Sparse Graphical Vector Autoregression: A Bayesian Approach

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...

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...

Oil dependence, institutional quality and economic growth: A panel vector autoregression approach

Resources are the foundation of economic growth. With speedy economic and population growth, economic growth is facing a scarcity of resources worldwide. Resource-economy co-ordination has become every government’s main focus in reaching strategic development goals in countries that are on the path of rapid economic development. Sustainable economic development in a country requires resources a...

Bayesian Learning of Sparse Gaussian Graphical Models

Sparse inverse covariance matrix modeling is an important tool for learning relationships among different variables in a Gaussian graph. Most existing algorithms are based on `1 regularization, with the regularization parameters tuned via cross-validation. In this paper, a Bayesian formulation of the problem is proposed, where the regularization parameters are inferred adaptively and cross-vali...

Lecture 6 : Vector Autoregression ∗