Jacobi, L., & Zhu, D. (2024). Estimating Posterior Sensitivities with Application to Structural Analysis of Bayesian Vector Autoregressions , forthcoming Journal of Business & Economic Statistics, 1-16.
Jacobi, L., Kwok, Jackson, Ramírez-Hassan, A. & Nghiem, N. (2024). Posterior manifolds over prior parameter regions: beyond pointwise sensitivity assessments for posterior statistics from MCMC Inference. Forthcoming Studies in Nonlinear Dynamics & Econometrics, 28(2), 403-434.
Wagner, H., Fruehwirth-Schnatter, S. & Jacobi, L. (2023). Factor-augmented Bayesian treatment effects models for panel outcomes. Econometrics and Statistics, 28, pp.63-80.
Chan, J. C., Jacobi, L., & Zhu, D. (2022). Automated Prior Sensitivity Analysis for Marginal Likelihood Estimation. Journal of Applied Econometrics 37(3), 583-602.
Jacobi, L., Nghiem, N., Ramírez-Hassan, A. & Blakely, T. (2021). Food Price Elasticities for Policy Interventions: Estimates from a Virtual Supermarket Experiment in a Multistage Demand Analysis with (Expert) Prior Information. Economic Record 97, 457-490 (https://doi.org/10.1111/1475-4932.12640).
Chan, J. C., Jacobi, L., & Zhu, D. (2020). Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation. Journal of Forecasting 39 (6), 934-943.
Wagner, H., Fruehwirth-Schnatter, S & Jacobi, L. (2020). Bayesian modelling of treatment effects on panel outcomes. Forthcoming Proceedings 35th International Statistical Modelling Workshop.
Chan, J. C., Jacobi, L., & Zhu, D. (2019). Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation. Forthcoming Journal of Forecasting.
Chan, J. C., Jacobi, L., & Zhu, D. (2019). How sensitive are VAR forecasts to prior hyperparameters? An automated sensitivity analysis. Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, Advances in Econometrics, 40, 229-248.
Jacobi, L., Sovinsky, M., Marijuana on Main Street? Estimating Demand in Markets with Limited Access. American Economic Review, 2016, 106(8), 2009-2045.
Jacobi, L., Wagner, H., Fruehwirth-Schnatter, S., Bayesian Treatment Effects Models with Variable Selection for Panel Outcomes with an Application to Earnings Effects of Maternity Leave. Journal of Econometrics, 2016, 193(1), 234-250.
Chib, S., Jacobi, L,. Bayesian Fuzzy Regression Discontinuity Analysis and Returns to Compulsory Schooling. Journal of Applied Econometrics, 2016, 31(6), 1026-1047.
Waterlander, W. E., Blakely, T., Nghiem, N., Cleghorn, C. L., Eyles, H., Genc, M., Wilson, N., Jian, Y., Swinburn, B., Jacobi, L., Michie, J., Mhurchu, C. , Study protocol: combining experimental methods, econometrics and simulation modelling to determine price elasticities for studying food taxes and subsidies (The Price ExaM Study). BMC Public Health, 2016, 16(1), 601.
Bretteville-Jensen, A.L., Jacobi, L., Climbing the Drug Staircase: A Bayesian Analysis of the Initiation of Hard Drug Use. Journal of Applied Econometrics, 2011, 26 (7), 1157-1186.
Chib, S., Jacobi, L., Calculating Causal Effects from Panel Data in Randomized Experiments with Partial Compliance. Advances in Econometrics, vol. 23, 2009, Jai Press, Elsevier Science, Amsterdam, 183-215.
Chib, S., Jacobi, L. - Analysis of Treatment Response Data from Eligibility Designs. Journal of Econometrics, 2008, 144, 465-478.
Chib, S., Jacobi, L., Modelling and Calculating the Effect of Treatment at Baseline from Panel Outcomes. Journal of Econometrics, 2007, 140, 781-801.
Jacobi, L., Marijuana on Main Street: Consumption and tax revenue in a legalised environment. Exchange, 2016, 3, 2-3.
Jacobi, L., Review of "Introduction to Bayesian Econometrics," by Edward Greenberg, Economic Record, 2009, 85, 270, 364-366.