Research

Publications

1. Ferrara, L. and Simoni, A. (2023). When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage. Journal of Business & Economic Statistics, Vol. 41, 1188-1202. [Paper] [Banque de France WP]
2. Chib, S., Greenberg, E. and Simoni, A. (2023). Nonparametric Bayes Analysis of the Sharp and Fuzzy Regression Discontinuity Designs. Econometric Theory, Vol. 39, 481-533.
3. Chib, S., Shin, M. and Simoni, A. (2022). Bayesian Estimation and Comparison of Conditional Moment Models. Journal of the Royal Statistical Society, Series B, Vol. 84, 740-764. [Paper]
4. Florens, J.P. and Simoni, A. (2021). Revisiting Identification Concepts in Bayesian Analysis. Annals of Economics and Statistics, Vol. 144, 1-38. [Paper]
5. Florens, J.P. and Simoni, A. (2021). Gaussian Processes and Bayesian Moment Estimation. Journal of Business & Economic Statistics, 39, 482-492. [Paper]
6. Mogliani, M. and Simoni, A. (2021). Bayesian MIDAS Penalized Regressions. Journal of Econometrics, 222, 833-860. [Paper]
7. Breunig, C., Mammen, E. and Simoni, A. (2020). Ill-posed Estimation in High-Dimensional Models with Instrumental Variables. Journal of Econometrics, 219, 171-200. [Paper]
8. Johannes, J., Simoni, A. and Schenk, R. (2020). Adaptive Bayesian estimation in indirect Gaussian sequence space models. Annals of Economics and Statistics, 137, 83-116 [Paper]
9. Liao, Y. and A., Simoni (2019), Bayesian Inference for Partially Identified Smooth Convex Models. Journal of Econometrics, 211, 338-360.
10. Chib, S., Shin, M. and A. Simoni (2018), Bayesian Empirical Likelihood Estimation and Comparison of Moment Condition Models. Journal of the American Statistical Association, 113, 1656-1668.
11. Breunig, C., Mammen, E. and A., Simoni (2018), Nonparametric estimation in case of endogenous selection. Journal of Econometrics, 202, 268-185.
12. Hoderlein, S., Nesheim L. and A., Simoni (2017), Semiparametric Estimation of Random Coefficients in Structural Economic Models, Econometric Theory, 33, 1265-1305.
13. Florens, J.P. and A., Simoni (2017), Introduction to the Special Issue on Inverse Problems in Econometrics, Annals of Economics and Statistics, 128, 1-3.
14. Florens, J.P. and A., Simoni (2016), Regularizing Priors for Linear Inverse Problems, Econometric Theory, 32, No. 1, 71-121.
15. Florens, J.P. and A., Simoni (2012), Nonparametric Estimation of an Instrumental Regression: a Quasi-Bayesian Approach Based on Regularized Posterior, Journal of Econometrics, 170, No. 2, 458-475.
16. Florens, J.P. and A., Simoni (2012), Regularized Posteriors in Linear Ill-posed Inverse Problems, Scandinavian Journal of Statistics, 39, No. 2, 214-235.

Contributions to Books

Works in Progress

1. Mogliani, M. and Simoni, A. (2024), Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting. ArXiv working paper: arXiv:2404.02671, revision requested by Journal of Econometrics.
2. Chib, S., Shin, M. and A. Simoni (2026), Testing for Endogeneity: A Moment-Based Bayesian Approach, revision requested by Econometric Theory.
3. Florens, J-P. and A. Simoni (2025), Panel data models with randomly generated groups. ArXiv working paper: ArXiv:2510.24496.
4. Doz, C., Ferrara, L. and Simoni, A. (2026), Impulse Response Function with functional approaches and mixed-frequency data, mimeo.
5. Doz, C., Ferrara, L. and Simoni, A. (2025), Nowcasting with functional approaches and mixed-frequency data, mimeo.
6. Ghysels, E. and Simoni, A. (2025), Every Second Counts: The Long Run Price Impact of Ultra-High Frequency Trading. Work in progress.
7. Mogliani, M. and Simoni, A. (2025), Dense versus grouped sparse models in Bayesian quantile nowcasting models with a large number of covariates. Work in progress.
8. Chopin, N., Fulop, A., Huo, Y., and Simoni, A. (2025), Conditional SMC for large Bayesian VAR with stochastic volatility. Work in progress.
9. Galtier, A., Lecué, G., Piquard, T., Poterszman, B., Simoni, A. (2025), Markov-Switching, Mixed Frequency and VAR. Work in progress.
10. Mogliani, M., Rossini, L. and Simoni, A. (2024), The importance of the tails in high dimensional macroeconomic forecasting, mimeo.