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...

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. The optimal shrinkage is chosen by maximizing the Marginal Likelihood of the model. Focusing o...

We examine the real-time forecasting performance of Bayesian VARs (BVARs) of different sizes using an unbalanced data panel. In a real-time out-of-sample forecasting exercise, we find that our BVAR methodology outperforms univariate and VAR benchmarks, and produces comparable forecast accuracy to the judgementally-adjusted forecasts produced internally at the Reserve Bank of New Zealand. We ana...

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...

This paper examines the nexus between economic growth and energy consumption shock, over period 1990-2020, in Iran. We employ a Bayesian vector autoregressive (BVAR) with new prior functions, which will give results more acceptable consequences than classical methods to study these relationships. estimates relationship shocks within multivariate BVAR framework by including gross capital formati...

Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. optimization is an iterative method where Gaussian process posterior underlying function sequentially updated new evaluations. We propose novel framework for situations user controls computational effort and therefore precision This common in econometrics likelihood computed Markov cha...