Macroeconometric and fi?nancial researchers often use secondary or constructed binary random variables that differ in terms of their sta- tistical properties from the primary random variables used in micro- econometric studies. One important difference between primary and secondary binary...

To match the NBER business cycle features it is necessary to employ Gen- eralised dynamic categorical (GDC) models that impose certain phase re- strictions and permit multiple indexes. Theory suggests additional shape re- strictions in the form of monotonicity and boundedness of certain...

Macroeconometric and fi?nancial researchers often use secondary or constructed binary random variables that differ in terms of their sta- tistical properties from the primary random variables used in micro- econometric studies. One important difference between primary and secondary binary...

To match the NBER business cycle features it is necessary to employ Gen- eralised dynamic categorical (GDC) models that impose certain phase re- strictions and permit multiple indexes. Theory suggests additional shape re- strictions in the form of monotonicity and boundedness of certain...

We examine the effectiveness of recursive-weight and equal-weight combination strategies for forecasting using many time-varying models of the relationship be- tween inflation and the output gap. The forecast densities for inflation reflect the uncertainty across models using many statistical...

We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents...

We propose a methodology for producing forecast densities for economic aggregates based on disaggregate evidence. Our ensemble predictive methodology utilizes a linear mixture of experts framework to combine the forecast densities from potentially many component models. Each component represents...

We examine the effectiveness of recursive-weight and equal-weight combination strategies for forecasting using many time-varying models of the relationship be- tween inflation and the output gap. The forecast densities for inflation reflect the uncertainty across models using many statistical...

We propose a methodology to gauge the uncertainty in output gap nowcasts across a large number of commonly-deployed vector autoregressions in US inflation and various measures of the output gap. Our approach constructs ensemble nowcast densities using a linear opinion pool. This yields...

We propose a methodology to gauge the uncertainty in output gap nowcasts across a large number of commonly-deployed vector autoregressions in US inflation and various measures of the output gap. Our approach constructs ensemble nowcast densities using a linear opinion pool. This yields...