Multivariate singular spectrum analysis for forecasting revisions to real-time data

摘要

School of Economics, University of Reading, Reading RG6 6AA, UK;School of Mathematics,Cardiff University, Cardiff, UK,Statistical Research and Training Center, Tehran, Iran;Cardiff Business School, Cardiff University, Cardiff, UK;School of Mathematics,Cardiff University, Cardiff, UK;%Real-time data on national accounts statistics typically undergo an extensive revision process, leading to multiple vintages on the same generic variable. The time between the publication of the initial and final data is a lengthy one and raises the question of how to model and forecast the final vintage of data - an issue that dates from seminal articles by Mankiw et al. [51], Mankiw and Shapiro [52] and Nordhaus [57]. To solve this problem, we develop the non-parametric method of multivariate singular spectrum analysis (MSSA) for multi-vintage data. MSSA is much more flexible than the standard methods of modelling that involve at least one of the restrictive assumptions of linearity, normality and stationarity. The benefits are illustrated with data on the UK index of industrial production: neither the preliminary vintages nor the competing models are as accurate as the forecasts using MSSA.