Chile, copper and resource revenue: A holistic approach to assessing commodity dependence

Ebert, Laura; Menza, Tania La

doi:10.1016/j.resourpol.2014.10.007

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…In this sense, and from a political point of view, it is necessary to allocate funds that originate in a positive cycle towards compensating for the greater losses to come under a scenario of lower prices. Indeed, a policy designed for more balanced growth would nurture the relationship between household consumption and private investment (Ebert & La Menza, 2015). Rising household income and consumption would generate product innovation; private investment would lead to rising labor productivity in non-mining tradable and nontradable goods sectors, which would, in turn, raise wages and further fuel domestic demand.…”

Section: Discussionmentioning

confidence: 99%

Sub-national economic effects of the resources sector in Chile

Valdés

2021

Journal of Applied Economics

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The significant contributions of resource sectors to national economies are well known; however, information on sub-national impacts is much scarcer. Using Chile as a case study, this paper investigates how disturbances in the price of copper impact the economies where this mineral is produced. The proposed method combines estimates of long-term copper prices with a general equilibrium model to simulate the differential effects of copper price variations. The results suggest that price variations affect the design and implementation of public and private policies, at economic and business levels.

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Section: Discussionmentioning

confidence: 99%

Sub-national economic effects of the resources sector in Chile

Valdés

2021

Journal of Applied Economics

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“…To illustrate this, we can consider the particular case of Chile. As the world's leading copper producer and exporter, Chile produced an estimated 5.6 million metric tons of copper in 2019 [3]. The Chilean government made copper the main point of reference for the country's structural budget rule introduced in 2000, trying to reduce the exposure of fluctuations in Chile's gross domestic product (GDP) to the oscillation of the price of copper [4].…”

Section: Introductionmentioning

confidence: 99%

Copper Price Prediction Using Support Vector Regression Technique

et al. 2020

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Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (C, ε, γ) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the 2.2% for prediction periods of 5 and 10 days.

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“…To illustrate this, we can consider the particular case of Chile. As the world's leading copper producer and exporter, Chile produced an estimated 5.6 million metric tons of copper in 2019 [3]. The Chilean government made copper the main point of reference for the country's structural budget rule introduced in 2000, trying to reduce the exposure of fluctuations in Chile's GDP to the oscillation of the price of copper [4].…”

Section: Introductionmentioning

confidence: 99%

Copper Price Prediction Using Support Vector Regression Technique

Astudillo¹,

Carrasco²,

Fernández‐Campusano³

et al. 2020

Preprint

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Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is non-linear, non-stationary, and which have periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of trend-cycle. In this paper, we study a copper price prediction method using Support Vector Regression. This work explores the potential of the Support Vector Regression with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchanges. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data-sets, our results obtained indicate that the parameters (C, ε, γ) of the model Support Vector Regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, being the RMSE equal or less than the 2.2% for prediction periods of 5 and 10 days.

show abstract

Chile, copper and resource revenue: A holistic approach to assessing commodity dependence

Cited by 18 publications

References 13 publications

Sub-national economic effects of the resources sector in Chile

Sub-national economic effects of the resources sector in Chile

Copper Price Prediction Using Support Vector Regression Technique

Copper Price Prediction Using Support Vector Regression Technique

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