tailieunhanh - Handbook of Economic Forecasting part 93

Handbook of Economic Forecasting part 93. 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 | 894 M. Marcellino 5. Construction of model based composite coincident indexes Within the model based approaches for the construction of a composite coincident index CCI two main methodologies have emerged dynamic factor models and Markov switching models. In both cases there is a single unobservable force underlying the current status of the economy but in the former approach this is a continuous variable while in the latter it is a discrete variable that evolves according to a Markov chain. We now review these two methodologies highlighting their pros and cons. . Factor based CCI Dynamic factor models were developed by Geweke 1977 and Sargent and Sims 1977 but their use became well known to most business cycle analysts after the publication of Stock and Watson s 1989 SW attempt to provide a formal probabilistic basis for Burns and Mitchell s coincident and leading indicators with subsequent refinements of the methodology in Stock and Watson 1991 1992 . The rationale of the approach is that a set of variables is driven by a limited number of common forces and by idiosyncratic components that are either uncorrelated across the variables under analysis or in any case common to only a limited subset of them. The particular model that SW adopted is the following kxt 3 y L Ct ut 4 D L ut et 5 L Ct 8 vt 6 where xt includes the logs of the four coincident variables used by the CB for their CCICB the only difference being the use of hours of work instead of employment since the former provides a more direct measure of fluctuations in labor input. Ct is the single factor driving all variables while ut is the idiosyncratic component A indicates the first difference operator L is the lag operator and y L D L L are respectively vector matrix and scalar lag polynomials. SW used first differenced variables since unit root tests indicated that the coincident indexes were integrated but not cointegrated. The model is identified by assuming that D L is diagonal and et and vt are