*! version 1.0.1 21aug2026

Title

gvar references -- works cited in the gvar documentation

Every author-year citation in this package's help files resolves here. Entries
are grouped by what they are cited for, and each carries the digital object
identifier where one exists, so a reader can go from a claim in a help page to
the paper that supports it in one step.

The GVAR model

Pesaran, M. H., T. Schuermann and S. M. Weiner. 2004.
Modeling regional interdependencies using a global error-correcting
macroeconometric model.
Journal of Business & Economic Statistics 22(2): 129-162.
doi:10.1198/073500104000000019.

The original GVAR: country-specific error-correcting models linked by
trade-weighted foreign variables. The construction of x* in
gvar foreign and the link matrices in
gvar weights follow this paper.

Dees, S., F. di Mauro, M. H. Pesaran and L. V. Smith. 2007.
Exploring the international linkages of the euro area: a global VAR analysis.
Journal of Applied Econometrics 22(1): 1-38.
doi:10.1002/jae.932.

Referred to throughout as DdPS(2007). The source of the weak-exogeneity F test
in gvar wetest, of the device that makes a global variable
endogenous in one country (gendog() in gvar setup), and
of the 26-country specification shipped as the demo.

Estimation and inference

Pesaran, M. H., Y. Shin and R. J. Smith. 2000.
Structural analysis of vector error correction models with exogenous I(1)
variables.
Journal of Econometrics 97(2): 293-343.

The VECMX* framework: reduced-rank ML with weakly exogenous I(1) regressors, and
the cointegration critical values used by gvar coint. The
five deterministic cases of case() are this paper's.

Pesaran, M. H. and Y. Shin. 1998.
Generalized impulse response analysis in linear multivariate models.
Economics Letters 58(1): 17-29.

The generalized impulse responses of gvar irf and the
generalized decompositions of gvar fevd, which do not require
an ordering of the variables.

Bayesian methods

Crespo Cuaresma, J., M. Feldkircher and F. Huber. 2016.
Forecasting with global vector autoregressive models: a Bayesian approach.
Journal of Applied Econometrics 31(7): 1371-1391.
doi:10.1002/jae.2504.

Boeck, M., M. Feldkircher and F. Huber. 2022.
BGVAR: Bayesian global vector autoregressions with shrinkage priors in R.
Journal of Statistical Software 104(9): 1-28.
doi:10.18637/jss.v104.i09.

The companion paper to the R package this package's Bayesian branch follows.
The Minnesota, SSVS, Normal-Gamma and Horseshoe priors of
gvar bayes are the four it implements.

Kastner, G. and S. Fruehwirth-Schnatter. 2014.
Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC
estimation of stochastic volatility models.
Computational Statistics & Data Analysis 76: 408-423.
doi:10.1016/j.csda.2013.01.002.

The target for sv in gvar bayes. This package uses the
standard mixture sampler rather than the interweaved one, so mixing may be
slower -- which is what gvar bconv is for.

Geweke, J. 1992.
Evaluating the accuracy of sampling-based approaches to calculating posterior
moments. In Bayesian Statistics 4, ed. J. M. Bernardo, J. O. Berger,
A. P. Dawid and A. F. M. Smith. Oxford: Clarendon Press.

The convergence diagnostic reported by gvar bconv.

Spiegelhalter, D. J., N. G. Best, B. P. Carlin and A. van der Linde. 2002.
Bayesian measures of model complexity and fit.
Journal of the Royal Statistical Society, Series B 64(4): 583-639.

The deviance information criterion and the effective number of parameters
computed by gvar bdic.

Connectedness

Diebold, F. X. and K. Yilmaz. 2014.
On the network topology of variance decompositions: measuring the connectedness
of financial firms.
Journal of Econometrics 182(1): 119-134.
doi:10.1016/j.jeconom.2014.04.012.

The connectedness table of gvar spillover: directional
to and from each unit, and the total connectedness index.

Structural change

Zeileis, A., F. Leisch, K. Hornik and C. Kleiber. 2002.
strucchange: an R package for testing for structural change in linear
regression models.
Journal of Statistical Software 7(2): 1-38.
doi:10.18637/jss.v007.i02.

Zeileis, A., C. Kleiber, W. Kraemer and K. Hornik. 2003.
Testing and dating of structural changes in practice.
Computational Statistics & Data Analysis 44(1-2): 109-123.
doi:10.1016/S0167-9473(03)00030-6.

The empirical fluctuation process family behind
gvar stability.

Software this package follows

This package is a port. Where a computation could be read from a reference
implementation rather than inferred from a paper, it was; the help page for each
command names the file it follows under its Source heading.

GVAR Toolbox 2.0 (August 2014), L. V. Smith and A. Galesi. MATLAB. The
source for estimation, the weak-exogeneity test, the dominant unit model and the
bootstrap.

BGVAR 2.6.0, M. Boeck, M. Feldkircher and F. Huber. R. The source for the
Bayesian branch -- priors, stochastic volatility, and the DIC.

GVARX 1.2. R. Consulted for the GVAR estimation interface.

vars 1.6-1, B. Pfaff. R. See Pfaff, B. 2008. VAR, SVAR and SVEC models:
implementation within R package vars. Journal of Statistical Software
27(4). Consulted for the VAR diagnostics.

strucchange 1.6-0, A. Zeileis et al. R. The source for the fluctuation
tests, as cited above.

Where this package's output does not reproduce a reference implementation's,
that is documented rather than hidden: see
gvar methods, "Weak exogeneity: a known deviation".

Author

Dr Merwan Roudane

merwanroudane920@gmail.com

https://github.com/merwanroudane