact different levels of the response variable, quantile regression models can be very useful, with their practical use being benefited from the increasing of computational power. The use of bounded response variables is also very common when there are data containing percentages, rates, or proportions. In this work, with the generalized Gompertz distribution as the baseline distribution, we derive two new two-parameter distributions with bounded support, and new quantile parametric mixed regression models are proposed based on these distributions, which consider bounded response variables with heavy tails. Estimation of the parameters using the Bayesian approach is considered for both models, relying on the No-U-Turn sampler algorithm. The inferential methods can be implemented and then easily used for data analysis. Simulation studies with different quantiles (q=0.1, q=0.5 and q=0.9) and sample sizes (n=100, n=200, n=500, n=2000, n=5000) were conducted for 100 replicas of simulated data for each combination of settings, in the (0, 1) and [0, 1), showing the good performance of the recovery of parameters for the proposed inferential methods and models, which were compared to Beta Rectangular and Kumaraswamy regression models. Furthermore, a dataset on extreme poverty is analyzed using the proposed regression models with fixed and mixed effects. The quantile parametric models proposed in this work are an alternative and complementary modeling tool for the analysis of bounded data.
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