This paper shows that the LM statistic for testing first order serial correlation in regression models can be computed using the Kalman Filter. It is shown tha.t when there are missing observations, the LM statistic for this tesi is equivalent to the tesi statistic derived by Robinson (1985)...

We propose and implement a framework for characterizing and monitoring the global business cycle. Our framework utilizes high-frequency data, allows us to account for a potentially large amount of missing observations, and is designed to facilitate the updating of global activity estimates as...

This paper analyzes the effects of IMF member countries participation in the IMF’s Data Standards Initiatives (DSI) on the statistical quality of WEO forecasts. Results show that WEO forecasts for SDDS subscribers are in general better than for GDDS participants and those member countries...

We consider the problem of smoothing data on two-dimensional grids with holes or gaps. Such grids are often referred to as difficult regions. Since the data is not observed on these locations, the gap is not part of the domain. We cannot apply standard smoothing methods since they smooth over...

We provide a simulation smoother to a exible state-space model with lagged states and lagged dependent variables. Qian (2014) has introduced this state-space model and proposes a fast Kalman filter with time-varying state dimension in the presence of missing observations in the data. In this...

We propose a new approach to sample unobserved states conditional on available data in (conditionally) linear unobserved component models when some of the observations are missing. The approach is based on the precision matrix of the states and model variables, which is sparse and banded in many...