Modeling Mortality with a Bayesian Vector Autoregression

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...

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...

Lecture 6 : Vector Autoregression ∗

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....

A Vector Autoregression Weather Model for Electricity Supply and Demand Modeling

Weather forecasting is crucial to both the demand and supply sides of electricity markets. Temperature has a great effect on energy demand. Moreover, solar and wind are very promising renewable energy sources. In this paper, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. We estimate the VAR model with 16 y...