CCMMshadowrateVAR-code

Code for „Shadow-Rate VARs“ by Carriero, Clark, Marcellino and Mertens (2023)

Andrea Carriero (Queen Mary University of London), Todd Clark (Federal Reserve Bank of Cleveland), Massimiliano Marcellino (Bocconi University, IGIER and CEPR) and Elmar Mertens (Deutsche Bundesbank)

The usual disclaimers apply; the views and results conveyed in our research are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Cleveland, the Federal Reserve System, the Eurosystem, or the Deutsche Bundesbank.

The working paper and supplementary appendices are available here: https://www.elmarmertens.com/research/workingpapers#h.nqkg0ogemt4s

WORK IN PROGRESS

Directories

All core scripts are in the main directory. In addition, there are the following subdirectories:

• data for data construction, use generateFREDdata.m to produce input files, named fredMD*.csv, as needed by the estimation routines described further below
• matlabtoolbox for general utilities (also available at https://github.com/elmarmertens/em-matlabbox)

The default data file is fredblockMD20-2022-09.csv (based on the 2022-09 vintage of FRED-MD, available at https://research.stlouisfed.org/econ/mccracken/fred-databases/).

General notes

• All scripts set the MATLAB path to point to toolboxes in matlabtoolbox. In addition, most scripts collect output in a temporary directory, which is by default created as subfolder foo within the main directory. Edit localtemp.m to change the location of this temp directory. Output is collected in a LaTeX file, which is also compiled at the end of each script (provided a LaTeX installation can be found on the path). To control the compilation of output, please edit finishwrap.m. To avoid collecting output files, comment out the call to initwrap in each script (and make sure to define instead a variable called wrap that is set to empty).

• The code requires a recent version of Matlab (we used Matlab versions 2019a-2021a) including access to Matlab’s Statistics and Machine Learning Toolbox. The codes employ parfor loops that are executed in parallel when a parpool has been created in Matlab, which requires availability of the Matlab Parallel Computing Toolbox (otherwise the loops will be executed sequentially).

• The main directory contains a bash script gobatch.sh that can be used to launch a sequence of multiple Matlab scripts from the shell. The Matlab scripts are executed in sequence and in separate Matlab sessions. Each Matlab session opens a parallel pool. For example, the shell command sh gobatch.sh goVAR.m goVARshadowrateBlockHybrid.m goVARhybrid.m will launch a command line session of Matlab, start a parallel pool, and then execute goVAR.m; once goVAR.m has been executed, the Matlab sessions closes, a new one is reopened for execution of goVARshadowrateBlockHybrid.m etc. (The shell script supports as many command line arguments as supported by bash and has been written for use on macOS and Linux.) Alternatively, Matlab scripts can, of course, equally be called interactively on the Matlab GUI’s command line.

• To execute individual models for a given sample, use scripts called do*.m. To launch out-of-sample runs for a given model, consider scripts called go*.m. After computing the out-of-sample runs each of these go*.m scripts stores results for further post-processing in a *.mat file. To produce forecst comparison tables, please use Matlab scripts called oosEvaluationTables.m, which assume that previously stored *.mat files are stored in a directory indicated at the beginning of each of theses scripts.

Model names

To produce quasi-real estiamtes

• goVAR.m for the standard linear VAR
• goVARshadowrateBlockHybrid.m for the block-hybrid shadow-rate VAR (as described in the paper)
• goVARhybrid.m for the fully-hybrid shadow-rate VAR (as described in the paper)

Other

• doVARshadowrateBlockHybridMissingData.m provides estimates for a single data sample, with and without imposing the ELB onto the missing-data problem of the shadow-rate sample and producing comparison figures as shown in paper and supplementary appendix
• oosEvaluationTables2023.m produces forecast comparison tables based on output stored by the goVAR*shadowrate.m scripts
• showQRTshadow2023.m produces figures of shadow-rate estimates (full-sample and quasi-real-time) based on output stored by the goVAR*shadowrate.m scripts
• showPAIchanges.m produces comparison figures of VAR transition coefficients
• doMCMClinear.m, doMCMCshadowrateBlockHybrid.m, and doMCMChybrid.m, produce full-sample MCMC estimates of each model. To create IRF, run the analogously named generateGIRF*.m scripts. To produce plots use prettyplotGIRF.m.