Threshold Autoregressive (TAR) Models Movements between regimes governed by an observed variable. TAR model: Where s t-k is the state determining variable.

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Presentation transcript:

Threshold Autoregressive (TAR) Models Movements between regimes governed by an observed variable. TAR model: Where s t-k is the state determining variable. The integer k determines with how many lags does the state- determining variable influences the regime in time t. When s t-k = y t-k we have a self-exciting TAR (SETAR) model: There are many possible variations of this simple model.

Threshold Autoregressive (TAR) Models Example: when s t-k = y t-k we have a self-exciting TAR (SETAR) model: Consider k = 1. Parameters to be estimated: –  1,  2,  1,  2, –r Estimation method: least squares with r estimated by a grid search. There are many possible variations of this simple model: Switching in only some of the parameters More than 2 regimes Different threshold variables Alternative dynamic specifications Can use AIC or other information criteria to select models

EXAMPLE: Threshold error correction (cointegration) model

EVIEWS program: series y = d(r120) series x = d(r3) series spread = r120 - r3 scalar th = 3.22 series _d = ( spread(-1) < th ) equation tar.ls y c y(-1) y(-2) x(-1) x(-2) _d*spread(-1) (1-_d)*spread(-1)

EXAMPLE: Threshold error correction (cointegration) model