We develop a Bayesian methodology for systemic risk assessment in financial networks such as the interbank market. Nodes represent participants in the network and weighted directed edges represent liabilities. Often, for every participant, only the total liabilities and total assets within this...

As the amount of economic and other data generated worldwide increases vastly, a challenge for future generations of econometricians will be to master efficient algorithms for inference in empirical models with large information sets. This Chapter provides a review of popular estimation...

In a context where European stock prices have been trending upwards, one of the main concerns is that stocks perceived as more sustainable from an environmental, social and governance (ESG) perspective could be particularly exposed to exuberance. To shed some light on the magnitude of the...

We provide a toolkit for efficient online estimation of heterogeneous agent (HA) New Keynesian (NK) models based on Sequential Monte Carlo methods. We use this toolkit to compare the out-of-sample forecasting accuracy of a prominent HANK model, Bayer et al. (2022), to that of the representative...

We develop a generally applicable full-information inference method for heterogeneous agent models, combining aggregate time series data and repeated cross-sections of micro data. To handle unobserved aggregate state variables that affect cross-sectional distributions, we compute a numerically...

Putting a price on carbon - with taxes or developing carbon markets - is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density forecasts for the monthly real price of carbon within the...

In this paper, we assess whether key relations between US interest rates have been stable over time. This is done by estimating trivariate hybrid time-varying parameter Bayesian VAR models with stochastic volatility for the three-month Treasury bill rate, the slope of the Treasury yield curve...

In this paper we review the methodology of forecasting with log-linearised DSGE models using Bayesian methods. We focus on the estimation of their predictive distributions, with special attention being paid to the mean and the covariance matrix of h-steps ahead forecasts. In the empirical...

We propose a new methodology for the Bayesian analysis of nonlinear non-Gaussian state space models with a Gaussian time-varying signal, where the signal is a function of a possibly high-dimensional state vector. The novelty of our approach is the development of proposal densities for the joint...

The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models...