Package: MSBVAR
Version: 0.9-3
Date: 2016-11-14
Title: Markov-Switching, Bayesian, Vector Autoregression Models
Author: Patrick T. Brandt
Maintainer: Patrick T. Brandt
Depends: R (>= 2.11.0), KernSmooth, xtable, coda, bit, mvtnorm
Description: Provides methods for estimating frequentist and
Bayesian Vector Autoregression (VAR) models. Functions for reduced
form and structural VAR models are also available. Includes
methods for the generating posterior inferences for VAR
forecasts, impulse responses (using likelihood-based error bands),
and forecast error decompositions. Also includes utility functions
for plotting forecasts and impulse responses, and generating draws
from Wishart and singular multivariate normal densities. Current
version includes functionality to build models with
Markov switching.
LazyLoad: yes
License: MIT
URL: http://www.utdallas.edu/~pbrandt/
/* Revisions and History */
2005-03-17 Initial version
2005-07-21 Revised to new site and location. Revised forecasting
functions to match the Political Analysis paper by Brandt
and Freeman.
2005-09-15 Revised documentation and code to pass checks for generics.
Included Israeli-Palestinian dataset for examples.
2005-10-18 Incorporates preliminary functions for Bayesian SVAR models.
2006-01-19 Updated calls to Matrix package so they do not default to
using sparse matrices in MSBVAR functions.
2006-01-29 Revised impulse response plotting functions to plot shocks
/ impulses in the columns and responses in the rows. The
code was documented to work this way, but delivered an
incorrect plot. Also changed the irf.var() function so
that it used an UPPER triangular A0 matrix rather than a
lower one (the former is consistent with the return from
chol()).
2006-02-01 Updated plotting functions for IRFs to use correct ylims
for response plots.
2006-02-02 Removed creation of "L" in the LU decomposition used to
backsolve structural VAR systems in "drawA0". The U's from
the lu(t(A0)) are valid -- sometimes the Matrix library
cannot generate a well behaved L, but it is not necessary
for the Gibbs sampler.
2006-02-10 Changed starting value computation for structural.szbvar()
from a set of N(0,25) to N(0,1).
Added a tolerance of 1e-12 to QR decompositions in drawing
A(0) for B-SVAR models.
2006-02-18 Version 0.1.6
Replaced LU decompositions with QR
decompositions in the computation of the orthonormal basis
of the B-SVAR model posterior sampling.
Removed dependency on matrix package by replacing LU
decompositions with QR decompositions
2006-02-24 Corrected limits for plots in plot.irf.var(). Previous
versions could truncate the plot axes incorrectly.
2006-03-02 Corrected an error in the computation of the marginal data
density for B-SVAR models. Optimized code for computing
posterior fit measures for
posterior.fit.structural.szbvar()
2006-03-07 Corrected overflow error in some computations of the log
determinants in posterior.fit.structural.szbvar() by
changing the computation of the log determinant for a
diagonal matrix to the simpler sum of the diagonal
elements or using the determinant(,logarithm=T) function.
2006-03-15 Version 0.1.9
1) Added option to change normalization rules for A(0) in
sampling the structural parameters of B-SVAR models.
Previous versions had hard coded the normalization. New
version supports all of the normalization rules in
normalize.svar(). Documentation for this change has been
updated as well.
2006-03-15 Version 0.1.10
1) Corrected matrix inversion of A(0) in
mc.irf.structural.szbvar() and mcmc.structural.szbvar().
Previous version were possibly zeroing out A(0) terms
incorrectly when computing IRFs. This did NOT affect the
sampling of IRFs using gibbs.A0.structural.szbvar().
2) Corrected indexing of AR coefficients and intercepts for
the output object in reduced.form.var(). Previous version
selected the wrong coefficients for the intercept and AR(1)
terms.
2006-03-17 Version 0.1.11
Changed order of arguments in gibbs.A0.structural.szbvar()
so that the normalization options do not break older
code.
2006-03-26 Added forced matrix via as.matrix for testing Ui object
dimensions in b2a function in hidden.R
2006-04-01 Version 0.1.12
Now requires "coda" package.
Flattened the A0 and W objects that are output by gibbs.A0
function to conserve memory. Lowers the memory overhead
for B-SVAR models by nearly 70% allowing for longer runs of
the Gibbs sampler.
Added function "A02mcmc" to convert A0 posterior draws to
coda mcmc objects.
2006-04-06 Changed selection criteria in var.lag.specification to
include the Hannan-Quinn measure and to minimize fit
criteria.
2006-04-07 Added a check on the rank of the exogenous variables to the
structural.szbvar() function.
2006-06-18 Implemented classes for VAR, BVAR, and B-SVAR models.
Class based dispatching is now available for most
post-estimation tasks such as impulse responses, Monte
Carlo sampling, plotting, etc. Some function names have
changed from previous (post 0.1.1) versions. Please be
aware of this.
2006-07-15 Revised documentation to acccount for new class-based
dispatching.
2006-09-19 Corrected incorrect handling of intercepts in
reduced.form.var() function. Previous version thought the
intercept was the first, when it should be the last
coefficients.
2007-06-18 C++ implementations added
2007-10-23 Bug fixes: corrected summary() methods for VAR, BVAR and
BSVAR classes and fixed bugs in gibbs.A0(), the Gibbs
sampler for B-SVAR models. Thanks to Tatevik Sekhposyan
for helping find this bug.
2007-10-29 Updated uc.forecast() to allow for forecasts with exogenous
regressors. The previous version would crash if there were
exogenous regressors to be included in the forecast
horizon.
2007-12-06 Fixed classes / defaults for plot.mc.irf() related
functions. Previous versions had hard coded values rather
than allowing the user to control the arguments. Thanks
to Mike Colaresi for finding this bug.
2008-04-22 Updated defaults to plot.mc.irf to properly pass user
arguments (which may have been arbitrarily ignored in the
past). Also updated the sanity.check.prior() functions
that are internal to the B-(S)VAR estimators. Some of
these recommendations were non-sensical or required
exogenous variables (a very VAR taboo).
2008-10-10 Upgraded version to 0.3.2, requires R 2.7.0 or higher,
updated UTD address info, and copyright dates.
Cleaned up empty documentation sections.
2008-10-13 Implemented storing of state matrices for MS functions with
the "bits" package.
2008-10-19 Fixed a but in the szbvar() code for the prior degrees of
freedom computation.
2008-11-20 Fixed scaling of prior for the intercept (lambda4) in the
szbsvar() function. Previously is was entering the prior
covariance as lambda4 rather than lambda4^2
2008-12-11 Revised mc.irf.BSVAR() to use means rather than medians for
central tendency for IRFs.
2009-06-12 Updated documentation to function under R 2.9 and the new
Rd parsing. Included MSBVAR estimation (initial cut) using
msbvar() and related functions. This will handle the 2
state MS case with random permutation sampling. Added
documentation for the new functions as well.
2010-02-24 Added classing mechanism to the forecast() function. It
can now handle VAR, BVAR and BSVAR models. Related summary()
and plot() methods have been added for forecast(). Also
updated the msbvar() and gibbs.msbvar() functions to handle
h>2 states. New C backend for the state-space sampling has
been implemented and is much faster and more robust (i.e.,
it will not segfault like the old one).
2010-03-29 Implemented a set of posterior fit measures (llf, logMDD,
etc.) for the MSBVAR models.
2010-06-10 Changed prior variance computation of szbvar() and
szbsvar() to used OLS and estimate an intercept. This
better cover the case of non-stationary data or data that
are not demeaned, since we are not forcing the prior
intercepts to zero when computing the variance of the
prior. Thanks to Gareth Thomas for comments on this.
2010-06-23 Added full documentation for the MSBVAR model gibbs
sampler. Example included for gibbs.msbvar().
2010-11-28 Updated NAMESPACE and DESCRIPTION to comply with R 2.12.0.
Update BCF citations in documentation.
Update BCFdata files to fit documentation and published
papers.
Updated version to 0.5.1.
2011-01-13 Added attribute objects to VAR, BVAR, MSBVAR models so we
have sensible variable names to use in the plots and
analyses.
2011-01-27 Added plotregimeid() function to plot post-posterior
sampling clustered draws from the Gibbs random permutation
sampler. This can be used for graphic identification of the
Markov-switching regimes based on intercepts and variances
of the VAR equations. Since there are h! orderings of the
regimes (and identical posterior modes) such a function
helps identify a single posterior mode for refined
sampling.
2011-01-31 Updated package to version 0.6-0 to reflect milestones in
MSBVAR development. Added initial documentation for
plotregimeid().
2011-02-11 Began works on forecasting MSBVAR models. This is part of
a wholesale rewrite of how the forecasts are computed,
presented and summarized in the package.
2011-03-01 Corrected sample indexing for the msbvar() function. It
was carrying along one extra observation that should have
been use for computing lags rather than part of the data.
Changes down-stream functions like gibbs.msbvar(), etc. for
compliance.
2011-03-04 Degrees of freedom corrections in the prior and the
posterior computations for the msbvar() models.
2011-12-07 Adjusted the underflow threshold for regime probabilities
from 10^{-30} to 10^{-100} to improve the accuracy of
regime classifications in the C++ code.
2012-01-20 Updated MSBVAR model functions to use revised compiled
Fortran code that is more numerically accurate. New code
follows Fruhwirth-Schnatter's (2006) recommendations for MS
models. Includes new code for MSBVAR models with all
switching parameters, posterior summaries, etc. See code
and package details.
2012-04-24 Revised computations of Sigma x (X'X)^{-1} computations in
msbvar() to be more robust to near singularities.
2012-07-21 Version 0.7-0 ready for release, including new code for
msbvar() modeling
2012-07-25 Version 0.7-1, corrects a bug in Fortran allocations.
2012-07-26 Updated to correct Fortran allocation error. Change
initialization of msvar() function. Corrects how the state
space filter compiles across archs.
2014-05-12 Corrected "extern C" errors in C++ code. Version 0.9-0
2014-06-09 *Bug fixes*
Corrected summary.BSVAR to include "p" (long-standing bug,
output now works).
New startup message code.
Added some gc() calls for memory cleanup after some
intensive functions.
Added par() reset to plotregimeid() so subsequent plot
calls have the correct layouts and expected margins.
Corrected startup of plotregimeid so it now produces
a plot rather an a user prompt for the first one.
*Features*
Added correct time series axis to plot.SS
Separated code to compute IRF modes and error bands from
the plotting. This makes the package easier to maintain
and extend for other methods and plotting.
Added impulse response functions for the MSBVAR models via
plot.mc.irf().
Added plot.ms.irf() functions as an experiment in
combining IRFs across regimes in one plot.
2014-06-20 *Features*
regimeSummary() command added to provide summary statistics
for estimated MSBVAR regime probability densities and
durations.
Added forecast() example for MSBVAR models
*Bug fixes*
Added length check on sign.list in mc.irf for BSVAR models.
Added dimension check on prior transition matrix to MSBVAR
models,
2015-02-04 Updated DESCRIPTION files.
Added wrapper.c so that RNG calls point to unif_rand for
use in the FFBS.f Fortran code. This way the RNG is all
seeded by R.
2016-11-14 Updates to correct use of return in hidden.R
MSBVAR Package
Copyright (C) 2005-2016 Patrick T. Brandt
Support provided by the U.S. National Science Foundation
(Grants SES-0351179, SES-0351205, SES-0540816, and SES-0921051)
Patrick T. Brandt
School of Economic, Political and Policy Sciences
University of Texas, Dallas
800 W. Campbell Rd, Green 31
Richardson, Texas 75080
http://www.utdallas.edu/~pbrandt
pbrandt@utdallas.edu