Open AccessKAJIAN PEMODELAN DERET WAKTU NONLINIER THRESHOLD AUTOREGRESSIVE (TAR)
Author(s) -
Puji Noviandari,
Renny Renny
Publication year - 2012
Publication title -
jurnal ilmiah matematika dan pendidikan matematika (jmp)/jurnal ilmiah matematika dan pendidikan matematika
Language(s) - English
Resource type - Journals
eISSN - 2550-0422
pISSN - 2085-1456
DOI - 10.20884/1.jmp.2012.4.1.2947
Subject(s) - akaike information criterion , tar (computing) , autoregressive model , statistics , mathematics , star model , estimator , ordinary least squares , econometrics , series (stratigraphy) , time series , autoregressive integrated moving average , computer science , paleontology , biology , programming language
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 .