RDP 2023-07: Identification and Inference under Narrative Restrictions Read me file

This ‘read me’ file contains instructions about how to replicate the results in RDP 2023-07.


The data underlying the empirical exercise in Section 7 of the paper were obtained from the replication files for Antolín-Díaz and Rubio-Ramírez (2018), available at <https://www.openicpsr.org/openicpsr/project/113168/version/V1/view>. These data are contained in Uhlig_Data_Updated.mat. Data used to plot the figures can be found in ‘rdp-2023-07-graph-data.xlsx’.


The results presented in the paper were obtained using Matlab R2023a on a desktop computer running Microsoft Windows 10 Enterprise with an Intel Core i7-9700 CPU @ 3.00GHz, 8 cores and 128 GB RAM. The Matlab code uses the Parallel Computing Toolbox and the Statistics and Machine Learning Toolbox.[1]

To replicate the results underlying Figures 1–5, run runAll.m.


Antolín-Díaz J and JF Rubio-Ramírez (2018), ‘Narrative Sign Restrictions for SVARs’, The American Economic Review, 108(10), pp 2802–2829.

26 October 2023


Researchers without access to the Parallel Computing Toolbox can run the code after replacing ‘parfor’ with ‘for’ in drawQs_ar.m within the auxFunctions folder. Researchers without access to the Statistics and Machine Learning Toolbox could run the code after writing their own functions to draw random variables from the inverse Wishart distribution (replacing Matlab's iwishrnd function) and to compute sample percentiles (replacing Matlab's prctile function) or quantiles (replacing Matlab's quantile function); this would require modifying mainfile.m, credibleRegion.m and highestPosteriorDensity.m in the auxFunctions folder. [1]

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