Macroeconomic data are subject to revisions as later vintages released. Yet, the usual way of generating real-time density forecasts from BVAR models makes no allowance for this form uncertainty. We evaluate two methods that consider uncertainty when forecasting with with/without stochastic volatility. First, model is estimated on vintages. Second, a included, so on, and conditioned estimates r...

The domestic and international transmission mechanism of fiscal policy shocks are analysed in the United States Germany. Using a Bayesian VAR approach, we find that both these countries expansion is associated with increases output as well private consumption investment. terms trade, which affect shocks, depreciate response to expansion, thus transferring some increased purchasing power abroad....

Este trabajo cuantifica el impacto de un conjunto choques externos sobre la economía peruana, los cuales son caracterizados como: i) demanda, ii) oferta, iii) financieros, y iv) precios exportación. Utilizando datos entre 1995 2019, se estiman modelos VAR bayesianos con bloque exógeno, identificados mediante restricciones ceros signos. Los resultados sugieren que peruana está altamente expuesta...

This paper quantifies and assesses the impact of an adverse loan supply (LS) shock on Peru's main macroeconomic aggregates using a Bayesian vector autoregressive (BVAR) model in combination with identification scheme sign restrictions. The results indicate that LS shock: (i) reduces credit real GDP growth by 372 75 basis points period, respectively; (ii) explains 11.2% variability average over ...

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...

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...