Handbook of Economic Forecasting part 37. Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing | 334 A. Harvey . State space and beyond The state space form SSF allows a general treatment of virtually any linear time series models through the general algorithms of the Kalman filter and the associated smoother. Furthermore it permits the likelihood function to be computed. Section 6 reviews the SSF and presents some results that may not be well known but are relevant for forecasting. In particular it gives the ARIMA and autoregressive AR representations of models in SSF. For multivariate series this leads to a method of computing the vector error correction model VECM representation of an unobserved component model with common trends. VECMs were developed by Johansen 1995 and are described in the chapter by Lutkepohl. The most striking benefits of the structural approach to time series modelling only become apparent when we start to consider more complex problems. The direct interpretation of the components allows parsimonious multivariate models to be set up and considerable insight can be obtained into the value of for example using auxiliary series to improve the efficiency of forecasting a target series. Furthermore the SSF offers enormous flexibility with regard to dealing with data irregularities such as missing observations and observations at mixed frequencies. The study by Harvey and Chung 2000 on the measurement of British unemployment provides a nice illustration of how STMs are able to deal with forecasting and nowcasting when the series are subject to data irregularities. The challenge is how to obtain timely estimates of the underlying change in unemployment. Estimates of the numbers of unemployed according to the ILO definition have been published on a quarterly basis since the spring of 1992. From 1984 to 1991 estimates were published for the spring quarter only. The estimates are obtained from the Labour Force Survey LFS which consists of a rotating sample of approximately 60 000 households. Another measure of unemployment based on .
