Density-Conditional Forecasts in Dynamic Multivariate Models

When generating conditional forecasts in dynamic models it is common to impose the conditions as restrictions on future structural shocks. However, these conditional forecasts often ignore that there may be uncertainty about the future development of the restricted variables. Our paper therefore proposes a generalization such that the conditions can be given as the full distribution of the restricted variables. We demonstrate, in two empirical applications, that ignoring the uncertainty about the conditions implies that the distributions of the unrestricted variables are too narrow

Loose Commitment In Medium-Scale Macroeconomic Models: Theory And Applications

This paper proposes a method and a toolkit for solving optimal policy with imperfect commitment. As opposed to the existing literature, our method can be employed in the medium- and large-scale models typically used in monetary policy. We apply our method to the Smets and Wouters model [American Economic Review 97(3), 586–606 (2007)], for which we show that imperfect commitment has relevant implications for interest rate setting, the sources of business cycle fluctuations, and welfare

Loose Commitment In Medium-Scale Macroeconomic Models: Theory And Applications

This paper proposes a method and a toolkit for solving optimal policy with imperfect commitment. As opposed to the existing literature, our method can be employed in the medium- and large-scale models typically used in monetary policy. We apply our method to the Smets and Wouters model [American Economic Review 97(3), 586–606 (2007)], for which we show that imperfect commitment has relevant implications for interest rate setting, the sources of business cycle fluctuations, and welfare

Sigma Point Filters for Dynamic Nonlinear Regime Switching Models

In this paper we take three well known Sigma Point Filters, namely the Unscented Kalman Filter, the Divided Difference Filter, and the Cubature Kalman Filter, and extend them to allow for a very general class of dynamic nonlinear regime switching models. Using both a Monte Carlo study and real data, we investigate the properties of our proposed filters by using a regime switching DSGE model solved using nonlinear methods. We find that the proposed filters perform well. They are both fast and reasonably accurate, and as a result they will provide practitioners with a convenient alternative to Sequential Monte Carlo methods. We also investigate the concept of observability and its implications in the context of the nonlinear filters developed and propose some heuristics. Finally, we provide in the RISE toolbox, the codes implementing these three novel filters

Reliability and validity of the range of motion scale (ROMS) in patients with abnormal postures

Sustained abnormal postures (i.e., fixed dystonia) are the most frequently reported motor abnormalities in complex regional pain syndrome (CRPS), but these symptoms may also develop after peripheral trauma without CRPS. Currently, there is no valid and reliable measurement instrument available to measure the severity and distribution of these postures. The range of motion scale (ROMS) was therefore developed to assess the severity based on the possible active range of motion of all joints (arms, legs, trunk, and neck), and the present study evaluates its reliability and validity