The paper examines over seventy episodes of current account adjustment in industrial and major emerging market economies. It argues that these episodes were characterised by strongly divergent economic developments. To reduce this divergence, the paper classifies episodes with similar...

The paper examines episodes of current account adjustment in individual economies. A central finding is that these episodes are very divergent and can be usefully classified, on the basis of cluster analysis, in three groups. A majority of cases is characterised by internal adjustment,...

This paper develops a new approach to change-point modeling that allows for an unknown number of change points in the observed sample. Our model assumes that regime durations have a Poisson distribution. The model approximately nests the two most common approaches: the time-varying parameter...

When nontraded goods prices are accounted for consistently and genuine stock data on bilateral foreign asset holdings is employed, a modified sticky-price exchange rate model by far outperforms the benchmark random walk-model in empirically forecasting the D-mark/dollar parity out of sample....

We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior...

We suggest a theoretical basis for the comparative evaluation of forecasts. Instead of the general assumption that the data is generated from a stochastic model, we classify three stages of prediction experiments: pure non-stochastic prediction of given data, stochastic prediction of given data,...

We investigate whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test-based procedure, which assigns non-zero weights to candidate models that add information not...