TAILIEUCHUNG - Handbook of Economic Forecasting part 39

Handbook of Economic Forecasting part 39. 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 | 354 A. Harvey 1981 and then increased quite substantially in the 1990s. They note that .previous models break down due to their inability to capture changes in the trend cyclical and seasonal components of teenage employment . Global warming. Visser and Molenaar 1995 use stationary explanatory variables to reduce the short term variability when modelling the trend in northern hemisphere temperatures. . Interventions Intervention variables may be introduced into a model. Thus in a simple stochastic trend plus error model yt i t wt 8t t 1 . T. 42 If an unusual event is to be treated as an outlier it may be captured by a pulse dummy variable that is 0 1 Wt for t t for t t. 43 A structural break in the level at time t may be modelled by a level shift dummy Wt 0 for t t 1 for t t or by a pulse in the level equation that is t i- t-1 wt Pt-1 nt where wt is given by 43 . Similarly a change in the slope can be modelled in 42 by defining 0 t Wt for t t for t t t or by putting a pulse in the equation for the slope. A piecewise linear trend emerges as a special case when there are no disturbances in the level and slope equations. Modelling structural breaks by dummy variables is appropriate when they are associated with a change in policy or a specific event. The interpretation of structural breaks as large stochastic shocks to the level or slope will prove to be a useful way of constructing a robust model when their timing is unknown see Section . Ch. 7 Forecasting with Unobserved Components Time Series Models 355 . Time-varying parameters A time-varying parameter model may be set up by letting the coefficients in 41 follow random walks that is 8t 8t-i vt vt - NID 0 Q . The effect of Q being . is to discount the past observations in estimating the latest value of the regression coefficient. Models in which the parameters evolve as stationary autoregressive processes have also been considered see for example Rosenberg 1973 . Chow 1984 and Nicholls and Pagan 1985 .

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