A Bayesian Poisson Vector Autoregression Model

A Bayesian Poisson Vector Autoregression Model

Multivariate count models are rare in political science, despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression (BaP-VAR) model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects...

Poisson Autoregression

This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional variance, implying an interpretation as an integer valued GARCH process. In a nonlinear conditional Pois...

Forecasting State Tax Revenue: A Bayesian Vector Autoregression Approach By

This paper compares alternative time-series models to forecast state tax revenues. Forecast accuracy is compared to a benchmark random walk forecast. Quarterly data for California is used to forecast total tax revenue along with its three largest components, sales, income, and corporate tax revenue. For oneand four-quarter-ahead forecasts from 2004 to 2009, Bayesian vector autoregressions gener...

Nonlinear Poisson autoregression

We study statistical properties of a class of non-linear models for regression analysis of count time series. Under mild conditions, it is shown that a perturbed version of the model is geometrically ergodic and possesses moments of any order. This result turns out to be instrumental on deriving large sample properties of the maximum likelihood estimators of the regression parameters. The theor...

Lecture 6 : Vector Autoregression ∗