arXiv subjects

Andrés Ramírez-Hassan

Publications and source records attributed to Andrés Ramírez-Hassan.

5 recordsLinked to original sources

arXiv2025

EASI Drugs in the Streets of Colombia: Modeling Heterogeneous and Endogenous Drug Preferences

The response of illicit drug consumers to policy changes like legalization is mediated by demand behavior. Since individual drug use is driven by many unobservable factors, accounting for unobserved heterogeneity becomes crucial for designing targeted policies. This paper introduces a finite Gaussian mixture of EASI demand systems to estimate joint demand for marijuana, cocaine, and basuco (a low-purity cocaine paste) in Colombia, accounting for corner solutions and endogenous prices. Our method classifies users into two groups with distinct preferences over consumption: "soft" and "hard" users. Nationally representative survey estimates find drugs are unit-elastic, with marijuana and cocaine complementary. International marijuana legalization episodes along with Colombia's low marijuana production cost suggest legalization is likely to drive prices down significantly. Legalization counterfactuals under the most likely scenario of a 50\% marijuana price decrease reveal \$363/year welfare gains for consumers, \$120M in governement revenue, and \$127M dealer losses.

econ.EM↗

arXiv2022

Testing model specification in approximate Bayesian computation

We present a procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation. We demonstrate theoretically, and empirically that this procedure can consistently detect the presence of model misspecification. Our examples demonstrates that this approach delivers good finite-sample performance and is computational less onerous than existing approaches, all of which require re-running the inference algorithm. An empirical application to modelling exchange rate log returns using a g-and-k distribution completes the paper.

stat.ME↗

arXiv2021

Semi-parametric estimation of the EASI model: Welfare implications of taxes identifying clusters due to unobserved preference heterogeneity

We provide a novel inferential framework to estimate the exact affine Stone index (EASI) model, and analyze welfare implications due to price changes caused by taxes. Our inferential framework is based on a non-parametric specification of the stochastic errors in the EASI incomplete demand system using Dirichlet processes. Our proposal enables to identify consumer clusters due to unobserved preference heterogeneity taking into account, censoring, simultaneous endogeneity and non-linearities. We perform an application based on a tax on electricity consumption in the Colombian economy. Our results suggest that there are four clusters due to unobserved preference heterogeneity; although 95% of our sample belongs to one cluster. This suggests that observable variables describe preferences in a good way under the EASI model in our application. We find that utilities seem to be inelastic normal goods with non-linear Engel curves. Joint predictive distributions indicate that electricity tax generates substitution effects between electricity and other non-utility goods. These distributions as well as Slutsky matrices suggest good model assessment. We find that there is a 95% probability that the equivalent variation as percentage of income of the representative household is between 0.60% to 1.49% given an approximately 1% electricity tariff increase. However, there are heterogeneous effects with higher socioeconomic strata facing more welfare losses on average. This highlights the potential remarkable welfare implications due taxation on inelastic services.

econ.EM↗

arXiv2020

Inferring hidden potentials in analytical regions: uncovering crime suspect communities in Medellín

This paper proposes a Bayesian approach to perform inference regarding the size of hidden populations at analytical region using reported statistics. To do so, we propose a specification taking into account one-sided error components and spatial effects within a panel data structure. Our simulation exercises suggest good finite sample performance. We analyze rates of crime suspects living per neighborhood in Medellín (Colombia) associated with four crime activities. Our proposal seems to identify hot spots or "crime communities", potential neighborhoods where under-reporting is more severe, and also drivers of crime schools. Statistical evidence suggests a high level of interaction between homicides and drug dealing in one hand, and motorcycle and car thefts on the other hand.

econ.EM↗

arXiv2020

Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects

We propose a simple approach to optimally select the number of control units in k nearest neighbors (kNN) algorithm focusing in minimizing the mean squared error for the average treatment effects. Our approach is non-parametric where confidence intervals for the treatment effects were calculated using asymptotic results with bias correction. Simulation exercises show that our approach gets relative small mean squared errors, and a balance between confidence intervals length and type I error. We analyzed the average treatment effects on treated (ATET) of participation in 401(k) plans on accumulated net financial assets confirming significant effects on amount and positive probability of net asset. Our optimal k selection produces significant narrower ATET confidence intervals compared with common practice of using k=1.

econ.EM↗