HEDIBERT FREITAS LOPES

HEDIBERT FREITAS LOPES

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Bayesian statistics with a smile: a resampling-sampling perspective
(2012) HEDIBERT FREITAS LOPES; Polson, Nicholas G.; Carvalho, Carlos M.
Particle Learning for General Mixtures
(2010) Carvalho, Carlos M.; HEDIBERT FREITAS LOPES; Polson, Nicholas G.; Taddy, Matt A.
Particle Learning and Smoothing
(2010) Carvalho, Carlos M.; Michael S. Johannes; HEDIBERT FREITAS LOPES; Polson, Nicholas G.
Particle Learning for Sequential Bayesian Computation
(2011) HEDIBERT FREITAS LOPES; Carvalho, Carlos M.; Johannes, Michael S.; Polson, Nicholas G.
Particle Learning for Sequential Bayesian Computation
(2011) HEDIBERT FREITAS LOPES; Johannes, Michael S.; Carvalho, Carlos M.; Polson, Nicholas G.
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.
Online Bayesian learning in dynamic models: An illustrative introduction to particle methods
(2013) HEDIBERT FREITAS LOPES; Carvalho, Carlos M.
On the Long-Run Volatility of Stocks
(2018) Carvalho, Carlos M.; HEDIBERT FREITAS LOPES; McCulloch, Robert E.
In this article, we investigate whether or not the volatility per period of stocks is lower over longer horizons.Taking the perspective of an investor, we evaluate the predictive variance of k-period returns under differentmodel and prior specifications. We adopt the state-space framework of Pástor and Stambaugh to model thedynamics of expected returns and evaluate the effects of prior elicitation in the resulting volatility estimates.Part of the developments includes an extension that incorporates time-varying volatilities and covariancesin a constrained prior information set-up. Our conclusion for the U.S. market, under plausible prior specifi-cations, is that stocks are less volatile in the long run. Model assessment exercises demonstrate the modelsand priors supporting our main conclusions are in accordance with the data. To assess the generality of theresults, we extend our analysis to a number of international equity indices. Supplementary materials for thisarticle are available online.
On the Long-Run Volatility of Stocks
(2018) Carvalho, Carlos M.; HEDIBERT FREITAS LOPES; McCulloch, Robert E.