Using a structural time-varying-parameter Bayesian vector autoregression (TVP-BVAR) framework, this paper documents that oil price increases caused by supply disruptions did not affect food commodity prices before the start of millennium, but had positive spillover effects in more recent periods and particularly era around Great Recession. Likewise, shortfalls global resulting from bad harvests...

شرایط افروزش و اشتعال سوخت‌های گداخت هسته‌ای در حضور ناخالص­‌ها، یکی از موارد مهمی است که طراحی قرص‌های سوخت باید مورد توجه قرار گیرد. این مقاله اثر ناخالصی هسته سنگین طلا بر کلیه فرایندهای احتراق پلاسمای غیرتعادلی دوتریم- تریتیم مدل چهار دمایی آن شکل تابع توزیع انرژی رفتار فوتون‌ها تأثیر می‌گذارد بررسی گرفته است. مطالعه شامل اثرات منفی سوخت، طول زمان تشکیل لکه داغ نتایج محاسبات عددی به­ دست آم...

Abstract The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To address these issues, we propose BVAR models with outlier-augmented stochastic volatility (SV) combine transitory persistent changes in volatility. resulting density are much less sensitive outliers the than BVARs. Predictive B...

We incorporate external information extracted from the European Central Bank’s Survey of Professional Forecasters into predictions a Bayesian VAR using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve plain BVAR point density forecasts. Importantly, we do not restrict at specific quarterly horizon but their possible paths over several horizons jo...

Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to making these highly parameterized useful is the use of shrinkage priors. We develop a family priors that captures best features two prominent classes priors: adaptive hierarchical and Minnesota Like priors, new ensure only ‘small’ coefficients strongly shrunk zero, while ‘large’ remain i...