Books
De Luca, G., and Magnus J. R. (2027, In progress). From the Normal Location Model to Weighted-Average Least Squares Model Averaging. CRC Press, Boca Raton.
Research articles
De Luca, G. (2008). SNP and SML estimation of univariate and bivariate binary-choice models. Stata Journal, 8: 190-220.
De Luca, G., and Magnus, J. R. (2011). Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues. Stata Journal, 11: 518-544.
De Luca, G., and Perotti, V. (2011). Estimation of ordered response models with sample selection. Stata Journal, 11: 213-239.
Dardanoni, V., De Luca, G., Modica, S., and Peracchi, F. (2012). A generalized missing-indicator approach to regression with imputed covariates. Stata Journal, 12: 575-604.
De Luca, G., and Peracchi, F. (2012). Estimating Engel curves under unit and item nonresponse. Journal of Applied Econometrics, 27: 1076-1099.
De Luca, G., Rossetti, C., and Vuri, D. (2014). In-work benefits for married couples: an ex-ante evaluation of EITC and WTC policies in Italy. IZA Journal of Labor Policy, 3: 23.
Dardanoni, V., De Luca, G., Modica, S., and Peracchi, F. (2015). Model averaging estimation of generalized linear models with imputed covariates. Journal of Econometrics, 184: 452-463.
Magnus, J. R., and De Luca, G. (2016). Weighted-average least squares (WALS): A survey. Journal of Economic Surveys, 30: 117-148.
De Luca, G., Magnus, J. R., and Peracchi, F. (2018). Weighted-average least squares estimation of generalized linear models. Journal of Econometrics, 204: 1-17.
De Luca, G., Magnus, J. R., and Peracchi, F. (2018). Balanced variable addition in linear models. Journal of Economic Surveys, 32: 1183-1200.
De Luca, G., Magnus, J. R., and Peracchi, F. (2019). Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”. Journal of Business & Economic Statistics, 37: 217-222.
De Luca, G., Magnus, J. R., and Peracchi, F. (2021). Posterior moments and quantiles for the normal location model with Laplace prior. Communications in Statistics Theory and Methods, 50(17), 4039-4049.
De Luca, G., and Magnus, J. R. (2021). Weak versus strong dominance of shrinkage estimators. Journal of Quantitative Economics, 19, 239-266.
De Luca, G., Magnus, J. R., and Peracchi, F. (2022). Sampling properties of the Bayesian posterior mean with an application to WALS estimation. Journal of Econometrics, 230, 299-317.
De Luca, G., Magnus, J. R., and Peracchi, F. (2023). Weighted-average least squares (WALS): Confidence and prediction intervals. Computational Economics, 61, 1637-1664.
De Luca, G., and Magnus, J. R. (2024). Shrinkage efficiency bounds: An extension. Communications in Statistics Theory and Methods, 53(11), 4147-4152.
De Luca, G., Magnus, J. R., and Peracchi, F. (2025). Bayesian estimation of the normal location model: A non-standard approach. Oxford Bulletin of Economics and Statistics, 87, 913-923.
De Luca, G., Magnus, J. R., and Vasnev, A. L. (2025). Maximum likelihood estimation of the linear model with equicorrelated errors. Communications in Statistics - Theory and Methods, 54, 6295-6302.
De Luca, G., and Magnus, J. R. (2025b). Weighted-average least squares: improvements and extensions. The Stata Journal, 25(3), 587-626.
De Luca, G. and Magnus, J. R. (2025c). Weighted-average least squares: Beyond the simple linear regression model. The Stata Journal, 25(4), 1-40.
Chapters in books
De Luca, G., and Magnus, J. R. (2025a). Weighted-average least squares estimation of panel data models. In: M. Arashi, A., and Norouzirad, M. (eds), Advances in Shrinkage and Penalized Estimation Strategies: Honoring the Contributions of A. K. Md. Ehsanes Saleh, Emerging Topics in Statistics and Biostatistics. Springer, New York.
Chapters in the SHARE methodology books
De Luca, G., and Lipps, O. (2005). Fieldwork and sample management in SHARE. In Borsch-Supan, A., and Jurges, H. (ed.), p. 75-81. The Survey of Health, Aging, and Retirement in Europe-Methodology, Mannheim.
De Luca, G., and Peracchi, F. (2005). Survey participation in the first wave of SHARE. In Borsch-Supan, A., and Jurges, H. (ed.), p. 88-104. The Survey of Health, Aging, and Retirement in Europe-Methodology, Mannheim.
De Luca, G., and Rossetti, C. (2008). Sampling design and weighting strategies in the second wave of SHARE. In Borsch-Supan, A., Brugiavini, A., Jurges, H., Mackenbach, J., Siegrist, J., and Weber, G. (ed.), p. 333-338. Health, Aging and Retirement in Europe (2004-2007) - Starting the Longitutinal Dimension, Mannheim.
Lynn, P., De Luca, G., Ganninger, M. and Hader, S. (2012). Sampling design in SHARE wave four. In Malter, F., and Borsch-Supan, A. (ed.), SHARE Wave 4: Innovations & Methodology, p. 74-123. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
De Luca, G., Celidoni, M. and Trevisan, E. (2015). Item nonresponse and imputation strategies in SHARE wave 5. In Malter, F., and Borsch-Supan, A. (ed.), SHARE Wave 5: Innovations & Methodology, p. 85-100. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
De Luca, G., Rossetti, C., and Malter, F. (2015). Sample design and weighting strategies in SHARE wave 5. In Malter, F., and Borsch-Supan, A. (ed.), SHARE Wave 5: Innovations & Methodology, p. 75-84. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
Bergmann, M., De Luca, G., and Scherpenzeel, A. (2017). Sample design and weighting strategies in SHARE wave 6. In Malter, F., and Borsch-Supan, A. (ed.), SHARE Wave 6: Panel Innovations and Collecting Dried Blood Spots, p. 77-93. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
Bergmann, M., Bethmann, A., and De Luca, G. (2019). Sampling design in SHARE wave 7. In Bergmann, M., Scherpenzeel, A., and and Borsch-Supan, A. (ed.), SHARE Wave 7 Methodology: Panel Innovations and Life Histories, Chapter 5, p. 81-87. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
De Luca, G., and Rossetti, C. (2019). Weights and imputations. In Bergmann, M., Scherpenzeel, A., and Borsch-Supan, A. (ed.), SHARE Wave 7 Methodology: Panel Innovations and Life Histories, Chapter 9, p. 167-189. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
Bergmann, M., Bethmann, A., De Luca, G. (2022). Sampling design in SHARE wave 8 and recruitment of refreshment samples until the suspension of fieldwork. In Bergmann, M., and Borsch-Supan, A. (ed.), SHARE Wave 8 Methodology: Collecting Cross-National Survey Data in Times of COVID-19, Chapter 2, p. 23-30. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
De Luca, G., Li Donni, P., Rashidi, M. (2022). Weights and imputations in SHARE wave 8. In Bergmann, M., and Borsch-Supan, A. (ed.), SHARE Wave 8 Methodology: Collecting Cross-National Survey Data in Times of COVID-19, Chapter 6, p. 133-145. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
Bergmann, M., Bethmann, A., De Luca, G. (2022). Sampling for the first SHARE Corona survey after the suspension of fieldwork in wave 8. In Bergmann, M., and Borsch-Supan, A. (ed.), SHARE Wave 8 Methodology: Collecting Cross-National Survey Data in Times of COVID-19, Chapter 7, p. 149-151. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.
De Luca, G., Li Donni, P., Rashidi, M. (2022). Weights and imputations in the first SHARE Corona survey. In Bergmann, M., and Borsch-Supan, A. (ed.), SHARE Wave 8 Methodology: Collecting Cross-National Survey Data in Times of COVID-19, Chapter 11, p. 175-178. Munich: Munich Center for the Economics of Aging, Max Planck Institute for Social Law and Social Policy.