A Large Bayesian Vector Autoregression Model for Russia

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...

Structured Regularization for Large Vector Autoregression

The vector autoregression (VAR), has long proven to be an effective method for modeling the joint dynamics of macroeconomic time series as well as forecasting. One of the major disadvantages of the VAR that has hindered its applicability is its heavy parameterization; the parameter space grows quadratically with the number of series included, quickly exhausting the available degrees of freedom....

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...

Large-Vector Autoregression for Multilayer Spatially Correlated Time Series

One of the most commonly used methods for modeling multivariate time series is the Vector Autoregressive Model (VAR). VAR is generally used to identify lead, lag and contemporaneous relationships describing Granger causality within and between time series. In this paper, we investigate VAR methodology for analyzing data consisting of multilayer time series which are spatially interdependent. Wh...

Lecture 6 : Vector Autoregression ∗