We show how simple statistical techniques for capturing critical transitions used in natural sciences, fail to capture economic regime shifts. This implies that we need to use model-based approaches to identify critical transitions. We apply a heterogenous agents model in a standard housing...

Due to high and low volatility periods, time series of absolute returns experience temporary level shifts which differ in length and size. In this paper we modify the basic Censored Latent Effects Autoregressive [CLEAR] model, such that it can describe and forecast the location and size of such...

GARCH models and Stochastic Volatility (SV) models can both be used to describe unobserved volatility in asset returns. We consider the issue of testing a GARCH model against an SV model. For that purpose, we propose a new and parsimonious GARCH-t model with an additional restricted moving...

Agents involved in the formation of a social or economic network typically face uncertainty about the benefits of creating a link. However, the interplay of such uncertainty and risk attitudes has been neglected in the network formation literature. We propose a dynamic network formation model...

This paper provides a method for identifying when rising house prices are in danger of becoming bubbles on the verge of bursting. As a result, it is a first step towards establishing an early warning system for house price changes that could help prevent important welfare costs. By examining...

We introduce heterogeneous expectations in a standard housing market model linking housing rental levels to fundamental buying prices. Using quarterly data we estimate the model parameters for eight different countries, US, UK, NL, JP, CH, ES, SE and BE. We find that the data support...

This paper proposes a simple social network model of occupational segregation, generated by the existence of inbreeding bias among individuals of the same social group. If network referrals are important in getting a job, then expected inbreeding bias in the social structure results in different...

Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model,...