This paper employs concepts from information theory to choosing the dimension of a data set. We propose a relative information measure connected to Kullback-Leibler numbers. By ordering the series of the data set according to the measure, we are able to obtain a subset of a data set that is most...

This paper employs concepts from information to choosing the dimension of a data set. We calculate relative measures of information in the data in terms of eigenvalues and derive criteria to determine the ‘optimal’ size of the data set, in particular whether an extra variable adds...

This paper focuses on testing non-stationary real-time data for forecastability, i.e., whether data revisions reduce noise or are news, by putting data releases in vector-error correction forms. To deal with historical revisions which affect the whole vintage of time series due to redefinitions,...

Chapter 1. Introduction -- Chapter 2. CAMPLET: Seasonal adjustment without revisions -- Chapter 3. Seasonal adjustment of economic tendency survey data -- Chapter 4. Residual Seasonality: A Comparison of X13 and CAMPLET -- Chapter 5. COVID-19 and Seasonal Adjustment -- Chapter 6. Seasonal...

This paper places the data revision model of Jacobs and van Norden (2011) within a class of trend-cycle decompositions relating directly to the Beveridge-Nelson decomposition. In both these approaches identifying restrictions on the covariance matrix under simple and realistic conditions may...

A well-documented property of the Beveridge-Nelson trend-cycle decomposition is the perfect negative correlation between trend and cycle innovations. We show how this may be consistent with a structural model where trend shocks enter the cycle, or cyclic shocks enter the trend and that...