Exchange Rate Forecasting: Results from a Threshold Autoregressive Model


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Abstract

Structural models of exchange rate determination rarely forecast the exchange rate more accurately than a naive random walk model. Recent innovations in exchange rate modeling indicate that changes in the exchange rate may follow a self-exciting threshold autoregressive model (SETAR). We estimate a SETAR model for various monthly US dollar exchange rates and generate forecasts for the estimated models. We find: (1) nonlinearities in the data not uncovered by the standard nonlinearity tests and (2) that the SETAR model produces better forecasts than the naive random walk model.

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