For more replication code see the links to the relevant papers in my CV. If you are looking for something you expect to find here and it is not here, e-mail me.
Event data: As part of of our NSF RIDIR grant I am also an organizer of the software on event data generation at https://github.com/openeventdata and our event data server at http://eventdata.utdallas.edu.
Bayesian Poisson Vector Autoregression model code and examples. This replication code fits the models in Brandt and Sandler (2012). It used R and JAGS. See the documentation inside the replication materials for details.
MSBVAR R Package MS-BVAR = Markov-switching and Bayesian Vector Autoregression package for R. Provides methods for estimating frequentist, Bayesian Vector Autoregression (BVAR), Bayesian structural vector autoregression (B-SVAR) and reduced form Markov-switching Bayesian vector autoregression (MS-BVAR) models. Includes methids for generating posterior inferences for these (MS)BVAR models and their forecasts, impulse responses (using likelihood-based error bands), and forecast error decompositions. Also includes utility functions for plotting forecasts and impulse responses, and draws from Wishart and singular multivariate normal densities. This package is available on CRAN, so try “install.packages(“MSBVAR”)” in R!
PEST: Poisson Estimators for State-Space Time Series R, version 1.1.6 (Update 2017-06-19) This is the R implementation fo the Gauss PESTS code. This code will allow you to estimate the models in Brandt et al. (2000) and Brandt and Williams (2001). This ia a major rewrite from the initial GAUSS version in the papers since it uses standard R formula specifications. Also includes are functions for computing impact multipliers and responses for the PAR(p) model. See the examples below for how to use this code.
PESTS - R version sample program. This program illustrates how to simulate PEWMA and PAR(p) data and estimate the models in R using the pests.R script that is linked above.
Additional example files for fitting different event time time series in R:
- Transnational hostage taking example based on Brandt and Sandler (2009)
- Poisson changepoint modeling in R based on both simulated data and Brandt and Sandler (2009)
- Hyde Park purse snatching example for the PAR(p) model based on Brandt and Williams (2001)
- See also the examples at this link
Gauss Code for the PESTS models (legacy code that has not been updated in 15+ years). Use the R code version above is what I support and maintain now.