This paper revisits a number of data-rich prediction methods, like factor models, Bayesian ridge regression and forecast combinations, which are widely used in macroeconomic forecasting, and compares these with a lesser known alternative method: partial least squares regression. Under the...

We compare a number of data-rich prediction methods that are widely used in macroeconomic forecasting with a lesser known alternative: partial least squares (PLS) regression. In this method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the...

The problem of weak identification has recently attracted attention in the analysis of structural macroeconomic models. Using robust methods can result in large confidence sets making inference difficult. We overcome this problem in the analysis of a forward-looking Taylor rule by seeking...

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...

Die vorliegende Arbeit untersucht, wie sich Angebots-, Nachfrage- und geldpolitische Schocks aus den Vereinigten Staaten auf Deutschland übertragen. Dabei wird ein so genanntes factor-augmented vector autoregressive model (FAVAR) auf einen neu zusammengestellten Datensatz mit mehr als 200...

We propose a Nelson-Siegel type interest rate term structure model where the underlying yield factors follow autoregressive processes with stochastic volatility. The factor volatilities parsimoniously capture risk inherent to the term structure and are associated with the time-varying...

When the yield curve is modelled using an affine factor model, residuals may still contain relevant information and do not adhere to the familiar white noise assumption.This paper proposes a pragmatic way to improve out of sample performance for yield curve forecasting. The proposed adjustment...

We suggest an alternative use of disaggregate information to forecast the aggregate variable of interest, that is to include disaggregate information or disaggregate variables in the aggregate model as opposed to first forecasting the disaggregate variables separately and then aggregating those...

The US economy is often considered to play a pivotal role in global growth. Such a view has persisted despite the falling contribution of the US economy to global growth (from almost 30% in 1950 to around 20% at present). In this paper, we analyse the veracity of this conjecture and consider the...

Following Bai (2004) and Bai and Ng (2004) we estimate a common factor representation of a panel of output series for India, disaggregated by 15 states and 14 broad industry groups. We find that a single common V-Factor accounts for a large part of the significant shift in the cross-sectional...