## Nonlinear models

Outside of the normal distribution, conditional expectations are typically nonlinear, and in general one would imagine that these infeasible optimal forecasts would be nonlinear functions of past data. The main difficulty that arises with nonlinear forecasts is choosing a feasible forecasting method that performs well with the fairly short historical time series available for macroeconomic forecasting. With many parameters, approximation error in (27.2) is reduced, but estimation error can be increased. Many nonlinear forecasting methods also pose technical problems, such as having objective functions with many local minima, having parameters that are not globally identified, and difficulties with generating internally consistent й-step ahead forecasts from one-step ahead models.

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