The paper presents an applied study of a multivariate AR(p) process fitted to daily data from U.S. commodity futures markets with the use of Bayesian statistics. In the first part a detailed description of the methods used is given. In the second part two BVAR models are chosen one with assumption of lognormal, the second with normal distribution of prices conditioned on the parameters. For a c...

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

‎This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...

1 Chris Laing, Southampton Institute, Technology Faculty, Southampton, SO14 0YN, UK, [email protected] 2 Alan Robinson, Southampton Institute, Technology Faculty, Southampton, SO14 0YN, UK, [email protected] Abstract  Previously it has been proposed that explanations of non-traditional withdrawal might be defined by the underlying characteristics of the teaching and learn...

This study extends the existing forecasting accuracy debate in the tourism literature by examining the forecasting performance of various vector autoregressive (VAR) models. In particular, this study seeks to ascertain whether the introduction of the Bayesian restrictions (priors) to the unrestricted VAR process would lead to an improvement in forecasting performance in terms of achieving a hig...

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...

This article compares the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian vector error correction (BVEC) models in forecasting the exchange rates for ve Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Polish Zloty, Slovak Koruna and Slovenian Tolar) agains...

This paper investigates the effect of a positive technology shock on per capita hours worked within the class of Bayesian Vector Auto-Regressive [BVAR] models. Such a framework avoids the current debate regarding the specification issue of per capita hours [level versus first-difference stationary]. Six priors are considered and for each, we examine the impulse responses of per capita hours fol...

This paper develops a Bayesian Vector Error Correction Model (BVECM) for forecasting inventory investment in South Africa. The model is estimated using quarterly data on actual sales, production, unfilled orders, price levels and interest rates, for the period of 1978 to 2000. The out-of-sample-forecast accuracy obtained from the BVECM, over the forecasting horizon of 2001:1 to 2003:4, is compa...