abstract in the recent decades, the iranian economy has been highly depended on oil revenues. considering the fact that a great part of non-oil exports are agricultures product, studying factors influencing growth of agricultural sector plays an important role in the iran's economy. supply domestic shocks and domestic demand pressure along with deviation of exchange rates from its equilibrium, ...

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...

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...

The use of renewable energy reduces environmental pollution and leads to achievement of sustainable development. The current study investigates the dynamic interrelationship between sustainable development, renewable and non-renewable energies and environment nexus by applying Bayesian vector autoregression (BVAR) and impulse response functions in Iran with an annual data frequency for the time...

Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods often employed to deal with their dense parameterization, imposing structure on model coefficients via prior information. The optimal choice of the degree informativeness implied by these priors is subject much debate can be approached hierarc...

We show that the Bayesian evidence framework can be applied to both-support vector regression (-SVR) and-support vector regression (-SVR) algorithms. Standard SVR training can be regarded as performing level one inference of the evidence framework, while levels two and three allow automatic adjustments of the regularization and kernel parameters respectively, without the need of a validation set.

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 ...