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

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

Households can rely on private savings or on public unemployment insurance to hedge against the risk of becoming unemployed. These hedging mechanisms are used differently across countries. In this paper, we use a life cycle model to study the effects of unemployment on the portfolio choice of...

This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model specification 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 coefficients,...

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...

The decline in output volatility in Germany is analysed. A lower level of variance in an autoregressive model of output growth can be either due to a change in the structure of the economy (a change in the propagation mechanism) or a reduced error term variance (reduced impulses). In Germany the...

In this paper, a mean adjustment scheme for unit root tests in the presence of deterministic seasonality is discussed. The Cauchy estimator for autoregressive processes provides some advantages in the application to unit root tests. In particular, it allows for asymptotically standard normal...