The goal of this paper is to analyze predictability of future asset returns in the context of model uncertainty. Using data for the euro area, the US and the U.K., we show that one can improve the forecasts of stock returns using a model averaging approach, and there is a large amount of model...

This paper investigates the link between fiscal policy shocks and movements in asset markets using a Fully Simultaneous System approach in a Bayesian framework. Building on the works of Blanchard and Perotti (2002), Leeper and Zha (2003), and Sims and Zha (1999, 2006), the empirical evidence for...

We investigate the macroeconomic effects of fiscal policy using a Bayesian Structural Vector Autoregression approach. We build on a recursive identification scheme, but we: (i) include the feedback from government debt (ii); look at the impact on the composition of output; (iii) assess the...

This paper empirically analyzes the political, institutional and economic sources of public deficit volatility. Using the system-GMM estimator for linear dynamic panel data models and a sample of 125 countries analyzed from 1980 to 2006, we show that higher public deficit volatility is typically...

This paper estimates the wealth effects on consumption in the euro area as a whole. I show that: (i) financial wealth effects are relatively large and statistically significant; (ii) housing wealth effects are virtually nil and not significant; (iii) consumption growth exhibits strong...

We use a new approach to assess long-term fiscal developments. By analyzing the time varying behaviour of the two components of government spending and revenue - responsiveness and persistence - we are able to infer about the sources of fiscal behaviour. Drawing on quarterly data we estimate...

We build a panel of 14 emerging economies to estimate the magnitude of housing, stock market, and money wealth effects on consumption. Using modern panel data econometric techniques and quarterly data for the period 1990:1-2008:2, we show that: (i) wealth effects are statistically significant...