Published papers and technical reports

  1. Minnesota BART, Journal of Business and Economic Statistics (submitted) (with Lima, Carvalho and Herren) + slides
  2. Learning CATE in RDD using BART, Bayesian Analysis (submitted) (with Alcântara and Hahn) + slides
  3. Volume-driven time-of-day effects in intraday volatility models, International Journal of Forecasting (R&R)  (with Martins, Virbickaitè and Nguyen)
  4. Fast and slow level shifts in intraday stochastic volatility, Journal of Financial Econometrics (R&R) (with Virbickaite, Martins and Nguyen) + slides
  5. A Conversation with Mike West. Statistical Science (accepted) (with Ascolani)
  6. Lower-dimensional posterior density and cluster summaries from over-parameterized Bayesian models, Statistics and Computing (accepted) (with Bolfarine and Carvalho)
  7. MCMC and particle filtering for dynamic INAR Processes, Applied Stochastic Models in Business and Industry (accepted) (with Soyer and Zhang) + slides
  8. Dynamic sparsity on dynamic regression models, Brazilian Journal of Probability and Statistics, 2025, 39(3): 366-386 (with Uribe) + slides + arxiv
  9. What events matter for exchange rate volatility? Quarterly Review of Economics and Finance, 2025, 104, 102073 (with Martins)
  10. Multivariate dynamic mixed-frequency density pooling for financial forecasting, International Journal of Forecasting, 2025, 41(3), 1184-1198 (with Virbickaite and Zaharieva)
  11. Sparse Bayesian factor analysis when the number of factors is unknown, Bayesian Analysis, 2024, pages 1-32 (with discussion, pages 33-112) (with Fruhwirth-Schnatter and Hosszejni) – (supplementary material) (2018 version) (2010 version)(ISBA webinar, 02/13/2025)
  12. Decoupling shrinkage and selection in Gaussian linear factor analysis, Bayesian Analysis, 2024, 19(1), 181-203. (with Bolfarine, Carvalho and Murray).
  13. Stochastic volatility models with skewness selection, Entropy, 2024, 26(2), 142. (with Martins)
  14. Probabilistic nearest neighbors classification, Entropy, 2024, 26(1), 39. (with Fava and Marques Jr.)
  15. Dynamic ordering learning in multivariate forecasting, in Chiann, Pinheiro and Toloi (Eds.), Springer Nature: Time Series and Wavelets Analysis: Festschrift in Honor of Pedro A. Morettin, 2024. (with Levy) + slides + presentation (in Portuguese)
  16. When it counts: Econometric identification of the basic factor model based on GLT structures, Econometrics, 2023, 11(4), 26. (with Fruhwirth-Schnatter and Darjus Hosszejni)
  17. Deep learning models for inflation forecasting, Applied Stochastic Models in Business and Industry, 2023,  Volume 39, Issue 3, pages 447-470. (with Theoharidis and Guillen)
  18. Parsimony inducing priors for large scale state-space models, Journal of Econometrics, 2022, Volume 230, Issue 1, Pages 39-61. (with McCulloch and Tsay)
  19. Bayesian generalizations of the integer-valued autoregressive model, Journal of Applied Statistics, 2022, Volume 49, 336-356 (with Graziadei and Marques) + slides + R package output
  20. The illusion of the illusion of sparsity. Brazilian Journal of Probability and Statistics, 2021, Vol. 35, Issue 4, (Nov 2021) , pgs 699-720. (with Fava) + slides + webinar at UFPE (Minute 62 forward)
  21. How many hospitalizations has the COVID-19 vaccination already prevented in São Paulo? , Clinics, 2021, 76:e3250. (with Izbicki, Bastos, Izbicki and Santos)
  22. Trend-following strategies via dynamic momentum learning, Technical report, 2021. (with Levy)
  23. Dynamic portfolio allocation in high dimensions using sparse risk factors, Technical report, 2021. (with Levy)
  24. Spatial prediction of sea level trends , Environmetrics, 2020, 31(4), e2609. (with Berrett, Christensen, Sain, Sandholtz, Coats and Tebaldi)
  25. Prior sensitivity analysis in a semi-parametric integer-valued time series model. Entropy, 2020, 22, 69. (with Graziadei, Lijoi, Marques and Prunster)
  26. Scalable semiparametric inference for the means of heavy-tailed distributions, In Tobias and Jeliazkov (Eds.), Advances in Econometrics: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling, 2019, Volume 40, Part B. (with Taddy and Gardner)
  27. Bayesian semi-parametric Markov switching stochastic volatility model, Applied Stochastic Models in Business and Industry, 2019, 35, 978-997. (with Virbickaite)
  28. Walk on the wild side: Multiplicative sunspots and temporarily unstable path, American Economic Review, 2019, 109, 1805-1842. (with Ascari and Bonomolo)
  29. Efficient sampling for Gaussian linear regression with arbitrary priors, Journal of Computational and Graphical Statistics, 2019, 28, 142-154. (with Hahn and He) + slides of most recent talk
  30. Particle learning for Bayesian semi-parametric stochastic volatility model, Econometric Reviews, 2019, 38, 1007-1023. (with Virbickaite, Ausin and Galeano)
  31. Dynamic models. In Gelfand, Fuentes, Hoeting and Smith (Eds.), Handbook of Environmental and Ecological Statistics, 2019, 57-80. Chapman & Hall. (with Schmidt)
  32. Bayesian hypothesis testing: Redux, Brazilian Journal of Probability and Statistics, 2019, 33, 745-755. (with Polson)
  33. On the long run volatility of stocks: time-varying predictive systems, Journal of the American Statistical Association, 2018, 113, 1050-1069. (with Carvalho and McCulloch)
  34. Bayesian factor model shrinkage for linear IV regression with many instruments, Journal of Business and Economic Statistics, 2018, 36(2), 278-287. (with Hahn and He)
  35. Sequential Bayesian learning for stochastic volatility with variance-gamma jumps in returns (with discussion), Applied Stochastic Models in Business and Industry, 2018, 34, 460-483. (with Warty and Polson).  Discussion by N. Ravishanker + Discussion by R. Soyer + Reply to the discussion.
  36. Efficient Bayesian inference for multivariate factor SV models, Journal of Computational and Graphical Statistics, 2017, 26, 905-917. (with Kastner and Fruehwirth-Schnatter)
  37. Tree-based Bayesian treatment effect analysis, Technical report, 2018. (with Santos)
  38. Cholesky realized stochastic volatility model, Econometrics and Statistics, 2017, 3, 34-59. (with Shirota, Omori and Piao)
  39. Particle learning for fat-tailed distributions, Econometric Reviews, 2016, 35, 1666-1691. (with Polson)

  40. Time-varying extreme pattern with dynamic models, Test, 2016, 26, 131-149. (with Nascimento and Gamerman)

  41. Bayesian instrumental variables: likelihoods and priors, Econometric Reviews, 2014, 33, 100-121. (with Polson)
  42. Treatment effects: a Bayesian perspective, Econometric Reviews, 2014, 33, 36-67. (with Heckman and Piatek)
  43. Modern Bayesian factor analysis. In Jeliazkov and Yang (Eds.), 2014, Bayesian Inference in the Social Sciences, 117-158. New York: Wiley.
  44. Online Bayesian learning in dynamic models: An illustrative introduction to particle methods. In West, Damien, Dellaportas, Polson and Stephens (Eds.), Bayesian Theory and Applications, 2013, 203-228. Clarendon: Oxford University Press. (with Carvalho)
  45. Evaluation and analysis of sequential parameter learning methods in Markov switching stochastic volatility models. In Zeng and Wu (Eds.), State-Space Models and Applications in Economics and Finance, 2013, 23-61. (with Rios)
  46. Sequential parameter learning and filtering in structured AR models, Statistics and Computing, 2013, 23, 43-57. (with Prado)
  47. Analysis of exchange rates via multivariate Bayesian factor stochastic volatility models.  In Lanzarone and Leva (Eds.), The Contribution of Young Researchers to Bayesian Statistics, 2013, 181-186. (with Kastner and Fruhwirth-Schnatter)
  48. Tracking epidemics with Google Flu Trends data and a state-space SEIR model, Journal of the American Statistical Association, 2012, 107, 1410-1426. (with Dukic and Polson)
  49. Measuring vulnerability via spatially hierarchical factor models, Annals of Applied Statistics, 2012, 6, 284-303. (with Schmidt, Salazar, Gomez and Achkar)
  50. A semiparametric Bayesian approach to extreme value estimation, Statistics and Computing, 2012, 22, 661-675. (with Nascimento and Gamerman)
  51. Bayesian statistics with a smile: a resampling-sampling perspective, Brazilian Journal of Probability and Statistics, 2012, 26, 358-371. (with Polson and Carvalho)
  52. Segmental dataset and whole body expression data do not support the hypothesis that non-random movement is an intrinsic property of Drosophila retrogenes, BMC Evolutionary Biology, 2012, 12, 169. (with Vibranovski, Zhang, Kemkemer, VanKuren, Karr and Long)
  53. Reanalysis of the larval testis data on meiotic sex chromosome inactivation revealed evidence for tissue-specific gene expression related to the Drosophila X chromosome, BMC Biology, 2012, 10, 49. (with Vibranovski, Zhang, Kemkemer, Karr and Long)
  54. Particle learning for sequential Bayesian computation (with discussion), Bayesian Statistics 9, 2011, 317-360. (with Carvalho, Johannes and Polson).
  55. Particle filters and Bayesian inference in financial econometrics, Journal of Forecasting, 2011, 30, 168-209. (with Tsay)(R code)
  56. Generalized spatial dynamic factor models, Computational Statistics and Data Analysis, 2011, 55, 1319-1330. (with Gamerman and Salazar).
  57. Confronting prior convictions: On issues of prior and likelihood sensitivity in Bayesian analysis, Annual Review of Economics, 2011, 3, 107-131. (with Tobias)
  58. Regression models for exceedance data via the full likelihood, Environmental and Ecological Statistics, 2011, 18, 495-512. (with Nascimento and Gamerman)
  59. Dynamic stock selection strategies: a structured factor model approach (with discussion), Bayesian Statistics 9, 2011, 69-90. (with Carvalho and Aguilar)
  60. Bayesian mixture of parametric and nonparametric density estimation: A misspecification Problem, Brazilian Review of Econometrics, 2011, 31, 19-44. (with Dias)
  61. Credit granting to small firms: a Brazilian case, Journal of Business Research, 2011, 64, 309-315. (with Zambaldi, Aranha and Politi)
  62. Particle learning and smoothing, Statistical Science, 2010, 25, 88-106. (with Carvalho, Johannes and Polson)
  63. Particle learning for general mixtures, Bayesian Analysis, 2010, 5, 709-740. (with Carvalho, Polson and Taddy).
  64. Time-varying joint distributions through copulas, Computational Statistics and Data Analysis, 2010, 54, 2383-2399. (with Ausin)
  65. Bayesian modeling of financial returns: a relationship between volatility and trading volume, Applied Stochastic Models in Business and Industry, 2010, 26, 172-193. (with Abanto and Migon)
  66. Direct evidence for postmeiotic transcription during Drosophila melanogaster spermatogenesis, Genetics, 2010, 186, 431-33. (with Vibranovski, Chalopin, Long and Karr)
  67. Extracting SP500 and NASDAQ volatility: The credit crisis of 2007-2008, in O’Hagan, A. and West, M. (Eds.), Handbook of Applied Bayesian Analysis, 2010, 319-342. (with Polson)
  68. Bayesian computation in finance, in Chen, M.-H., Dey, D., Mueller, P., Sun, D. and Ye, K. (Eds.)Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, 383-396. (with Hore, Johannes, McCulloch and Polson)
  69. Bayesian inference for stochastic volatility modeling, in Bocker, K. (Ed.) Rethinking Risk Measurement and Reporting: Uncertainty, Bayesian Analysis and Expert Judgement, 2010, 515-551. (with Polson)
  70. Bayesian prediction of risk measurements using copulas, in Bocker, K. (Ed.) Rethinking Risk Measurement and Reporting: Uncertainty, Bayesian Analysis and Expert Judgement, 2010, 553-578. (with Ausin)
  71. Constructing economically justified aggregates: An application of the early origins of health, 2010, Technical Report, University of Chicago Booth School of Business  (with Piatek, Conti and Heckman) – Also here.
  72. Stage-specific expression of Drosophila spermatogenesis suggests that meiotic sex chromosome inactivation drives the genomic relocation of testis-expressed genes, PLoS Genetics, 2009, 5, e1000731. (with Vibranovski, Karr and Long) (SpermPress page)
  73. Put option implied risk-premia in general equilibrium under recursive preferences, 2008, Technical Report, University of Chicago Booth School of Business  (with Hore and McCulloch)
  74. Sequential Monte Carlo estimation of DSGE models, 2008, Technical Report, University of Chicago Booth School of Business  (with Chen and Petralia)
  75. Spatial dynamic factor models, Bayesian Analysis, 2008, 3, 759-92. (with Salazar and Gamerman)
  76. Copula, marginal distributions and model selection: A Bayesian note, Statistics and Computing, 2008, 18, 313-20. (with Silva)
  77. Factor stochastic volatility with time varying loadings and Markov switching regimes, Journal of Statistical Planning and Inference, 2007, 137, 3082-3091. (with Carvalho)
  78. Simulation-based sequential analysis of Markov switching stochastic volatility models, Computational Statistics and Data Analysis, 2007, 51, 4526-4542. (with Carvalho)
  79. Bayesian computational methods in biomedical research, in Khattree and Naik (Eds.) Computational Methods in Biomedical Research, Marcel Dekker/Taylor & Francis, 2007, 211-59. (with Mueller and Ravishanker)
  80. Bayesian estimation of ruin probabilities with heterogeneous and heavy-tailed insurance claim size distribution, Australian & New Zealand Journal of Statistics, 2007, 49, 415-34. (with Ausin)
  81. Bayesian model uncertainty in smooth transition autoregressions, Journal of Time Series Analysis, 2006, 27, 99-117. (with Salazar)
  82. Time series mean level and stochastic volatility modeling by smooth transition autoregressions: a Bayesian approach, In Fomby (Ed.) Advances in Econometrics: Econometric Analysis of Financial and Economic Time Series/Part B, 2006, Volume 20, 229-242. (with Salazar)
  83. The extended generalized inverse Gaussian distribution for log-linear and stochastic volatility models, Brazilian Journal of Probability and Statistics, 2006, 20, 67-91. (with Silva and Migon)
  84. Spatio-temporal models for mapping the Incidence of malaria in Para, Environmetrics, 2005, 16, 291-304. (with Nobre and Schmidt) (Abstract)
  85. Dynamic models, In Dey and Rao (Eds.), Handbook of Statistics, Volume 25: Bayesian Thinking, Modeling and Computation, 2005, Chapter 19, 553-588. (with Migon, Gamerman and Ferreira)
  86. Factor stochastic volatility with time-varying loadings, Estadística, 57, 168-169, pp. 75-91.
  87. Bayesian model assessment in factor analysis, Statistica Sinica, 2004, 14, 41-67. (with West)
  88. Bayesian analysis of extreme events with threshold estimation , Statistical Modelling, 2004, 4, 227-244. (with Behrens and Gamerman)
  89. Data driven estimates for mixtures, Computational Statistics and Data Analysis, 2004, 47, 583-598. (with Mendes)
  90. Bayesian meta-analysis for longitudinal data models using multivariate mixture priors, Biometrics, 2003, 59, 66-75. (with Mueller and Rosner)
  91. Expected posterior priors in factor analysis, Brazilian Journal of Probability and Statistics, 2003, 17, 91-105.
  92. Factor models: an annotated bibliography, ISBA Bulletin, June 2003, 7-10.
  93. Co-movements and contagion in emergent markets: stock indexes volatilities, In Gatsonis, Kass, Carlin, Carriquiry, Gelman, Verdinelli and West (Eds.), Case Studies in Bayesian Statistics, 2002, Volume VI, 285-300, Springer-Verlag. (with Migon)
  94. Bayesian forecasting and inference in latent structure for the Brazilian industrial production index, Brazilian Review of Econometrics, 2000, 20, 1-26. (with Huerta)
  95. Hyperparameter estimation in forecasting models, Computational statistics and data analysis, 1999, 29, pp. 387-410. (with Moreira and Schmidt)
  96. Um modelo para a previsão conjunta do PIB, inflação e liquidez, Brazilian Review of Econometrics, 1997, 17, 67-111. (with Moreira and Fiorencio)
  97. Tendência estocástica do produto no Brasil: efeitos das flutuações da taxa de crescimento da produtividade e da taxa de juro real, Pesquisa e Planejamento Econômico, 1995, 25, 249-278. (with Rocha-Lima, Moreira and Pereira) (also here)
  98. Efeitos dinâmicos dos choques de oferta e demanda agregada sobre o nível de atividade econômica do Brasil, Revista Brasileira de Economia, 1993, 47, 177-204. (with Migon and Rocha-Lima) (also here)