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Research Article Open access CC BY 4.0

Self-exciting Threshold Autoregressive Model with Application to Crude Oil Production in Nigeria

J. N. Onyeka-Ubaka, O. A. Ebiringa

Asian Journal of Probability and Statistics · pp. 1–18 · Published 3 Apr 2023

10.9734/ajpas/2023/v22i1473

Abstract

In the last two years the price of oil and its fluctuations have reached levels never recorded in the history of international oil markets. The determinants of past, current, and future levels of the price of oil and its fluctuations have been the subject of analysis by academics and energy experts, given the relevance of crude oil in the worldwide economy. The paper, therefore, model and forecast crude oil production (2002-2022) sourced from Central Bank of Nigeria website. The successive least squares estimation and model diagnostics are applied. The results affirmed that the self-exciting threshold autoregressive (SETAR(2,2,1)) model outperformed autoregressive integrated moving average (ARIMA(3,1,3)) model for crude oil production fluctuations based on our diagnostics (AIC, SC, SSR and log-likelihood ratio). The results obtained will greatly assist the government and policy makers in planning for economic development since the long-run success of any country is closely related to how well management is able to foresee the future and develop appropriate strategies.

Backward shift crude oil SETAR threshold autoregressive volatility clustering

References (18)

  1. 1 Distribution of the Estimators for Autoregressive Time Series with a Unit Root [DOI]
  2. 2 Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation [DOI]
  3. 3 Distribution of the Estimators for Autoregressive Time Series With a Unit Root [DOI]
  4. 4 Efficient tests for normality, homoscedasticity and serial independence of regression residuals [DOI]
  5. 5 A Capital Asset Pricing Model with Time-Varying Covariances [DOI]
  6. 6 Modelling Nonlinear Economic Relationships [DOI]
  7. 7 On the Behaviour of Commodity Prices [DOI]
  8. 8 Testing and Modeling Threshold Autoregressive Processes [DOI]
  9. 9 Non Linear Time Series. A Dynamical System Approach [DOI]
  10. 10 Modelling Volatility of Cryptocurrencies Using Markov-Switching GARCH Models [DOI]
  11. 11 A Capital-Asset Pricing Model with Time-varying Covariances [DOI]
  12. 12 Some recent developments in non-linear time series modelling, testing, and forecasting [DOI]
  13. 13 Evaluating the forecast densities of linear and non-linear models: applications to output growth and unemployment [DOI]
  14. 14 Percentage Points of Likelihood Ratio Tests for Threshold Autoregression [DOI]
  15. 15 The Volatility of Oil Prices: What Factors?
  16. 16 The Interrelationship between Crude Oil Price Volatility and Money Market Rate Volatility in a Developing, Oil-Producing Economy
  17. 17 Searching for Appropriate Crude Oil Price Benchmarking Method in the Nigerian Budgeting Process
  18. 18 Time Series Analysis: Forecasting and Control [DOI]

Cited by 1

Self-Exciting Threshold Autoregressive (SETAR) Modelling of the NSE 20 Share Index Using the Bayesian Approach

Jacinta Muindi, George Muhua, Ronald Wanyonyi · American Journal of Theoretical and Applied Statistics · 2024

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