KAJIAN PEMODELAN DERET WAKTU NONLINIER THRESHOLD AUTOREGRESSIVE (TAR)

Nonlinear time series are time series that are not stable due to a sudden jump. Nonlinear time series often found in financial data. Threshold Autoregressive (TAR) modeling is a time series modeling with a segmented  autoregressive (AR)’s  model such that among different regimes may have different AR model. This research studied how to obtain the Ordinary Least Square (OLS) estimator for TAR model and examine signification the OLS’s estimator by using t test. This research also studied the other stages of TAR modeling, which are nonlinearity test using Tsay test, TAR model identification by using arranged AR approach and Akaike’s Information Criterion (AIC), and diagnostic test by examining the white noise properties and normality test on the residuals. As an illustration, the TAR modeling was applied on weekly data of  rupiah exchange rate against US dollar for period October 4th 2004 to November 7th 2011. The result show that the best TAR model for the data is TAR  with threshold value