We use available methods for testing macro models to evaluate a model of China over the period from Deng Xiaoping's reforms up until the crisis period. Bayesian ranking methods are heavily influenced by controversial priors on the degree of price/wage rigidity. When the overall models are tested...

We use available methods for testing macro models to evaluate a model of China over the period from Deng Xiaoping's reforms up until the crisis period. Bayesian ranking methods are heavily influenced by controversial priors on the degree of price/wage rigidity. When the overall models are tested...

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model that accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for...

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those...

We consider mean-variance portfolio choice of a robust investor. The investor receives advice from J experts, each with a different prior for the distribution of returns. Confronted with these multiple priors the investor follows a min-max portfolio strategy. We study the structure of the robust...

We study the mechanics of transmission of fiscal shocks to labour markets. We characterize a set of robust implications following government consumption, investment and employment shocks in a RBC and a New-Keynesian model and use part of them to identify shocks in the data. In line with the...

The paper proposes a technique to test jointly for groupings of unknown size in the cross-sectional dimension of a panel and estimates the parameters of each group, applying it to identifying convergence clubs in income per-capita. The approach uses the predictive density of the data,...