Recently, interest in the methodology of constructing coincident economic indicators has been revived by the work of Stock and Watson (1989b). They adopt the framework of the state space form and Kalman filter in which to construct an optimal estimate of an unobserved component. This is interpreted as corresponding to underlying economic activity derived from a set of observed indicator variables. In this paper we apply the Stock and Watson approach to the UK where the observed indicator variables are those that make up the Central Statistical Office (CSO) coincident indicator. The time series properties of the indicator variables are examined and three of the five variables are first difference stationary and are cointegrated, the remaining two are stationary in levels. We then construct two alternative measures of economic activity, each of which deals with the different orders of stationarity of the variables. The first uses the levels of the observed component variables that allows for the cointegrating relationship. The second imposes stationarity on the I(1) variables before the estimation by taking first differences. The levels index is viewed as the preferred specification as it allows for the long-run relationships between the variables and has a superior statistical fit.
