In this paper we derive a general parametric bootstrapping approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. We consider both classical and unrestricted MIDAS regressions with and without an autoregressive component. First, we compare the...

In this paper we derive a general parametric bootstrapping approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. We consider both classical and unrestricted MIDAS regressions with and without an autoregressive component. First, we compare the...

In this paper we derive a general parametric bootstrapping approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. We consider both classical and unrestricted MIDAS regressions with and without an autoregressive component. First, we compare the...

In this paper we propose a parametric block wild bootstrap approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. First, Monte Carlo simulations show that predictive densities for the various MIDAS models derived from the block wild bootstrap approach...

We propose a new VAR identfication scheme that enables us to disentangle labor supply shocks from wage bargaining shocks. Identification is achieved by imposing robust signrestrictions that are derived from a New Keynesian model with endogenous labor force participation. According to our...

Despite its stability over time, as for any statistical relationship, Okun's law is subject to deviations that can be large at times. In this paper, we provide a mapping between residuals in Okun's regressions and structural shocks identified using a SVAR model by inspecting how unemployment...