Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

Lee, Chul-Yong; Huh, Sung-Yoon

doi:10.3390/su9020190

Cited by 25 publications

(16 citation statements)

References 66 publications

(80 reference statements)

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“…Figure 11 also depicts the potential scenario based on the particle filtering update based deterioration model with ECSs. The particle filtering algorithm (Equations (9) to (14)) is used to calculate the revised deterioration model ( . = 0.198, .…”

Section: Case 1: Updated Mean State (µ Pfmentioning

confidence: 99%

“…These days, the bridge deterioration model based on regression analysis is conducted to derive an efficient maintenance method for bridge management system in advanced countries, such as the USA, Japan, and Australia [1][2][3]. Domestic and foreign research trends related to bridge maintenance denote that they have focused on estimating the bridge performance changes with high accuracy through monitoring technologies based on unmanned inspection devices (sensors, drones, or robots) and by establishing an optimal maintenance decision making process system [4][5][6][7][8][9][10][11][12][13][14][15]. In addition, Bayesian deterioration model updating for maintenance cost estimation in steel box girder bridges has been proposed [16].…”

Section: Introductionmentioning

confidence: 99%

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Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

et al. 2019

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A deterioration model plays an important role to predict the valid total maintenance cost for sustainable maintenance of bridges. In the current state-of-the-art, the deterioration model has regression parameters as a probabilistic process by an initially determined mean and standard deviation, called an existing model. However, the existing model has difficulty to predict maintenance costs accurately, because it cannot reflect an information based on structural damage at an operational stage. In this research, updating the probabilistic deterioration model is presented for the prediction of pre-stressed concrete I-type (PSCI) girder bridges using a particle filtering technique which is an advanced Bayesian updating method based on big data analysis. The method enables predicting maintenance cost fitted in the current structural status, which includes the recent information by inspection with bridge-monitoring. The method is adapted in the Mokdo Bridge which is currently being used for evaluating the efficiency of maintenance cost by effects on updated probabilistic values with two different scenarios. As the result, it is shown that the proposed method is effective in predicting maintenance costs.

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Section: Case 1: Updated Mean State (µ Pfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

et al. 2019

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“…As a result, it has gained attention even among mathematicians: Cai and Newt [1], Krugman [2] especially with the downturn dynamics of 2015; Lee and Huh [3]. Because to mathematicians, if nation A derives proceeds in a space X when the dynamics are positively increasing and sufficient for instance, the dynamics can be represented.…”

Section: Introductionmentioning

confidence: 99%

Criteria for Maximizing Jobs in Imperfect Production Centers

Sani¹,

Musa²,

Malumfashi³

2017

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“…Furthermore, it is highly likely that oil demand in emerging markets will continue to grow at a remarkable rate [44]. With stagnated oil supply, the rise in oil price in the long run seems to be inevitable [45].…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

The US Shale Gas Revolution and Its Externality on Crude Oil Prices: A Counterfactual Analysis

Liu¹,

Li²

2018

Sustainability

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Abstract:The expansion of shale gas production since the mid-2000s which is commonly referred to as "shale gas revolution" has had large impacts on global energy outlook. The impact is particularly substantial when it comes to the oil market because natural gas and oil are substitutes in consumption and complements and rivals in production. This paper investigates the price externality of shale gas revolution on crude oil. Applying a structural vector autoregressive model (VAR) model, the effect of natural gas production on real oil price is identified in particular, and then based on the identification, counterfactuals of oil price without shale gas revolution are constructed. We find that after the expansion of shale gas production, the real West Texas Intermediate (WTI) oil price is depressed by 10.22 USD/barrel on average from 2007 to 2017, and the magnitude seems to increase with time. In addition, the period before shale gas revolution is used as a "thought experiment" for placebo study. The results support the hypothesis that real WTI oil price can be reasonably reproduced by our models, and the estimated gap for oil price during 2007-2017 can be attributed to shale gas revolution. The methodology and framework can be applied to evaluate the economic impacts of other programs or policies.

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Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

Cited by 25 publications

References 66 publications

Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model

Criteria for Maximizing Jobs in Imperfect Production Centers

The US Shale Gas Revolution and Its Externality on Crude Oil Prices: A Counterfactual Analysis

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