We highlight how detrending within Structural Vector Autoregressions (SVAR) is directly linked to the shock identification. Consequences of trend misspecification are investigated using a prototypical Real Business Cycle model as the Data Generating Process. Decomposing the different sources of...

This investigation aims to explain and quantify the deviations of the Taylor Rule. A novel three-step econometric procedure designed to reflect the data-rich environment in which central banks operate is proposed using information for 229 macroeconomic series. This procedure can be applied to...

This paper examines the interdependence of China’s policy uncertainty, the global oil market, and stock market returns in China. A structural VAR model is estimated that shows a positive shock to economic policy uncertainty in China has a delayed negative effect on global oil production, real...

This paper examines the impact of structural oil price shocks on the covariance of U.S. stock market return and stock market volatility. We construct from daily data on return and volatility the covariance of return and volatility at monthly frequency. The measures of daily volatility are...

Jointly identifying the effects of both fiscal and monetary policy shocks in an open economy structural VAR poses identification challenges. The innovations in this paper are to combine the methods of identification via sign restrictions, cointegration and traditional exclusion restrictions...

This paper quantifies the relative contribution of domestic, regional and international factors to the fluctuation of domestic output in six key Latin American (LA) countries: Argentina, Bolivia, Brazil, Chile, Mexico and Peru. Using quarterly data over the period 1980:1-2003:4, a multi-variate,...

Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting...

We consider an adaptive importance sampling approach to estimating the marginal likelihood, a quantity that is fundamental in Bayesian model comparison and Bayesian model averaging. This approach is motivated by the difficulty of obtaining an accurate estimate through existing algorithms that...

The empirical support for a DSGE type of real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure that makes use of a finite mixture of many models within the class of vector autoregressive (VAR) processes. The linear VAR model is extended to...

We survey Bayesian methods for estimating dynamic stochastic general equilibrium (DSGE) models in this article. We focus on New Keynesian (NK)DSGE models because of the interest shown in this class of models by economists in academic and policy-making institutions. This interest stems from the...