TAILIEUCHUNG - Handbook of Economic Forecasting part 94

Handbook of Economic Forecasting part 94. 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 | 904 M. Marcellino of x and y in the x equations should also be equal to zero. Notice that these are all testable assumptions as long as m n is small enough with respect to the sample size to leave sufficient degrees of freedom for the VAR parameter estimation. For example in the case of the Conference Board m n 14 and monthly data are available for about 45 years for a total of more than 500 observations. Auerbach 1982 found that a regression based CLI in sample performed better than the equal weighted CLIcb for industrial production and the unemployment rate but not out of sample. If the restrictions in 33 are not satisfied but it is desired to use in any case CLIew or more generally a given CLI to forecast the CCI it can be possible to improve upon its performance by constructing a VAR for the two composite indexes CCI and CLIEW wxt inyt say CCI CLIew fccI cLIew L g L f L CCI -i vccit h L CLIew -1 vcliew 34 and construct the new composite index as CLI4 fCCi e L CCI f L CLIew . 35 This is for example the methodology adopted by Koch and Rasche 1988 who analyzed a VAR for IP as a coincident indicator and the equal weighted DOC leading index. Since CLI4 has a dynamic structure and also exploits past information in the CCI it can be expected to improve upon CLIEW. Moreover since the VAR in 34 is much more parsimonious than both 21 and 29 CLI4 could perform in practice even better than the other composite indexes in particular in small samples. A point that has not gained attention in the literature but can be of importance is the specification of the equations for the single or composite leading indicators. Actually in all the models we have considered so far the leading variables depend on lags of the coincident ones which can be an unreliable assumption from an economic point of view. For example the interest rate spread depends on future expected short terminterest rates and the stock market index on future expected profits and dividends and these expectations are

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