Skip to content

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.

master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 

README.txt

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.

Resources

License

Releases

No releases published

Packages

No packages published
You can’t perform that action at this time.