TAR—Threshold Autoregression

[TomDoan]

@TAR is a procedure for testing for and estimating a "self-exciting" threshold autoregression. The testing method is from Bruce
Hansen(1996), "Inference When a Nuisance Parameter is Not Identified Under the Null Hypothesis", Econometrica, vol 64, pp 413-430.

The regression includes a constant and some set of lags of the dependent variable. These can either be consecutive lags (use P option), or a list with skips in it (use LAGLIST option). As a "self-exciting" TAR, the threshold variable must be one of the lags---all the included lags are tested as possible thresholds.

For a more general procedure for estimating and testing threshold models, see @THRESHTEST, which allows for any series to be a threshold in any linear regression. It does, however, require that the threshold series of interest be known, while @TAR tests across lags.

tar.src

Procedure file-requires RATS 7.3 or later.

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Detailed description

Example

Code: Select all

*
* Replication of results for a SETAR model on GNP from Bruce
* Hansen(1996), "Inference When a Nuisance Parameter is Not Identified
* Under the Null Hypothesis", Econometrica, vol 64, no. 2, pp 413-430.
* Requires the TAR procedure (from TAR.SRC).
*
open data gnp.asc
calendar(q) 1947
data(format=free,org=columns) 1947:01 1990:03 gnp dontknow
*
set ggrowth = log(gnp/gnp{1})*400.0
*
@tar(laglist=||1,2,5||,nreps=1000) ggrowth

gnp.asc

Data file

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