1/13/2024 0 Comments Jstock indicator download![]() Based on basic statistical cognition and product partition model (PPM), the historical change points are defined, identified, and analyzed. This paper introduces the Poisson distribution, power-law distribution, and logarithmic-normal distribution as the prior distributions to construct Bayes statistical probability inference model for the simulation of the monthly crude oil price change point trends. We conclude that WTI crude oil price would take a shock upstream tendency in the short-term but the rising scope would not be large. shale oil production are used as two scenario variables, and the WTI price is forecasted fluctuating around 50 dollar/barrel based on three scenario prediction. Besides, we make scenario prediction on WTI crude oil price to examine the implementation effect of OPEC cut-off agreement at the end of 2016. ![]() Compared with some other competing models and benchmark model of ARIMA, the newly proposed method shows superior forecasting ability in four statistical tests. Finally, the time-varying parameter structure time series model (TVP-STSM) is used to decompose the oil sequence, capture the time-variation of coefficients in “volatile upward” regime, and forecast the crude oil price. Then, we use Bayesian model averaging (BMA) to filtrate main determinants at each regime. Next, we apply a time-varying transition probability Markov regime switching (TVTP-MRS) model to identify the regime-switching characteristic. First, product partition model-K-means (PPM-KM) model is used to detect change points in the oil price sequence. In view of the importance and complexity of international crude oil price, this paper proposes a novel combination forecast approach that captures a variety of fluctuation features in crude oil data series, including change points, regime-switching, time-varying determinants, trend decomposition of high-frequency sequences, and the possible nonlinearity of model setting.
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