Density forecasts of euro area inflation are a fundamental input for a medium-term oriented central bank, such as the European Central Bank (ECB). We show that a quantile regression forest, capturing a general non-linear relationship between euro area (headline and core) inflation and a large...

This paper studies optimal monetary policy responses in an economy featuring sectorial heterogeneity in the frequency of price adjustments. It shows that a central bank facing heterogeneous nominal rigidities is more likely to behave less aggressively than in a fully sticky economy. Hence, the...

This paper shows that general equilibrium effects can partly rationalize the high correlation between saving and investment rates observed in OECD countries. We find that once controlling for general equilibrium effects the saving-retention coefficient remains high in the 70’s but decreases...

Standard accounts of the Great Depression attribute an important causal role to monetary policy errors in accounting for the catastrophic collapse in economic activity observed in the early 1930s. While views vary on the relative importance of money versus credit contraction in the propagation...

The main result in Svensson (2017) and its previous versions is that, given current knowledge and empirical estimates, the cost of using monetary policy to “lean against the wind” for financial-stability purposes exceeds the benefit by a substantial margin. Adrian and Liang (2016a) conduct a...

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large...

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of sample forecasts, particularly for models with many variables....