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Handbook of Economic Forecasting part 45

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Handbook of Economic Forecasting part 45. 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 | 414 T Terasvirta 7. Empirical forecast comparisons 445 7.1. Relevant issues 445 7.2. Comparing linear and nonlinear models 447 7.3. Large forecast comparisons 448 7.3.1. Forecasting with a separate model for each forecast horizon 448 7.3.2. Forecasting with the same model for each forecast horizon 450 8. Final remarks 451 Acknowledgements 452 References 453 Abstract The topic of this chapter is forecasting with nonlinear models. First a number of well-known nonlinear models are introduced and their properties discussed. These include the smooth transition regression model the switching regression model whose univariate counterpart is called threshold autoregressive model the Markov-switching or hidden Markov regression model the artificial neural network model and a couple of other models. Many of these nonlinear models nest a linear model. For this reason it is advisable to test linearity before estimating the nonlinear model one thinks will fit the data. A number of linearity tests are discussed. These form a part of model specification the remaining steps of nonlinear model building are parameter estimation and evaluation that are also briefly considered. There are two possibilities of generating forecasts from nonlinear models. Sometimes it is possible to use analytical formulas as in linear models. In many other cases however forecasts more than one periods ahead have to be generated numerically. Methods for doing that are presented and compared. The accuracy of point forecasts can be compared using various criteria and statistical tests. Some of these tests have the property that they are not applicable when one of the two models under comparison nests the other one. Tests that have been developed in order to work in this situation are described. The chapter also contains a simulation study showing how in some situations forecasts from a correctly specified nonlinear model may be inferior to ones from a certain linear model. There exist relatively large .