create a website

Machine Learning and Oil Price Point and Density Forecasting. (2021). Gaglianone, Wagner ; Lin, Yihao ; Issler, Joo Victor ; Teixeira, Osmani ; Cavalcanti, Pedro ; Bonnet, Alexandre.
In: Working Papers Series.
RePEc:bcb:wpaper:544.

Full description at Econpapers || Download paper

Cited: 11

Citations received by this document

Cites: 80

References cited by this document

Cocites: 50

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

  1. Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches. (2024). Nyawa, Serge ; Dey, Prasanta Kumar ; Tchuente, Dieudonne ; Gnekpe, Christian.
    In: Journal of Business Research.
    RePEc:eee:jbrese:v:183:y:2024:i:c:s0148296324003254.

    Full description at Econpapers || Download paper

  2. A multiscale time-series decomposition learning for crude oil price forecasting. (2024). Li, Zhixi ; Jiang, Yuansheng ; Shi, Long ; Tan, Jinghua ; Zhang, Chuanhui.
    In: Energy Economics.
    RePEc:eee:eneeco:v:136:y:2024:i:c:s0140988324004419.

    Full description at Econpapers || Download paper

  3. The impact of oil and global markets on Saudi stock market predictability: A machine learning approach. (2024). Ibrahim, Bassam A ; Abedin, Mohammad Zoynul ; Elamer, Ahmed A ; Abdou, Hussein A.
    In: Energy Economics.
    RePEc:eee:eneeco:v:132:y:2024:i:c:s0140988324001245.

    Full description at Econpapers || Download paper

  4. The role of green energy stock market in forecasting Chinas crude oil market: An application of IIS approach and sparse regression models. (2024). Sharif, Arshian ; Muhammadullah, Sara ; Khan, Faridoon ; Lee, Chien-Chiang.
    In: Energy Economics.
    RePEc:eee:eneeco:v:130:y:2024:i:c:s0140988323007673.

    Full description at Econpapers || Download paper

  5. Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices. (2024). Ikonnikova, Svetlana ; Madadkhani, Shiva.
    In: Energy Economics.
    RePEc:eee:eneeco:v:129:y:2024:i:c:s0140988323007399.

    Full description at Econpapers || Download paper

  6. Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models. (2023). Gaglianone, Wagner ; Araujo, Gustavo Silva.
    In: Latin American Journal of Central Banking (previously Monetaria).
    RePEc:eee:lajcba:v:4:y:2023:i:2:s2666143823000042.

    Full description at Econpapers || Download paper

  7. Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data. (2023). Soderberg, Magnus ; Sjolander, Par ; Mnsson, Kristofer ; Javed, Farrukh ; Duras, Toni.
    In: Energy Economics.
    RePEc:eee:eneeco:v:120:y:2023:i:c:s0140988323001196.

    Full description at Econpapers || Download paper

  8. Predicting Recessions in (almost) Real Time in a Big-data Setting. (2023). Gaglianone, Wagner ; Fialho, Artur Brasil ; Issler, Joo Victor ; Teixeira, Osmani ; Cavalcanti, Pedro ; Bonnet, Alexandre.
    In: Working Papers Series.
    RePEc:bcb:wpaper:587.

    Full description at Econpapers || Download paper

  9. Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach. (2022). Wang, Ping ; Zhu, Bangzhu.
    In: Energy Economics.
    RePEc:eee:eneeco:v:115:y:2022:i:c:s014098832200490x.

    Full description at Econpapers || Download paper

  10. Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm. (2022). Li, Shaoting ; Wang, Xuerui.
    In: Applied Energy.
    RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014519.

    Full description at Econpapers || Download paper

  11. Basic Hyperparameters Tuning Methods for Classification Algorithms. (2021). Antal-Vaida, Claudia.
    In: Informatica Economica.
    RePEc:aes:infoec:v:25:y:2021:i:2:p:64-74.

    Full description at Econpapers || Download paper

References

References cited by this document

  1. Aastveit, K.A., H.C. Bjørnland, and L.A. Thorsud, 2015, What drives Oil Prices? Emerging Versus Developing Economies? Journal of Applied Econometrics 30(1), 10131028.

  2. Adolfson, M., Linde, J., Villani, M., 2005. Forecasting Performance of an Open Economy Dynamic Stochastic General Equilibrium Model. Sveriges Riksbank Working Paper n.190.

  3. Alquist, R., Kilian, L., Vigfusson, R.J., 2013. Forecasting the Price of Oil. Handbook of Economic Forecasting, Vol. 2A, Chapter 8. Elsevier.

  4. Altmann, A., Tolosi, L., Sander, O., Lengauer, T., 2010. Permutation importance: a corrected feature importance measure. Bioinformatics 26, 1340-1347.
    Paper not yet in RePEc: Add citation now
  5. Apostol, T.M., 1967. Calculus, Vol. 1: One-Variable Calculus with an Introduction to Linear Algebra (2nd Ed.), New York: John Wiley & Sons.
    Paper not yet in RePEc: Add citation now
  6. Araujo, G.S., Gaglianone, W.P., 2020. Machine learning methods for in‡ ation forecasting in Brazil: New contenders versus classical models. Mimeo.

  7. Bai, J., Ng, S., 2002. Determining the number of factors in approximate factor models. Econometrica 70, 191-221.

  8. Bai, J., Ng, S., 2008. Forecasting economic time series using targeted predictors. Journal of Econometrics 146, 304-317.

  9. Baker, S.R., Bloom, N., Davis, S.J., 2015. Measuring Economic Policy Uncertainty. NBER Working Paper 21633, National Bureau of Economic Research.

  10. Bańbura, M., Giannone, D., Modugno, M., Reichlin, L., 2013. Now-casting and the real-time data ‡ ow. Working Paper Series n.1564, European Central Bank.

  11. Barsky, R.B., Kilian,L., 2002. Do we really know that oil caused the great stag‡ ation? A monetary alternative. In: Bernanke, B.S., Rogo, K. (Eds.), NBER Macroeconomics Annual 2001. MIT Press, Cambridge, 137-183.

  12. Batchelor, R., 2007. Bias in macroeconomic forecasts. International Journal of Forecasting 23(2), 189-203.

  13. Baumeister, C., Kilian, L., 2012. Real-time forecasts of the real price of oil. Journal of Business and Economic Statistics 30, 326-336.
    Paper not yet in RePEc: Add citation now
  14. Baumeister, C., Kilian, L., 2015. Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach. Journal of Business and Economic Statistics 33(3), 338-351.

  15. Baumeister, C., Kilian, L., 2016. Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us. Journal of Economic Perspectives 30(1), 139-160.

  16. Bjørnland, H.C., Zhulanova, J., 2018. The Shale Oil Boom and the U.S. Economy: Spillovers and Time-Varying Eects. CAMP Working paper 8/2018.

  17. Breiman, L., 2001. Random forests. Machine Learning 45, 5-32.
    Paper not yet in RePEc: Add citation now
  18. Caldara, D., Iacoviello, M., 2018. Measuring Geopolitical Risk. FRB International Finance Discussion Paper n. 1222, Board of Governors - Fed.

  19. Chen, T., Guestrin, C., 2016. XGBoost: A Scalable Tree Boosting System. The 22nd SIGKDD Conference on Knowledge Discovery and Data Mining. Mimeo.
    Paper not yet in RePEc: Add citation now
  20. Cheng, K., Huang, N., Shi, Z., 2019. Survey-Based Forecasting: To Average or Not To Average. Mimeo.
    Paper not yet in RePEc: Add citation now
  21. Clark, T.E., 2011. Real-Time Density Forecasts from Bayesian Vector Autoregressions with Stochastic Volatility. Journal of Business and Economic Statistics 29, 327-341.

  22. Clark, T.E., West, K.D., 2007. Approximately Normal Tests for Equal Predictive Accuracy in Nested Models. Journal of Econometrics 138, 291-311.

  23. Cologni, A., Manera, M., 2008. Oil prices, in‡ ation and interest rates in a structural cointegrated VAR model for the G-7 countries. Energy Economics 38, 856-888.

  24. Cortazar, G., Kovacevic, I., Schartz, E., 2015. Expected commodity returns and pricing models. Energy Economics 49, 60-71.

  25. Cortazar, G., Naranjo, L., 2006. An N-factor Gaussian model of oil futures prices. Journal of Futures Markets: Futures, Options, and Other Derivative Products 26(3), 243-268.

  26. Duarte, A.M., Gaglianone, W.P., Guillén, O.T.C., Issler, J.V., 2019. Commodity Prices and Global Economic Activity: A Derived-Demand Approach. Mimeo.

  27. Elliott, G., Gargano, A., Timmermann, A., 2013. Complete subset regressions. Journal of Econometrics 177(2), 357-373.

  28. Elliott, G., Gargano, A., Timmermann, A., 2015. Complete subset regressions with large-dimensional sets of predictors. Journal of Economic Dynamics and Control 54, 86-110.

  29. Forni, M., Hallim, M., Lippi, M., Reichlin, L., 2000. The generalized dynamic factor model: Identi…cation and estimation. Review of Economics and Statistics 82, 540-554.

  30. Gaglianone, W.P., Issler, J.V., 2019. Microfounded Forecasting. Ensaios Econômicos EPGE n.813, Getulio Vargas Foundation.

  31. Garcia, M.G.P., Medeiros, M.C., Vasconcelos, G.F.R., 2017. Real-time in‡ ation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting 33, 679-693.

  32. Gargano, A., Timmermann, A., 2014. Forecasting commodity price indexes using macroeconomic and …nancial predictors International Journal of Forecasting 30, 825-843.

  33. Gibson, R., Schwartz, E.S., 1990. Stochastic convenience yield and the pricing of oil contingent claims. Journal of Finance 45, 959-976.

  34. Gneiting, T., 2011. Making and Evaluating Point Forecasts. Journal of the American Statistical Association 106(494), 746-762.

  35. Gneiting, T., Raftery, A.E., 2007. Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association 102(477), 359-378.

  36. Gogolin, F., Kearney, F., Lucey, B.M., Peat, M., Vigne, S.A., 2018. Uncovering long term relationships between oil prices and the economy: A time-varying cointegration analysis. Energy Economics 76, 584-593.

  37. Goyal, A.,Welch, I., 2008. A Comprehensive Look at the Empirical Performance of Equity Premium Prediction. Review of Financial Studies 21(4), 1455-1508.

  38. Granger, C.W.J., Ramanathan, R., 1984. Improved methods of combining forecasting. Journal of Forecasting 3, 197-204.
    Paper not yet in RePEc: Add citation now
  39. Hall, A.S., 2018. Machine Learning Approaches to Macroeconomic Forecasting. Federal Reserve Bank of Kansas City Economic Review, 4th quarter of 2018, 63-81.
    Paper not yet in RePEc: Add citation now
  40. Hamilton, J.D., 2003. What is an oil shock? Journal of Econometrics 113, 363-398.

  41. Hamilton, J.D., Herrera, A., 2004. Oil Shocks and Aggregate Macroeconomic Behavior: The Role of Monetary Policy. Journal of Money, Credit, and Banking 36(2), 265-286.

  42. Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition. Springer-Verlag, New York.
    Paper not yet in RePEc: Add citation now
  43. Hoerl, A.E., Kennard, R.W., 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1), 55-67.
    Paper not yet in RePEc: Add citation now
  44. Hong, H., Yogo, M., 2012. What does futures market interest tell us about the macroeconomy and asset prices? Journal of Financial Economics 105, 473-490.

  45. Isserlis, L., 1938. Tramp shipping cargoes and freights. Journal of the Royal Statistical Society 101 (1), 53-134.
    Paper not yet in RePEc: Add citation now
  46. Issler, J.V., Lima, L.R., 2009. A Panel Data Approach to Economic Forecasting: The Bias-corrected Average Forecast. Journal of Econometrics 152 (2), 153-164.

  47. Janitza, S., Celik, E., Boulesteix, A.-L., 2018. A computationally fast variable importance test for random forests for high-dimensional data. Advances in Data Analysis and Classi…cation 12(4), 885-915.

  48. Judge, G.G., Hill, R.C., Gri ths, W.E., Lütkepohl, H., Lee, T.-C., 1988. Introduction to the Theory and Practice of Econometrics. New York, Wiley.
    Paper not yet in RePEc: Add citation now
  49. Jung, J.K., Patnam, M., Ter-Martirosyan, A., 2018. An Algorithmic Crystal Ball: Forecasts-based on Machine Learning. IMF Working Paper WP/18/230.

  50. Kilian, L., Murphy, D., 2014. The Role of Inventories and Speculative Trading in the Global Market for Crude Oil. Journal of Applied Econometrics 29 (3), 454-478.

  51. Kilian, L., Vigfusson, R.J., 2013. Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries. Journal of Business and Economic Statistics 31(1), 78-93.

  52. Kilian, L., Vigfusson, R.J., 2017. The Role of Oil Price Shocks in Causing U.S. Recessions. Journal of Money, Credit, and Banking 49(8), 1747-1776.

  53. Koenker, R., 2005. Quantile Regression. Cambridge University Press.
    Paper not yet in RePEc: Add citation now
  54. Laster, D., Bennett, P., Geoum, I., 1999. Rational bias in macroeconomic forecasts. The Quarterly Journal of Economics 114(1), 293-318.

  55. Lima, L.R., Meng, F., 2017. Out-of-sample return predictability: a quantile combination approach. Journal of Applied Econometrics 32(4), 877-895.

  56. Marcellino, M., Stock, J., Watson, M., 2006. A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. Journal of Econometrics 135, 499-526.

  57. McCracken, M.W., Ng, S., 2015. FRED-MD: A Monthly Database for Macroeconomic Research. Working Paper 2015-012B, Federal Reserve Bank of St. Louis.
    Paper not yet in RePEc: Add citation now
  58. Medeiros, M., Mendes, E., 2016. L1-regularization of high-dimensional time-series models with ‡ exible innovations. Journal of Econometrics 191, 255-271.
    Paper not yet in RePEc: Add citation now
  59. Medeiros, M., Vasconcelos, G.F.R., de Freitas, E.H., 2016. Forecasting Brazilian In‡ ation with High Dimensional Models. Brazilian Review of Econometrics 36(2), 223-254.
    Paper not yet in RePEc: Add citation now
  60. Meinshausen, N., 2006. Quantile Regression Forests. Journal of Machine Learning Research 7, 983-999.
    Paper not yet in RePEc: Add citation now
  61. Miller, J.I., Ni, S., 2011. Long-Term Oil Price Forecasts: A New Perspective on Oil and the Macroeconomy. Macroeconomic Dynamics 15(S3), 396-415.

  62. Miller, J.I., Ratti, R., 2009. Crude Oil and Stock Markets: Stability, Instability, and Bubbles. Energy Economics 31(4), 559-568.

  63. Mohaddes, K., Pesaran, M.H., 2016. Oil Prices and the Global Economy: Is It Dierent This Time Around? IMF Working Paper WP/16/210.

  64. Morales-Arias, L., Moura, G.V., 2013. Adaptive forecasting of exchange rates with panel data. International Journal of Forecasting 29, 493-509.

  65. Morde, V., Setty, V.A., 2019. XGBoost Algorithm: Long May She Reign! Mimeo.
    Paper not yet in RePEc: Add citation now
  66. Neal, B., Mittal, S., Baratin, A., Tantia, V., Scicluna, M., Lacoste-Julien, S., Mitliagkas, I., 2018. A Modern Take on the Bias-Variance Tradeo in Neural Networks. Mimeo, available at https://arxiv.org/abs/1810.08591
    Paper not yet in RePEc: Add citation now
  67. Nembrini, S., Koenig, I.R., Wright, M.N., 2018. The revival of the Gini Importance? Bioinformatics 34(21), 3711-3718.
    Paper not yet in RePEc: Add citation now
  68. Phillips, P.C.B., Moon, H.R., 1999. Linear regression limit theory for nonstationary panel data. Econometrica 67 (5), 1057-1111.

  69. Rapach, D. E., Strauss, J. K., & Zhou, G., 2010. Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy. The Review of Financial Studies 23(2), 821-862.

  70. Ravazzolo F., Rothman, P., 2012. Oil and U.S. GDP: A Real-Time Out-of-Sample Examination. Journal of Money, Credit and Banking 45(2-3), 449-463.
    Paper not yet in RePEc: Add citation now
  71. Schwartz, E., Smith, J.E., 2000. Short-Term Variations and Long-Term Dynamics in Commodity Prices. Management Science 46 (7), 893-911.

  72. Stock, J., Watson, M., 2002. Forecasting Using Principal Components from a Large Number of Predictors. Journal of the American Statistical Association 97(460), 11671179.

  73. Tibshirani, R., 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society 58(1), 267-288.
    Paper not yet in RePEc: Add citation now
  74. U.S. Energy Information Administration, 2020. What drives crude oil prices? Mimeo.
    Paper not yet in RePEc: Add citation now
  75. Varian, H.R., 2014. Big data: New tricks for econometrics. Journal of Economic Perspectives 28(2), 3-28.

  76. Yu, L., Zhao, Y., Tang, L., Yang, Z., 2019. Online big data-driven oil consumption forecasting with Google trends. International Journal of Forecasting 35, 213-223.

  77. Zagaglia, P., 2010. Macroeconomic factors and oil futures prices: a data-rich model. Energy Economics 32, 409-417.

  78. Zou, H., 2006. The Adaptive Lasso and Its Oracle Properties. Journal of the American Statistical Association 101 (476), 1418-1429.

  79. Zou, H., Hastie, T., 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society 67(2), 301-320.

  80. Zou, H., Hastie, T., Tibshirani, R., 2007. On the degrees of freedom of the lasso. The Annals of Statistics 35, 2173-2192. – Technical Appendix – Machine Learning and Oil Price Point and Density Forecasting Contents:
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. The Econometrics of Oil Market VAR Models. (2020). Kilian, Lutz ; Zhou, Xiaoqing.
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:14460.

    Full description at Econpapers || Download paper

  2. The Econometrics of Oil Market VAR Models. (2020). Kilian, Lutz ; Zhou, Xiaoqing.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8153.

    Full description at Econpapers || Download paper

  3. The Effectiveness of Chinas Monetary Policy: Based on the Mixed-Frequency Data. (2020). Pan, Shengjie ; Zhang, Hongyan ; Song, Yinqiu ; Wang, Deqing.
    In: Asian Economic and Financial Review.
    RePEc:asi:aeafrj:2020:p:325-339.

    Full description at Econpapers || Download paper

  4. Forecasting energy commodity prices: a large global dataset sparse approach. (2019). Vespignani, Joaquin ; Ravazzolo, Francesco ; Ferrari, Davide.
    In: Working Papers.
    RePEc:tas:wpaper:32152.

    Full description at Econpapers || Download paper

  5. The Heterogeneous Interconnections between Supply or Demand Side and Oil Risks. (2019). Du, Ziqing ; Li, Zhenghui ; Liao, Gaoke ; Liu, Yue.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:11:p:2226-:d:238956.

    Full description at Econpapers || Download paper

  6. Uncertainty-Dependent and Sign-Dependent Effects of Oil Market Shocks. (2019). Tran, Trung Duc ; Tatsuyoshi, Okimoto ; Nguyen, Bao H.
    In: Discussion papers.
    RePEc:eti:dpaper:19042.

    Full description at Econpapers || Download paper

  7. Oil price elasticities and oil price fluctuations. (2019). Iacoviello, Matteo ; Cavallo, Michele ; Caldara, Dario .
    In: Journal of Monetary Economics.
    RePEc:eee:moneco:v:103:y:2019:i:c:p:1-20.

    Full description at Econpapers || Download paper

  8. Do heterogeneous countries respond differently to oil price shocks?. (2019). Hernandez-Vega, Marco ; Hernandez-Del, Gerardo ; Guerrero-Escobar, Santiago .
    In: Journal of Commodity Markets.
    RePEc:eee:jocoma:v:16:y:2019:i:c:s2405851317301952.

    Full description at Econpapers || Download paper

  9. Oil shocks and production network structure: Evidence from the OECD. (2019). Caraiani, Petre.
    In: Energy Economics.
    RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319303548.

    Full description at Econpapers || Download paper

  10. Asymmetric effects of oil prices and exchange rates on China’s industrial prices. (2019). Zhu, Huiming ; Chen, Xiuyun.
    In: Energy Economics.
    RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319303469.

    Full description at Econpapers || Download paper

  11. Asymmetric reactions of the US natural gas market and economic activity. (2019). Okimoto, Tatsuyoshi ; Nguyen, Bao H.
    In: Energy Economics.
    RePEc:eee:eneeco:v:80:y:2019:i:c:p:86-99.

    Full description at Econpapers || Download paper

  12. Dutch disease dynamics reconsidered. (2019). Torvik, Ragnar ; Thorsrud, Leif ; Bjørnland, Hilde ; Bjornland, Hilde C.
    In: European Economic Review.
    RePEc:eee:eecrev:v:119:y:2019:i:c:p:411-433.

    Full description at Econpapers || Download paper

  13. Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach. (2019). Vespignani, Joaquin ; Ravazzolo, Francesco ; Ferrari, Davide.
    In: Working Papers.
    RePEc:bny:wpaper:0083.

    Full description at Econpapers || Download paper

  14. OPECs crude game. (2019). Hvinden, Even Comfort.
    In: Working Papers.
    RePEc:bny:wpaper:0082.

    Full description at Econpapers || Download paper

  15. New Kid on the Block? China vs the US in World Oil Markets. (2019). Cross, Jamie ; Zhang, BO ; Nguyen, Bao H.
    In: Working Papers.
    RePEc:bny:wpaper:0074.

    Full description at Econpapers || Download paper

  16. Comovements In The Real Activity Of Developed And Emerging Economies: A Test Of Global Versus Specific International Factors. (2018). Djogbenou, Antoine.
    In: Working Paper.
    RePEc:qed:wpaper:1392.

    Full description at Econpapers || Download paper

  17. Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework. (2018). Drachal, Krzysztof.
    In: Energies.
    RePEc:gam:jeners:v:11:y:2018:i:5:p:1207-:d:145404.

    Full description at Econpapers || Download paper

  18. Forecasting crude oil price: Does exist an optimal econometric model?. (2018). de Albuquerquemello, Vinicius Phillipe ; Maia, Sinezio Fernandes ; da Nobrega, Cassio ; de Medeiros, Rennan Kertlly.
    In: Energy.
    RePEc:eee:energy:v:155:y:2018:i:c:p:578-591.

    Full description at Econpapers || Download paper

  19. Time-varying effects of oil supply and demand shocks on Chinas macro-economy. (2018). Lin, Boqiang ; Gong, XU.
    In: Energy.
    RePEc:eee:energy:v:149:y:2018:i:c:p:424-437.

    Full description at Econpapers || Download paper

  20. Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example. (2018). Drachal, Krzysztof.
    In: Energy Economics.
    RePEc:eee:eneeco:v:74:y:2018:i:c:p:208-251.

    Full description at Econpapers || Download paper

  21. On the China factor in international oil markets: A regime switching approach. (2018). Cross, Jamie ; Nguyen, Bao H ; Hou, Chenghan.
    In: Working Papers.
    RePEc:bny:wpaper:0069.

    Full description at Econpapers || Download paper

  22. The Shale Oil Boom and the U.S. Economy: Spillovers and Time-Varying Effects. (2018). Bjørnland, Hilde ; Zhulanova, Julia ; Bjornland, Hilde C.
    In: Working Papers.
    RePEc:bny:wpaper:0066.

    Full description at Econpapers || Download paper

  23. Dutch Disease Dynamics Reconsidered. (2018). Torvik, Ragnar ; Thorsrud, Leif ; Bjørnland, Hilde.
    In: Working Papers.
    RePEc:bny:wpaper:0062.

    Full description at Econpapers || Download paper

  24. On the exposure of the BRIC countries to global economic shocks. (2017). Dreger, Christian ; Belke, Ansgar ; Dubova, Irina.
    In: Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking.
    RePEc:zbw:vfsc17:168110.

    Full description at Econpapers || Download paper

  25. On the exposure of the BRIC countries to global economic shocks. (2017). Dreger, Christian ; Belke, Ansgar ; Dubova, Irina.
    In: GLO Discussion Paper Series.
    RePEc:zbw:glodps:37.

    Full description at Econpapers || Download paper

  26. World steel production: A new monthly indicator of global real economic activity. (2017). Vespignani, Joaquin ; Ravazzolo, Francesco.
    In: Working Papers.
    RePEc:tas:wpaper:23636.

    Full description at Econpapers || Download paper

  27. On the Exposure of the BRIC Countries to Global Economic Shocks. (2017). Dreger, Christian ; Belke, Ansgar ; Dubova, Irina.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp10634.

    Full description at Econpapers || Download paper

  28. Oil Price Pass-Through into Core Inflation. (2017). Luciani, Matteo ; Conflitti, Cristina.
    In: Finance and Economics Discussion Series.
    RePEc:fip:fedgfe:2017-85.

    Full description at Econpapers || Download paper

  29. Asymmetric Reactions of the U.S. Natural Gas Market and Economic Activity. (2017). Okimoto, Tatsuyoshi ; Tatsuyoshi, Okimoto ; Nguyen, Bao H.
    In: Discussion papers.
    RePEc:eti:dpaper:17102.

    Full description at Econpapers || Download paper

  30. The G7 business cycle in a globalized world. (2017). Carstensen, Kai ; Salzmann, L.
    In: Journal of International Money and Finance.
    RePEc:eee:jimfin:v:73:y:2017:i:pa:p:134-161.

    Full description at Econpapers || Download paper

  31. Determinants of the crude oil futures curve: Inventory, consumption and volatility. (2017). Yeung, Danny ; Thorp, Susan ; Nikitopoulos-Sklibosios, Christina ; Squires, Matthew.
    In: Journal of Banking & Finance.
    RePEc:eee:jbfina:v:84:y:2017:i:c:p:53-67.

    Full description at Econpapers || Download paper

  32. Forecasting GDP with global components: This time is different. (2017). Thorsrud, Leif ; Ravazzolo, Francesco ; Bjørnland, Hilde.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:33:y:2017:i:1:p:153-173.

    Full description at Econpapers || Download paper

  33. The relationship between global oil price shocks and Chinas output: A time-varying analysis. (2017). Cross, Jamie ; Nguyen, Bao H.
    In: Energy Economics.
    RePEc:eee:eneeco:v:62:y:2017:i:c:p:79-91.

    Full description at Econpapers || Download paper

  34. Fundamental and Financial Influences on the Co-movement of Oil and Gas Prices. (2017). Sévi, Benoît ; Chevallier, Julien ; Sevi, Benoit ; Derek, Julien Chevallier .
    In: The Energy Journal.
    RePEc:aen:journl:ej38-2-bunn.

    Full description at Econpapers || Download paper

  35. On the exposure of the BRIC countries to global economic shocks. (2016). Dreger, Christian ; Belke, Ansgar ; Dubova, Irina.
    In: Ruhr Economic Papers.
    RePEc:zbw:rwirep:622.

    Full description at Econpapers || Download paper

  36. What drives long-term oil market volatility? Fundamentals versus Speculation. (2016). Yin, Libo ; Zhou, Yimin.
    In: Economics Discussion Papers.
    RePEc:zbw:ifwedp:20162.

    Full description at Econpapers || Download paper

  37. On the exposure of the BRIC countries to global economic shocks. (2016). Dreger, Christian ; Belke, Ansgar ; Dubova, Irina.
    In: ROME Working Papers.
    RePEc:rmn:wpaper:201605.

    Full description at Econpapers || Download paper

  38. Oil Price Elasticities and Oil Price Fluctuations. (2016). Iacoviello, Matteo ; Caldara, Dario ; Cavallo, Michele.
    In: International Finance Discussion Papers.
    RePEc:fip:fedgif:1173.

    Full description at Econpapers || Download paper

  39. Commodity prices and fiscal policy design: Procyclical despite a rule. (2016). Thorsrud, Leif ; Bjørnland, Hilde.
    In: CAMA Working Papers.
    RePEc:een:camaaa:2016-27.

    Full description at Econpapers || Download paper

  40. Forecasting GDP with global components. This time is different. (2016). Thorsrud, Leif ; Ravazzolo, Francesco ; Bjørnland, Hilde.
    In: CAMA Working Papers.
    RePEc:een:camaaa:2016-26.

    Full description at Econpapers || Download paper

  41. Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics. (2016). Stock, J H ; Watson, M W.
    In: Handbook of Macroeconomics.
    RePEc:eee:macchp:v2-415.

    Full description at Econpapers || Download paper

  42. Country-specific oil supply shocks and the global economy: A counterfactual analysis. (2016). Pesaran, M ; Mohaddes, Kamiar.
    In: Energy Economics.
    RePEc:eee:eneeco:v:59:y:2016:i:c:p:382-399.

    Full description at Econpapers || Download paper

  43. An empirical analysis of the relationship between oil prices and the Chinese macro-economy. (2016). Wei, Yanfeng ; Guo, Xiaoying.
    In: Energy Economics.
    RePEc:eee:eneeco:v:56:y:2016:i:c:p:88-100.

    Full description at Econpapers || Download paper

  44. On the Exposure of the BRIC Countries to Global Economic Shocks. (2016). Dreger, Christian ; Belke, Ansgar ; Dubova, Irina.
    In: Discussion Papers of DIW Berlin.
    RePEc:diw:diwwpp:dp1594.

    Full description at Econpapers || Download paper

  45. The G7 Business Cycle in a Globalized World. (2016). Carstensen, Kai ; Salzmann, Leonard .
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_5980.

    Full description at Econpapers || Download paper

  46. Sovereign yields and the risk-taking channel of currency appreciation. (2016). SHIM, ILHYOCK ; Shin, Hyun Song ; Hofmann, Boris.
    In: BIS Working Papers.
    RePEc:bis:biswps:538.

    Full description at Econpapers || Download paper

  47. Multivariate Stochastic Volatility-Double Jump Model: an application for oil assets. (2016). Mauad, Roberto ; Laurini, Márcio ; Aiube, Fernando Antonio ; Lucena, Fernando Antonio .
    In: Working Papers Series.
    RePEc:bcb:wpaper:415.

    Full description at Econpapers || Download paper

  48. A new monthly indicator of global real economic activity. (2015). Vespignani, Joaquin ; Ravazzolo, Francesco.
    In: Working Papers.
    RePEc:tas:wpaper:22664.

    Full description at Econpapers || Download paper

  49. Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All?. (2015). Beidas-Strom, Samya ; Beckers, Benjamin.
    In: IMF Working Papers.
    RePEc:imf:imfwpa:2015/251.

    Full description at Econpapers || Download paper

  50. Causes and Consequences of Oil Price Shocks on the UK Economy. (2015). Pieroni, Luca ; Lorusso, Marco.
    In: CEERP Working Paper Series.
    RePEc:hwc:wpaper:002.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-02-24 02:04:38 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Sponsored by INOMICS. Last updated October, 6 2023. Contact: CitEc Team.

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy