INFERENCE IN TAR MODELS

A distribution theory is developed for least-squares estimates of the threshold in Threshold Autoregressive (TAR) models. We find that if we let the threshold effect (the difference in slopes between the two re gimes) become small as the sample sire increases, then the asymptotic distribution of the threshold estimator is free of nuisance parameters (up to scale). Similarly, the likelihood ratio statistic for testing hypotheses concerning the unknown threshold is asymptotically free of nuisance parameters. These asymptotic distributions are nonstandard, b ut are available in closed form, so critical values are readily availa ble. To illustrate this theory, toe report an application to the U.S. unemployment rate. We find statistically significant threshold effects .