Economic policy makers, international organisations and private-sector forecasters commonly use short-term forecasts of real GDP growth based on monthly indicators, such as industrial production, retail sales and confidence surveys. An assessment of the reliability of such tools and of the...

This paper adopts the Impulse-Response methodology to understand inflation persistence. It has often been argued that existing models of pricing fail to explain the persistence that we observe. We adopt a common general framework which allows for an explicit modelling of the distribution of...

Banks increasingly use short-term wholesale funds to supplement traditional retail deposits. Existing literature mainly points to the "bright side" of wholesale funding: sophisticated financiers can monitor banks, disciplining bad but refinancing good ones. This paper models a "dark side" of...

We study the functioning of secured and unsecured inter-bank markets in the presence of credit risk. The model generates empirical predictions that are in line with developments during the 2007-2009 financial crises. Interest rates decouple across secured and unsecured markets following an...

We study the functioning and possible breakdown of the interbank market in the presence of counterparty risk. We allow banks to have private information about the risk of their assets. We show how banks’ asset risk affects funding liquidity in the interbank market. Several interbank market...

I study economies where banks do not fully internalize the social costs of default, which distorts their lending decisions. In all these economies, a common general equilibrium effect leads to aggregate over-investment. As a result, under laissez-faire, crises are too frequent and too costly...

This paper compares the predictive ability of the factor models of Stock and Watson (2002) and Forni, Hallin, Lippi, and Reichlin (2005) using a "large" panel of US macroeconomic variables. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar...

Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative...

A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...

We define nowcasting as the prediction of the present, the very near future and the very recent past. Crucial in this process is to use timely monthly information in order to nowcast key economic variables, such as e.g. GDP, that are typically collected at low frequency and published with long...