Handbook of Economic Forecasting part 97. 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 | 934 M. Marcellino which is again estimated over a training sample using the recession probabilities from the single models to be pooled rt t_1 - and the actual values of the recession indicator rt. The pooling method described above was studied from a theoretical point of view by Li and Dorfman 1996 in a Bayesian context. A more standard Bayesian approach to forecast combination is the use of the posterior odds of each model as weights see . Zellner and Min 1993 . When all models have equal prior odds this is equivalent to the use of the likelihood function value of each model as its weight in the pooled forecast. 9. Evaluation of leading indicators In this section we deal with the evaluation of the forecasting performance of the leading indicators when used either in combination with simple rules to predict turning points or as regressors in one of the models described in the previous sections to forecast either the growth rate of the target variable or its turning points. In the first subsection we consider methodological aspects while in the second subsection we discuss empirical examples. . Methodology A first assessment of the goodness of leading indicators can be based on standard insample specification and mis-specification tests of the models that relate the indicators to the target variable. The linear model in 21 provides the simplest framework to illustrate the issues. A first concern is whether it is a proper statistical model of the relationships among the coincident and the leading variables. This requires the estimated residuals to mimic the assumed . characteristics of the errors the parameters to be stable over time and the absence of nonlinearity. Provided these hypotheses are not rejected the model can be used to assess additional properties such as Granger causality of the leading for the coincident indicators or to evaluate the overall goodness of fit of the equations for the coincident variables or for the composite coincident index
