HEDIBERT FREITAS LOPES

HEDIBERT FREITAS LOPES

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When It Counts—Econometric Identification of the Basic Factor Model Based on GLT Structures
(2023) Frühwirth-Schnatter, Sylvia; Hosszejni, Darjus; HEDIBERT FREITAS LOPES
Despite the popularity of factor models with simple loading matrices, little attention has been given to formally address the identifiability of these models beyond standard rotation-based identification such as the positive lower triangular (PLT) constraint. To fill this gap, we review the advantages of variance identification in simple factor analysis and introduce the generalized lower triangular (GLT) structures. We show that the GLT assumption is an improvement over PLT without compromise: GLT is also unique but, unlike PLT, a non-restrictive assumption. Furthermore, we provide a simple counting rule for variance identification under GLT structures, and we demonstrate that within this model class, the unknown number of common factors can be recovered in an exploratory factor analysis. Our methodology is illustrated for simulated data in the context of post-processing posterior draws in sparse Bayesian factor analysis.
Bayesian generalizations of the integer-valued autoregressive model
(2022) HEDIBERT FREITAS LOPES; PAULO CILAS MARQUES FILHO; Graziadei, Helton
Stochastic Volatility Models with Skewness Selection
(2024) Martins, Igor; HEDIBERT FREITAS LOPES
This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks’ mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.
The illusion of the illusion of sparsity: an exercise in prior sensitivity
(2021) Fava, Bruno Vinicius Nunes; HEDIBERT FREITAS LOPES
How many hospitalizations has the COVID-19 vaccination already prevented in São Paulo
(2021) Izbick, Rafael; Bastos, Leonardo S.; Izbicki, Meyer; HEDIBERT FREITAS LOPES; Santos, Tiago Mendonça dos
A judicialização de benefícios previdenciários e assistenciais
(2020) NATALIA PIRES DE VASCONCELOS; Arguelles, Diego Werneck; Lima, Rafael Scavone Bellem de; FABIO JOSE AYRES; HEDIBERT FREITAS LOPES; Carlotti, Danilo; Wang, Henrique Yu Jiunn; Funari, Helena; PAULO FURQUIM DE AZEVEDO; Queirós, Danielly; Colares, Elisa; Stemler, Igor; Mota, Isabely; Monteiro, Alexander; Bittencourt, Cristianna; Amorim, Pedro; Marques, Ricardo; Rosa, Thatiane; Ferreira, Carlos Vinicius Ribeiro; Pereira, Filipe; Borges, Davi; Amorim, Pedro; Barbão, Jaqueline; VANESSA BOARATI
Decisões administrativas na área de previdência social são objeto frequente de demandas judiciais. A magnitude dessa judicialização é grande o suficiente para afetar não só a política previdenciária, mas o funcionamento do próprio Judiciário, visto que se trata de um dos tipos de demanda que mais congestiona as cortes brasileiras. Esta pesquisa se dedica a esse tema, tendo como principal objetivo o de (i) investigar as causas da revisão judicial de decisões administrativas do Instituto Nacional do Seguro Social (INSS) referentes à concessão ou revisão de benefícios previdenciários ou assistenciais, bem como (ii) apontar propostas de políticas para mitigar os custos associados ao elevado nível de litigância nessa área. Sendo um fenômeno de representatividade nacional, esta pesquisa também investiga as heterogeneidades regionais e os diferentes padrões de concessão administrativa e judicial de benefícios previdenciários e assistenciais.
Probabilistic Nearest Neighbors Classification
(2024) Fava, Bruno; PAULO CILAS MARQUES FILHO; HEDIBERT FREITAS LOPES
Analysis of the currently established Bayesian nearest neighbors classification model points to a connection between the computation of its normalizing constant and issues of NP-completeness. An alternative predictive model constructed by aggregating the predictive distributions of simpler nonlocal models is proposed, and analytic expressions for the normalizing constants of these nonlocal models are derived, ensuring polynomial time computation without approximations. Experiments with synthetic and real datasets showcase the predictive performance of the proposed predictive model.
Parsimony inducing priors for large scale state–space models
(2022) HEDIBERT FREITAS LOPES; McCulloch, Robert E.; Tsay, Ruey S.
State–space models are commonly used in the engineering, economic, and statistical literature. They are flexible and encompass many well-known statistical models, including random coefficient autoregressive models and dynamic factor models. Bayesian analysis of state–space models has attracted much interest in recent years. However, for large scale models, prior specification becomes a challenging issue in Bayesian inference. In this paper, we propose a flexible prior for state–space models. The proposed prior is a mixture of four commonly entertained models, yet achieving parsimony in high-dimensional systems. Here ‘‘parsimony’’ is represented by the idea that, in a large system, some states may not be time-varying. Our prior for the state–space component’s standard deviation is capable to accommodate different scenarios. Simulation and simple examples are used throughout this paper to demonstrate the performance of the proposed prior. As an application, we consider the time-varying conditional covariance matrices of daily log returns of the components of the S&P 100 index, leading to a state–space model with roughly five thousand time-varying states. Our model for this large system enables us to use parallel computing.
Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis
(2024) Bolfarine, Henrique; Carvalho, Carlos M.; HEDIBERT FREITAS LOPES; Murray, Jared S.
Factor analysis is a popular method for modeling dependence in multivariate data. However, determining the number of factors and obtaining a sparse orientation of the loadings are still major challenges. In this paper, we propose a decision-theoretic approach that brings to light the relationship between model fit, factor dimension, and sparse loadings. This relation is done through a summary of the information contained in the multivariate posterior. A two-step strategy is used in our method. First, given the posterior samples from the Bayesian factor analysis model, a series of point estimates with a decreasing number of factors and different levels of sparsity are recovered by minimizing an expected penalized loss function. Second, the degradation in model fit between the posterior of the full model and the recovered estimates is displayed in a summary. In this step, a criterion is proposed for selecting the factor model with the best trade-off between fit, sparseness, and factor dimension. The findings are illustrated through a simulation study and an application to personality data. We used different prior choices to show the flexibility of the proposed method.
Prior Sensitivity Analysis in a Semi-Parametric Integer-Valued Time Series Model
(2020) Graziadei, Helton; Lijoi, Antonio; HEDIBERT FREITAS LOPES; PAULO CILAS MARQUES FILHO; Prünster, Igor