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...

The predictive likelihood is useful for ranking models in forecast comparison exercises using Bayesian inference. We discuss how it can be estimated, by means of marginalization, for any subset of the observables in linear Gaussian state-space models. We compare macroeconomic density forecasts for the euro area of a DSGE model to those of a DSGE-VAR, a BVAR, and a multivariate random walk over ...

چکیده مقدمه: اکثر محصولاتی که امروزه تولید می¬شوند نتیجه فرآیندهای چندمرحله¬ای هستند، به این معنی که فرآیندهای چند متغیره¬ای هستند که طی چندین مرحله متوالی انجام می¬شوند که در هر مرحله یک یا چندین مشخصه کیفی مختلف مطابق با مشخصات فرآیند می¬تواند جهت کنترل کردن انتخاب شوند. هدف از انجام این تحقیق تعیین پارامترهای موثر بر کیفیت محصول شکر سفید و ارایه یک مدل برای پیش بینی کیفیت محصول تولیدی می باش...

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...