Evaluating a Global Vector Autoregression for Forecasting

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...

Evaluating Structural Vector Autoregression Models in Monetary Economies

This paper uses Monte Carlo simulations to evaluate alternative identi…cation strategies in VAR estimation of monetary models, and to assess the accuracy of measuring money instability as a cause of output ‡uctuations. I construct theoretical monetary economies using general equilibrium models with cash-in-advance constraints, which also include technology shocks, labor supply shocks, and monet...

A Time-Varying Parameter Vector Autoregression Model for Forecasting Emerging Market Exchange Rates

In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a blend of the flexible least squares and Kalman filter techniques. The out-of-sample forecasting perf...

Lecture 6 : Vector Autoregression ∗

Topology-regularized universal vector autoregression for traffic forecasting in large urban areas

Autonomous vehicles are soon to become ubiquitous in large urban areas, encompassing cities, suburbs and vast highway networks. In turn, this will bring new challenges to the existing traffic management expert systems. Concurrently, urban development is causing growth, thus changing the network structures. As such, a new generation of adaptive algorithms are needed, ones that learn in real-time...