<![CDATA[Data in the fields of finance, business, economics, agriculture, the environment and weather are commonly in the form of time series data. To analyze time series data that involves more than one variable (multivariate), vector autoregressive (VAR) models, vector autoregressive moving average (VARMA) models are generally used. If the variables discussed have cointegration, then the VAR model is modified into a vector error correction model (VECM). The relationship between short-term dynamics and deviation in the VECM model is assumed to be linear. If there is a nonlinear relationship between short-term dynamics and deviation, then a threshold vector error correction model (TVECM) can be used. The variables used in this research consist of oil production and Indonesian oil and gas production from January 2019 to March 2021. The research results show that the best model for data on oil production and oil and gas production is the TVECM 2 Regime model. Based on the TVECM 2 Regime model, further analysis, namely Granger causality and Impulse Response Function are discussed.]]>
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