Lutz Kilian and Xiaoqing Zhou, "Oil Prices, Gasoline Prices and Inflation Expectations", Journal of Applied Econometrics, Vol. 37, No. 5, 2022, pp. 867-881. All code is in MATLAB. The raw data are provided in ASCII format as *.txt files in the respective subdirectories. The data frequency in \var is monthly and the estimation period is 1981.7-2020.4. The data frequency in \table1 is monthly and the estimation period is 1990.1-2020.4. The data frequency in \phillipscurve is quarterly and the estimation period matches Coibion and Gorodnichenko (2015). (a) Subdirectory \var: The files figure1a figure1b figure2 figureF1 figureF2 figureG2 reproduce the corresponding figures in the paper and in the online appendix. The files inflexp_vdc inflexp_vdc_chol also produce the variance decomposition estimates reported in the text. The files counterfactual_inflexp counterfactual_inflexp_chol compute the cumulative changes in the gasoline price component and the corresponding cumulative changes in inflation expectations and in the counterfactual, as reported in the text. To re-generate the posterior simulations underlying these results execute in the order given the following files: Baseline model: constructdata main_posterior0 arrw_importance_sampler0 figureirf0 prephd_inflexp figurehd_inflexp counterfactual_inflexp inflexp_vdc Alternative partially identified block recursive model: constructdata_chol main_posterior0_chol figureirf0_chol prephd_inflexp_chol figurehd_inflexp_chol counterfactual_inflexp_chol inflexp_vdc_chol (b) Subdirectory\phillipscurve Figure 3 and the data underying Table 2 are generated by phillips_cg The file counterfactual.mat was copied from \var after constructing the VAR counterfactual. (c) Subdirectory \table 1 To generate the four columns of Table 1 execute table1a table1b table1c table1d