This repository contains a Matlab suite to implement the sup-t band and other popular simultaneous confidence bands in the environment described in the paper "Simultaneous Confidence Bands: Theory, Implementation, and an Application to SVARs", by Jose Luis Montiel Olea and Mikkel Plagborg-Møller; Journal of Applied Econometrics, 2018.
License
jm4474/Confidence_Bands
master
Name already in use
Code
-
Clone
Use Git or checkout with SVN using the web URL.
Work fast with our official CLI. Learn more.
- Open with GitHub Desktop
- Download ZIP
Sign In Required
Please sign in to use Codespaces.
Launching GitHub Desktop
If nothing happens, download GitHub Desktop and try again.
Launching GitHub Desktop
If nothing happens, download GitHub Desktop and try again.
Launching Xcode
If nothing happens, download Xcode and try again.
Launching Visual Studio Code
Your codespace will open once ready.
There was a problem preparing your codespace, please try again.
Latest commit
Git stats
Files
This README.txt file was generated on 08/08/2018 by José Luis Montiel Olea and Mikkel Plagborg-Moller ---------------------- i) GENERAL INFORMATION ---------------------- The folders 1SimInferenceClass 2Gertler_Karadi_application 3Head_Mayer_Ries_application 4Additional_Figures Reg_Sens VAR_IRF contain .csv files, Matlab scripts/functions/classes, and STATA do files to generate the figures reported in the paper "Simultaneous Confidence Bands: Theory, Implementation, and an Application to SVARs" by José Luis Montiel Olea and Mikkel Plagborg-Moller. -------------------------- ii) HARDWARE/SOFTWARE (specifications and requirements) -------------------------- All the files have been tested on both: * A MacBook Pro @2.4 GHz Intel Core i7 (8 GB 1600 MHz DDR3) running Matlab 2016b and Stata 13. * A Lenovo Thinkpad @2.3 GHz Intel Core i5 (8 GB RAM) running Matlab R2017a and Stata 15. -------------------------- iii) RECOMMENDED CITATION -------------------------- When using this code please cite: "Simultaneous Confidence Bands: Theory, Implementation, and an Application to SVARs", Montiel Olea, J.L. and Plagborg-Moller, Journal of Applied Econometrics, 2018. --------------------- iv) DATA & MAIN FILE OVERVIEW --------------------- * 1SimInferenceClass This folder contains the "SimInference.m" Matlab class file, which collects different Matlab functions that are used to create the sup-t band, and other popular simultaneous bands (such as Bonferroni, Sidak, and Projection). This Matlab class also contains a simple algorithm to implement the "calibrated" Bootstrap/Bayes sup-t band. NOTE: Both applications call the SimInference.m class. * 2Gertler_Karadi_application This folder contains the .csv files and Matlab scripts to replicate the figures related to the Gertler-Karadi Structural VAR application. The two main files for replication (both in the /Script folder) are: run_gk_iv.m run_gk_chol.m The first file replicates Figure 2 and the second file Figure 3 in the paper (simply run the files on the Matlab command window or section by section). NOTE: To generate Figure 6, simply change line 43 and 50 in run_gk_iv.m. To generate Figure 7, simply change line 44 and 51 in run_gk_chol.m * 3Head_Mayer_Ries_application This folder contains the Stata file and Matlab scripts to replicate the figures related to our sensitivity analysis for the Head-Mayer-Ries application. The main file for replication (in the /Script folder) is: run_hmr.m This file generates Figure 8 in the paper. NOTE: To run the Matlab file, you must perform the following three steps first: a) Download the following zip file: http://econ.sciences-po.fr/sites/default/files/file/tmayer/data/col_regfile09.zip b) Unzip the Stata data file "col_regfile09.dta" and place it in the subfolder 3Head_Mayer_Ries_application/Data c) Run the Stata do-file create.do in the subfolder 3Head_Mayer_Ries_application/Data These steps will create a large .csv file used by the above-mentioned Matlab script "run_hmr.m". The latter file is currently set to draw only 100 bootstrap and Bayes draws, which takes a couple of hours on a personal laptop. To increase the number of draws to 2,000 as in the paper, simply change lines 13 and 14 in "run_hmr.m". * 4Additional_Figures This folder contains Matlab scripts to replicate Figures 1, 4, and 5. --------------------- iv) Additional Folders --------------------- The folders Reg_Sens and VAR_IRF contain application-specific functions for the regression sensitivity analysis and for the VAR application.
About
This repository contains a Matlab suite to implement the sup-t band and other popular simultaneous confidence bands in the environment described in the paper "Simultaneous Confidence Bands: Theory, Implementation, and an Application to SVARs", by Jose Luis Montiel Olea and Mikkel Plagborg-Møller; Journal of Applied Econometrics, 2018.