We propose a method to produce density forecasts of the term structure of government bond yields that accounts for (i) the possible mispecification of an underlying Gaussian Affine Term Structure Model (GATSM) and (ii) the time varying volatility of interest rates. For this, we derive a Bayesian...

Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models is linear...

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes...

This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to different sampling frequencies and publication delays. Two model classes...

This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model speci…cation in the presence of mixed-frequency data, e.g., monthly and quarterly series. MIDAS leads to parsimonious models based on exponential lag polynomials for the coe¢ cients,...

Measuring and displaying uncertainty around path-forecasts, i.e. forecasts made in period T about the expected trajectory of a random variable in periods T+1 to T+H is a key ingredient for decision making under uncertainty. The probabilistic assessment about the set of possible trajectories that...

We propose a classical approach to estimate factor-augmented vector autoregressive (FAVAR) models with time variation in the factor loadings, in the factor dynamics, and in the variance-covariance matrix of innovations. When the time-varying FAVAR is estimated using a large quarterly dataset of...

The accession of ten countries into the European Union makes the forecasting of their key macroeconomic indicators an exercise of some importance. Because of the transition period, only short spans of reliable time series are available, suggesting the adoption of simple time series models as...

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass...

It is investigated whether Euro-area variables can be forecast better based on synthetic time series for the pre-Euro period or by using just data from Germany for the pre-Euro period. Our forecast comparison is based on quarterly data for the period 1970Q1 - 2003Q4 for ten macroeconomic...