Christian Gross and Pierre Siklos, "Analyzing Credit Risk Transmission
to the Non-Financial Sector in Europe: A Network Approach", Journal of
Applied Econometrics, Vol. 35, No. 1, 2020, pp. 61-81.

Thee are two data files, which are zipped in the file readme.gs.txt.
Both files are ASCII files in DOS format. Unix/Linux users should use
"unzip -a".

The file raw_data.csv contains the daily raw series for the 152 CDS
spreads over the period Oct 2006 -- July 2017 that are used in the
paper. Each column represents one series (plus one date vector) and
the variables are organized block-wise according to the sector of each
CDS: columns 2-110 are CDS for non-financial corporations, 111-143 are
financial firms, and 144-153 are sovereigns. The data are taken from
Datastream and Bloomberg. The file serves as input for running the
generalized dynamic factor model by Forni et al.  as described in
Section 2.1 and Appendix A. The Matlab code for estimating the
generalized dynamic factor model can be found on Matteo Barigozzi's
website: www.barigozzi.eu.

The file idiosyncratic_full_sample.csv contains the idiosyncratic
returns for each variable after the dynamic factor model has been 
applied to the raw data. The file has the same structure and number of
columns as the file with the raw data (152 variables plus the date
vector). The first two observations are lost relative to the raw data
due to first-differencing and the lag in the dynamic factor model. 

The file idiosyncratic_full_sample is used as input for the static
estimation of the large-dimensional VAR as described in Sections 
2.2-2.4. Hence, these data are used for the results reported in
Section 4.1 (Figures 1-4, A6, A8-A11 and Tables 1-3, A4). The results 
for the dynamic estimation framework (Section 4.2, Figures 5-9 and
A3-A5, A7, A12) are obtained by estimating the dynamic factor model 
repeatedly for each window based on the file raw_data.csv and using
the resulting idiosyncratic returns as input in the rolling-window 
VAR estimation.

Christian Gross, June 2019