Forecasting with measurement errors in dynamic models - Richard T. Harrison; George Kapetanios (Queen Mary University of London, Department of Economics, 2004)
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 on the most recent events contain the the largest signal about the future. We discuss a variety of forecasting problems in this environment, and present an application using a univariate model of the quarterly growth of UK private consumption expenditure.
Keywords: C32; C53; 330; Forecasting; Data revisions; Dynamic models; Prognoseverfahren; Statistische Erhebung; Statistischer Fehler