Exchange Rate Forecasting: Results from a Threshold Autoregressive Model
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. Copyright Kluwer Academic Publishers 1998
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