This paper develops a forecasting model for important macroeconomic variables in the state of Indiana. In this study, we specify a Bayesian Vector Autoregression (BVAR) model with Litterman’s prior. A comparison with the Vector Autoregression (VAR) model shows that BVAR improves forecast by reducing root mean square

We extend the vector autoregression (VAR) based expectations hypothesis test of term structure, considered in Bekaert & Hodrick (2001) using recent developments in bootstrap literature. Modifications include the use of wild bootstrap to allow for conditional heteroskedasticity in the VAR residuals without imposing strict parameterization, endogeneous model selection procedure in the bootstrap r...

A set of rigorous diagnostic techniques is used to evaluate the forecasting performance of five multivariate time-series models for the U.S. cattle sector. The root-meansquared-error criterion along with an evaluation of the rankings of forecast errors reveals that the Bayesian vector autoregression (BVAR) and the unrestricted VAR (UVAR) models generate forecasts which are superior to both a re...

We compare the performance of a subset of CBO’s economic forecasts against that of an unrestricted vector autoregression (VAR) model. We evaluate forecasts of real economic indicators as well as budget-related nominal statistics. We find that under most specifications, the VAR performs competitively with, if slightly worse than, the corresponding CBO forecasts at up to 20 quarters. Therefore, a...

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...

Bayesian methods are developed for the seemingly unrelated regressions (SUR) model where the model order or structure is presumed random. In particular we consider the class of models that are linear in some basis space. This class includes standard linear regression as a special case, as well as those models that involve non-linear transformations of the explanatory variables through a set of ...

This paper analyzes the transmission mechanisms of a contractionary monetary policy shock on the real economy. The sufficiently long regime uniform time period since the political transformation in the Czech Republic provides evidence for effective inflation targeting by the Czech National Bank. I apply a recursive vector autoregression (VAR), a structural VAR, and structural vector error corre...

In this paper, we apply a Vector AutoRegression (VAR) based trust model over the Backpressure Collection Protocol (BCP), a collection mechanism based on dynamic backpressure routing in Wireless Sensor Networks (WSN) and show that the VAR trust model is suited for resource constraint networks. The backpressure scheduling is known for being throughput-optimal. However, it is usually assumed that ...

This note corrects a mistake in the estimation algorithm of the time-varying structural vector autoregression model of Primiceri (2005) and proposes a new algorithm that correctly applies the procedure proposed by Kim, Shephard, and Chib (1998) to the estimation of VAR or DSGE models with stochastic volatility. Relative to Primiceri (2005), the correct algorithm involves a different ordering of...

"The dynamic relationship of net migration and employment change is examined for ten selected states of the U.S. using a multivariate time series approach--a vector autoregression (VAR) model. Granger causality tests and dynamic multipliers provide information on the dynamic process. The results suggest a state-level process in which employment change occurs first, and net migration follows wi...