Most macroeconomic data are uncertain - they are estimates rather than perfect measures of underlying economic variables. One symptom of that uncertainty is the propensity of statistical agencies to revise their estimates in the light of new information or methodological advances. This paper...

Real-time macroeconomic data are typically incomplete for today and the immediate past (‘ragged edge’) and subject to revision. To enable more timely forecasts the recent missing data have to be imputed. The paper presents a state-space model that can deal with publication lags and data...

Over time, economic statistics are refined. This means that newer data is typically less well measured than old data. Time variation in measurement error like this influences how forecasts should be made. We show how modelling the behaviour of the statistics agency generates both an estimate of...

In this paper we explore the consequences for forecasting of the following two facts: first, that over time statistical agencies revise and improve published data, so that observations on more recent events are those that are least well measured. Second, that economies are such that observations...

We use a structural dynamic stochastic general equilibrium model to investigate how initial data releases of key macroeconomic aggregates are related to final revised versions and how identified aggregate shocks influence data revisions. The analysis sheds light on how well preliminary data...

We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extending it in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying...

We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extending it in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying...

Data, data, data . . . Economists know it well, especially when it comes to monitoring macroeconomic conditions - the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before...

In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

This paper investigates the precision of multivariate models of the output gap and considers their implications for the formulation of macroeconomic policy. Multivariate models identify the gap by including information from structural economic relationships, such as Okun's Law, the Phillips...