The goal of MSBVAR is to …
You can install the released version of MSBVAR fromfrom GitHub with:
if (!require("devtools")) install.packages("devtools")
devtools::install_github("tulipsliu/MSBVAR",dependencies = TRUE)This is a basic example which shows you how to solve a common problem:
library(MSBVAR)
#> ##
#> ## MSBVAR Package v.0.9-2
#> ## Build date: Wed Nov 18 19:15:43 2020
#> ## Copyright (C) 2005-2020, Patrick T. Brandt
#> ## Written by Patrick T. Brandt
#> ##
#> ## Support provided by the U.S. National Science Foundation
#> ## (Grants SES-0351179, SES-0351205, SES-0540816, and SES-0921051)
#> ##
## basic example codeWhat is special about using README.Rmd instead of just README.md?
You can include R chunks like so:
Package: MSBVAR Version: 0.9-3 Date: 2016-11-14 Title: Markov-Switching, Bayesian, Vector Autoregression Models Author: Patrick T. Brandt pbrandt@utdallas.edu Maintainer: Patrick T. Brandt pbrandt@utdallas.edu 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 2020-11-18 Updates bu Daniel tulips liu 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)
2020-11-23 The package have not update many years.For example the 'bit' package update so that the 'MSBVAR' package`s SS.R file some function can not run by a right way.now,Daniel try to do something in order to let the package running. 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
Danielu Tulips Liu Renmin University of China bbs.pinggu.org/tulipsliu xudongliu520@vip.qq.com