HEDIBERT FREITAS LOPES
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Artigo Científico Particle Learning for Fat-Tailed Distributions(2016) HEDIBERT FREITAS LOPES; Polson, Nicholas G.- When It Counts—Econometric Identification of the Basic Factor Model Based on GLT Structures(2023) Frühwirth-Schnatter, Sylvia; Hosszejni, Darjus; HEDIBERT FREITAS LOPESDespite 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.
Artigo Científico Bayesian statistics with a smile: a resampling-sampling perspective(2012) HEDIBERT FREITAS LOPES; Polson, Nicholas G.; Carvalho, Carlos M.Artigo Científico Particle Learning for General Mixtures(2010) Carvalho, Carlos M.; HEDIBERT FREITAS LOPES; Polson, Nicholas G.; Taddy, Matt A.Artigo Científico Bayesian generalizations of the integer-valued autoregressive model(2022) HEDIBERT FREITAS LOPES; PAULO CILAS MARQUES FILHO; Graziadei, HeltonArtigo Científico Credit granting to small firms: a Brazilian case(2009) Zambaldi, Felipe; Aranha, Francisco; HEDIBERT FREITAS LOPES; Politi. RicardoArtigo Científico Stochastic Volatility Models with Skewness Selection(2024) Martins, Igor; HEDIBERT FREITAS LOPESThis 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.Artigo Científico Particle Learning and Smoothing(2010) Carvalho, Carlos M.; Michael S. Johannes; HEDIBERT FREITAS LOPES; Polson, Nicholas G.Artigo Científico The illusion of the illusion of sparsity: an exercise in prior sensitivity(2021) Fava, Bruno Vinicius Nunes; HEDIBERT FREITAS LOPESArtigo Científico 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