Technical Reports

  1. Bartolucci, F., Peracchi, F., and Terlizzese, D. (2021), A note of caution in interpreting crosscountry correlations of COVID-19 vaccination and infection, Working Paper EIEF 18.

  2. Pandolfi, S., Bartolucci, F., and Pennoni, F. (2021), Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout, https://arxiv.org/abs/2106.15948.

  3. Foricna, A. and Bartolucci, F. (2021), Estimating the size of a closed population by modeling latent and observed heterogeneity, https://arxiv.org/abs/2106.03811.

  4. Pennoni, F., Bartolucci, F., Forte, G., and Ametrano, F. (2020), Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model, MPRA paper n. 106150.

  5. Genge, W. and Bartolucci, F. (2019), Are attitudes towards immigration changing in Europe? An analysis based on bidimensional latent class IRT models, MPRA paper n. 94672.

  6. Bartolucci, F. and Pigini, C. (2019), Partial effects estimation for fixed-effects logit panel data models, MPRA paper n. 92251.

  7. Tullio, F. and Bartolucci, F. (2018), Evaluating time-varying treatment effects in latent Markov models: An application to the effect of remittances on poverty dynamics, https://ideas.repec.org/p/pra/mprapa/91459.html.

  8. Bartolucci, F., Peluso, S., and Mira, A. (2018), Marginal models with individual-specific effects for the analysis of longitudinal bipartite networks, https://arxiv.org/abs/1810.08778.

  9. Bacci, S., Bartolucci, F., Grilli, L., and Rampichini, C. (2016), Evaluation of student proficiency through a multidimensional finite mixture IRT model, https://arxiv.org/pdf/1609.06465.pdf.

  10. Bartolucci, F., Marino, F., and Pandolfi, S. (2015), Composite likelihood inference for hidden Markov models for dynamic networks, MPRA paper n. 67242.

  11. Bacci, S., Bartolucci, F., and Pandolfi, S. (2015), A joint model for longitudinal and survival data based on an AR(1) latent process, http://www.econ.unipg.it/files/generale/wp_2015/WP_14_2015_Bacci_Bartolucci_Pandolfi.pdf.

  12. Bartolucci, F., Bacci, S. & Pigini, C. (2015), A misspecification test for finite-mixture logistic models for clustered binary and ordered responses, https://mpra.ub.uni-muenchen.de/64220/1/MPRA_paper_64220.pdf.

  13. Bartolucci, F. and Pigini, C. (2015), cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models, https://mpra.ub.uni-muenchen.de/67030/

  14. Bartolucci, F., Pennoni, F., and Vittadini, G. (2015), Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies, https://mpra.ub.uni-muenchen.de/66492/

  15. Bartolucci, F., Chiaromonte, F., Kuruppumullage Don, P., and Lindsay, B. G. (2015), Composite likelihood inference in a discrete latent variable model for two-way "clustering-by-segmentation" problems, http://arxiv.org/abs/1506.08278

  16. Bartolucci, F., Farcomeni, A., Pandolfi, S. and Pennoni, F. (2015), LMest: an R package for latent Markov models for categorical longitudinal data, http://arxiv.org/abs/1501.04448

  17. Minelli, L., Pigini, C., Chiavarini, M. & Bartolucci, F. (2014), Employment status and perceived health condition: longitudinal data from Italy, http://ideas.repec.org/p/pra/mprapa/55788.html

  18. Bartolucci, F., Montanari, G. E. and Pandolfi, S. (2014), Three-step estimation of latent Markov models with covariates, http://arxiv.org/pdf/1402.1033v1.pdf

  19. Bartolucci, F., Nigro, V. and Pgini, C. (2013), Testing for state dependence in binary panel data with individual covariates, http://mpra.ub.uni-muenchen.de/48233/

  20. Cagnone, S. and Bartolucci, F. (2013): Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data, http://mpra.ub.uni-muenchen.de/51037/.

  21. Bartolucci, F. and Farcomeni, A. (2013), A discrete time event-history approach to informative drop-out in multivariate latent Markov models with covariates, http://arxiv.org/abs/1306.1678

  22. Bacci, S. and Bartolucci, F. (2013), A multidimensional latent class IRT model for non-ignorable missing responses, http://ssrn.com/abstract=2213721

  23. Gnaldi, M., Bartolucci, F. and Bacci, S. (2012), Joint Assessment of the Differential Item Functioning and Latent Trait Dimensionality of Students' National Tests, http://arxiv.org/abs/1212.0378

  24. Bartolucci, F. and Farcomeni, A. (2012), Causal inference in paired two-arm experimental studies under non-compliance with application to prognosis of myocardial infarction, http://arxiv.org/abs/1210.6678

  25. Bartolucci, F., Bacci, S.and Gnaldi, M. (2012), MultiLCIRT: An R package for multidimensional latent class item response models, http://arxiv.org/abs/1210.5267

  26. Bartoucci, F., Montanari, G. E. and Pandolfi, S. (2012), Item Selection by an Extended Latent Class Model: An Application to Nursing Homes Evaluation, http://ssrn.com/abstract=2040719

  27. Bacci, S., Bartoucci, F. and Pieroni, L. (2012), A Causal Analysis of Mother’s Education on Birth Inequalities, http://ssrn.com/abstract=2017489, http://arxiv.org/abs/1212.0372, http://mpra.ub.uni-muenchen.de/38754/

  28. Bartolucci, F., Bellio, R., Salvan, A. and Sartori, N. (2012), Modified Profile Likelihood for Panel Data Models, http://ssrn.com/abstract=2000666

  29. Bacci, S., Bartolucci, F. and Gnaldi, M. (2012), A class of Multidimensional Latent Class IRT models for ordinal polytomous item responses, http://arxiv.org/abs/1201.4667

  30. Bartolucci, F., Farcomeni, A. and Pennoni, F. (2012), "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023

  31. Bartolucci, F., Bacci, S., and Pennoni, F. (2011), Mixture latent autoregressive models for longitudinal data, http://arXiv:1108.1498v1

  32. Bartolucci, F. and Pandolfi, S. (2010), Bayesian inference for a class of latent Markov models for categorical longitudinal data, http://arxiv.org/PS_cache/arxiv/pdf/1101/1101.0391v2.pdf

  33. Bartolucci, F., d'Agostino, G. and Montanari, G. E. (2010), An investigation of the discriminant power and dimensionality of items used for assessing health condition of elderly people, http://arxiv.org/abs/1008.3268

  34. Pandolfi, S., Bartolucci, F. and Friel, N. (2010), A generalized Multiple-try Metropolis version of the Reversible Jump algorithm, http://arxiv.org/abs/1006.0621.

  35. Bartolucci, F., Farcomeni, A. and Pennoni, F. (2010), An overview of latent Markov models for longitudinal categorical data, http://arxiv.org/abs/1003.2804.