We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR...

Determining good parameter estimates in ESTAR models is known to be difficult. We showthat the phenomena of getting strongly biased estimators is a consequence of the so-calledidentication problem, the problem of properly distinguishing the transition function in relationto extreme parameter...

While it is widely agreed that Purchasing Power Parity (PPP) holds as a long-runconcept the specific dynamic driving the process is largely build upon a priori economicbelief rather than a thorough statistical modeling procedure. The two prevailing timeseries models, i.e. the exponential smooth...

We study the empirical behaviour of semi-parametric log-periodogram estimation forlong memory models when the true process exhibits a change in persistence. Simulationresults confirm theoretical arguments which suggest that evidence for long memory islikely to be found...

The paper provides a proof of consistency of the ridge estimator for regressions where the number of regressors tends to infinity. Such result is obtained without assuming a factor structure. A Monte Carlo study suggests that shrinkage autoregressive models can lead to very substantial...

Models based on economic theory have serious problems at forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a...

We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...