IDEAS home Printed from https://ideas.repec.org/f/pna386.html
   My authors  Follow this author

Hanan Naser

Personal Details

First Name:Hanan
Middle Name:
Last Name:Naser
Suffix:
RePEc Short-ID:pna386
Business Administration Studies Arab Open University, Bahrain Road 3206, Block 732 A'ali Bahrain
+973-17407548

Affiliation

Arab Open University, Business Studies Department

http://www.aou.org.bh
A'ali, Bahrain

Research output

as
Jump to: Working papers Articles

Working papers

  1. Naser, Hanan & Ahmed, Abdul Rashid, 2016. "Oil Price Shocks and Stock Market Performance in Emerging Economies: Some Evidence using FAVAR Models," MPRA Paper 77868, University Library of Munich, Germany.
  2. Naser, Hanan & Alaali, Fatema, 2015. "Can Oil Prices Help Predict US Stock Market Returns: An Evidence Using a DMA Approach," MPRA Paper 65295, University Library of Munich, Germany, revised 25 Jun 2015.
  3. Naser, Hanan, 2014. "On the cointegration and causality between Oil market, Nuclear Energy Consumption, and Economic Growth: Evidence from Developed Countries," MPRA Paper 65252, University Library of Munich, Germany, revised 25 Mar 2015.

Articles

  1. Hanan Naser, 2017. "Can Gold Investments Provide a Good Hedge Against Inflation? An Empirical Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 7(1), pages 470-475.
  2. Hanan Naser, 2016. "The Role of the Gulf Cooperation Council's Sovereign Wealth Funds in the New Era of Oil," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1657-1664.
  3. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
  4. Naser, Hanan, 2015. "Analysing the long-run relationship among oil market, nuclear energy consumption, and economic growth: An evidence from emerging economies," Energy, Elsevier, vol. 89(C), pages 421-434.
  5. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
  6. Hanan Naser, 2015. "Can Nuclear Energy Stimulates Economic Growth? Evidence from Highly Industrialised Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 164-173.
  7. Hanan Naser, 2014. "Oil Market, Nuclear Energy Consumption and Economic Growth: Evidence from Emerging Economies," International Journal of Energy Economics and Policy, Econjournals, vol. 4(2), pages 288-296.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Naser, Hanan & Ahmed, Abdul Rashid, 2016. "Oil Price Shocks and Stock Market Performance in Emerging Economies: Some Evidence using FAVAR Models," MPRA Paper 77868, University Library of Munich, Germany.

    Cited by:

    1. Muhammad Ali, Khalid M. Iraqi, Abdul Waheed Khan, 2019. "Impact of Oil Prices on Stock Market Performance: Evidence from Top Oil Importing Countries," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 4(2), pages 1-14, October.
    2. Uğur Akkoç & Anıl Akçağlayan & Gamze Kargın Akkoç, 2021. "The impacts of oil price shocks in Turkey: sectoral evidence from the FAVAR approach," Economic Change and Restructuring, Springer, vol. 54(4), pages 1147-1171, November.

  2. Naser, Hanan & Alaali, Fatema, 2015. "Can Oil Prices Help Predict US Stock Market Returns: An Evidence Using a DMA Approach," MPRA Paper 65295, University Library of Munich, Germany, revised 25 Jun 2015.

    Cited by:

    1. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    2. Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
    3. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    4. Athambawa Jahfer & Abdul Hameed Mulafara, 2016. "Dividend policy and share price volatility: evidence from Colombo stock market," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 8(2), pages 97-108.
    5. Dorra Zouari & Achraf Ghorbel & Sonia Ghorbel-Zouari & Younes Boujelbène, 2014. "Volatility spillovers and dynamic correlation between liquidity risk factors in Tunisian banks," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 6(1), pages 1-26.

  3. Naser, Hanan, 2014. "On the cointegration and causality between Oil market, Nuclear Energy Consumption, and Economic Growth: Evidence from Developed Countries," MPRA Paper 65252, University Library of Munich, Germany, revised 25 Mar 2015.

    Cited by:

    1. Izabela Pruchnicka-Grabias, 2021. "The Relationship between Gold and Brent Crude Oil Prices: An Unrestricted Vector Autoregression Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 276-282.
    2. Akadiri, Ada Chigozie & Akadiri, Seyi Saint & Gungor, Hasan, 2019. "The role of natural gas consumption in Saudi Arabia's output and its implication for trade and environmental quality," Energy Policy, Elsevier, vol. 129(C), pages 230-238.

Articles

  1. Hanan Naser, 2017. "Can Gold Investments Provide a Good Hedge Against Inflation? An Empirical Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 7(1), pages 470-475.

    Cited by:

    1. José Alves & João Quental Gonçalves, 2022. "How Money relates to value? An empirical examination on Gold, Silver and Bitcoin," Working Papers REM 2022/0222, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    2. Zbyněk Revenda & Markéta Arltová, 2022. "Akcie, zlato a inflace - vztahy a souvislosti v posledních 25 letech [Stocks, Gold and Inflation - Relationships and Contexts Over the Last 25 Years]," Politická ekonomie, Prague University of Economics and Business, vol. 2022(3), pages 288-311.
    3. Hanan Naser, 2019. "Oil Price Fluctuation, Gold Returns and Inflationary Pressure: An Empirical Analysis Using Cointegration Approach," Applied Economics and Finance, Redfame publishing, vol. 6(2), pages 71-78, March.
    4. Memet Agustiar & Fariastuti Djafar & Afrizal, 2017. "Construction of an Optimum Currency Area Index Anchored to the Gold Dinar: The Case of Selected Islamic Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 51-56.

  2. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.

    Cited by:

    1. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
    2. Wang, Jue & Zhou, Hao & Hong, Tao & Li, Xiang & Wang, Shouyang, 2020. "A multi-granularity heterogeneous combination approach to crude oil price forecasting," Energy Economics, Elsevier, vol. 91(C).
    3. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    4. Lu-Tao Zhao & Shun-Gang Wang & Zhi-Gang Zhang, 2020. "Oil Price Forecasting Using a Time-Varying Approach," Energies, MDPI, vol. 13(6), pages 1-16, March.
    5. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
    6. Ikhlaas Gurrib & Qian Long Kweh & Davide Contu & Firuz Kamalov, 2021. "COVID-19, Short-selling Ban and Energy Stock Prices," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 1(1), pages 1-4.
    7. Lan Bai & Xiafei Li & Yu Wei & Guiwu Wei, 2022. "Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3694-3712, July.
    8. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
    9. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
    10. Cristiana Tudor & Andrei Anghel, 2021. "The Financialization of Crude Oil Markets and Its Impact on Market Efficiency: Evidence from the Predictive Ability and Performance of Technical Trading Strategies," Energies, MDPI, vol. 14(15), pages 1-19, July.
    11. Cheng, Xian & Wu, Peng & Liao, Stephen Shaoyi & Wang, Xuelian, 2023. "An integrated model for crude oil forecasting: Causality assessment and technical efficiency," Energy Economics, Elsevier, vol. 117(C).
    12. Wang, Tiantian & Qu, Wan & Zhang, Dayong & Ji, Qiang & Wu, Fei, 2022. "Time-varying determinants of China's liquefied natural gas import price: A dynamic model averaging approach," Energy, Elsevier, vol. 259(C).
    13. Donghua Wang & Yang Xin & Xiaohui Chang & Xingze Su, 2021. "Realized volatility forecasting and volatility spillovers: Evidence from Chinese non‐ferrous metals futures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2713-2731, April.
    14. Raymond Swaray & Afees A. Salisu, 2017. "The impact of crude oil prices on stock prices of oil firms: Should upstream-downstream dichotomy in supply chain be ignored?," Working Papers 021, Centre for Econometric and Allied Research, University of Ibadan.
    15. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    16. Xuluo Yin & Jiangang Peng & Tian Tang, 2018. "Improving the Forecasting Accuracy of Crude Oil Prices," Sustainability, MDPI, vol. 10(2), pages 1-9, February.
    17. Jan Prüser, 2019. "Adaptive learning from model space," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 29-38, January.
    18. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    19. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    20. E, Jianwei & Bao, Yanling & Ye, Jimin, 2017. "Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 412-427.
    21. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    22. Abdullah Sultan Al Shammre & Benaissa Chidmi, 2023. "Oil Price Forecasting Using FRED Data: A Comparison between Some Alternative Models," Energies, MDPI, vol. 16(11), pages 1-24, May.
    23. Zhao, Lu-Tao & Wang, Yi & Guo, Shi-Qiu & Zeng, Guan-Rong, 2018. "A novel method based on numerical fitting for oil price trend forecasting," Applied Energy, Elsevier, vol. 220(C), pages 154-163.
    24. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
    25. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    26. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    27. Zhaojie Luo & Xiaojing Cai & Katsuyuki Tanaka & Tetsuya Takiguchi & Takuji Kinkyo & Shigeyuki Hamori, 2019. "Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks," JRFM, MDPI, vol. 12(1), pages 1-13, January.
    28. Nademi, Arash & Nademi, Younes, 2018. "Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases," Energy Economics, Elsevier, vol. 74(C), pages 757-766.
    29. He, Shanshan & Wang, Yudong, 2017. "Revisiting the multifractality in stock returns and its modeling implications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 11-20.
    30. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    31. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    32. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    33. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    34. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    35. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
    36. Gao, Xiangyun & Fang, Wei & An, Feng & Wang, Yue, 2017. "Detecting method for crude oil price fluctuation mechanism under different periodic time series," Applied Energy, Elsevier, vol. 192(C), pages 201-212.
    37. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    38. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    39. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    40. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
    41. George Filis & Stavros Degiannakis & Zacharias Bragoudakis, 2022. "Forecasting macroeconomic indicators for Eurozone and Greece: How useful are the oil price assumptions?," Working Papers 296, Bank of Greece.
    42. Dong, Xiyong & Song, Li & Yoon, Seong-Min, 2021. "How have the dependence structures between stock markets and economic factors changed during the COVID-19 pandemic?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    43. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    44. Krüger, Jens & Ruths Sion, Sebastian, 2019. "Improving oil price forecasts by sparse VAR methods," Darmstadt Discussion Papers in Economics 237, Darmstadt University of Technology, Department of Law and Economics.
    45. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    46. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    47. Nicholas Apergis, 2023. "Forecasting energy prices: Quantile‐based risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 17-33, January.
    48. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
    49. Zhongxin Ni & Xing Lu & Wenjun Xue, 2021. "Does the belt and road initiative resolve the steel overcapacity in China? Evidence from a dynamic model averaging approach," Empirical Economics, Springer, vol. 61(1), pages 279-307, July.
    50. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    51. Li, Jingjing & Tang, Ling & Wang, Shouyang, 2020. "Forecasting crude oil price with multilingual search engine data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    52. Wang, TianTian & Zhang, Dayong & Clive Broadstock, David, 2019. "Financialization, fundamentals, and the time-varying determinants of US natural gas prices," Energy Economics, Elsevier, vol. 80(C), pages 707-719.
    53. Lu-Tao Zhao & Guan-Rong Zeng & Wen-Jing Wang & Zhi-Gang Zhang, 2019. "Forecasting Oil Price Using Web-based Sentiment Analysis," Energies, MDPI, vol. 12(22), pages 1-18, November.
    54. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    55. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    56. Degiannakis, Stavros & Filis, George, 2020. "Oil price assumptions for macroeconomic policy," MPRA Paper 100705, University Library of Munich, Germany.
    57. Zhang, Yue-Jun & Li, Zhao-Chen, 2021. "Forecasting the stock returns of Chinese oil companies: Can investor attention help?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 531-555.
    58. Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020. "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, vol. 87(C).
    59. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
    60. Bekiroglu, Korkut & Duru, Okan & Gulay, Emrah & Su, Rong & Lagoa, Constantino, 2018. "Predictive analytics of crude oil prices by utilizing the intelligent model search engine," Applied Energy, Elsevier, vol. 228(C), pages 2387-2397.
    61. Yingrui Zhou & Taiyong Li & Jiayi Shi & Zijie Qian, 2019. "A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices," Complexity, Hindawi, vol. 2019, pages 1-15, February.

  3. Naser, Hanan, 2015. "Analysing the long-run relationship among oil market, nuclear energy consumption, and economic growth: An evidence from emerging economies," Energy, Elsevier, vol. 89(C), pages 421-434.

    Cited by:

    1. Soytas, Ugur & Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2022. "Economic and environmental implications of the nuclear power phase-out in Belgium: Insights from time-series models and a partial differential equations algorithm," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 241-256.
    2. Bakirtas, Tahsin & Akpolat, Ahmet Gokce, 2018. "The relationship between energy consumption, urbanization, and economic growth in new emerging-market countries," Energy, Elsevier, vol. 147(C), pages 110-121.
    3. Buhari DOĞAN & Osman DEĞER, 2018. "The Energy Consumption and Economic Growth in the E7 Countries: Cointegration in Panel Data with Structural Breaks," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 63-75, December.
    4. Sheilla Nyasha & Yvonne Gwenhure & Nicholas M Odhiambo, 2018. "Energy consumption and economic growth in Ethiopia: A dynamic causal linkage," Energy & Environment, , vol. 29(8), pages 1393-1412, December.
    5. Junsheng Ha & Pei-Pei Tan & Kim-Leng Goh, 2018. "Linear and nonlinear causal relationship between energy consumption and economic growth in China: New evidence based on wavelet analysis," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-21, May.
    6. Somjit Barat, 2022. "Attitudes of the Indian Middle Class: A Theory of Planned Behavior Approach," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 8(1), pages 21-42, January.
    7. Adewuyi, Adeolu O. & Awodumi, Olabanji B., 2017. "Biomass energy consumption, economic growth and carbon emissions: Fresh evidence from West Africa using a simultaneous equation model," Energy, Elsevier, vol. 119(C), pages 453-471.
    8. Hassan, Syed Tauseef & Khan, Danish & Zhu, Bangzhu & Batool, Bushra, 2022. "Is public service transportation increase environmental contamination in China? The role of nuclear energy consumption and technological change," Energy, Elsevier, vol. 238(PC).
    9. Yvonne Gwenhure & Nicholas Odhiambo, 2015. "Energy consumption and growth: a review of international empirical literature," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 2015(3), pages 47-70.
    10. Jakubelskas Ugnius & Skvarciany Viktorija, 2022. "An Evaluation of Circular Economy Development in the Baltic States," Folia Oeconomica Stetinensia, Sciendo, vol. 22(2), pages 193-208, December.
    11. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
    12. Gong, Xu & Lin, Boqiang, 2018. "Time-varying effects of oil supply and demand shocks on China's macro-economy," Energy, Elsevier, vol. 149(C), pages 424-437.
    13. Kourtzidis, Stavros A. & Tzeremes, Panayiotis & Tzeremes, Nickolaos G., 2018. "Re-evaluating the energy consumption-economic growth nexus for the United States: An asymmetric threshold cointegration analysis," Energy, Elsevier, vol. 148(C), pages 537-545.
    14. Cheng, Yuk-Shing & Li, Raymond & Woo, Chi-Keung, 2021. "Regional energy-growth nexus and energy conservation policy in China," Energy, Elsevier, vol. 217(C).
    15. Hsiao, Cody Yu-Ling & Chen, Hsing Hung, 2018. "The contagious effects on economic development after resuming construction policy for nuclear power plants in Coastal China," Energy, Elsevier, vol. 152(C), pages 291-302.
    16. Wei, Yanfeng & Guo, Xiaoying, 2017. "Oil price shocks and China's stock market," Energy, Elsevier, vol. 140(P1), pages 185-197.
    17. Vladimir M. Cvetković & Adem Öcal & Yuliya Lyamzina & Eric K. Noji & Neda Nikolić & Goran Milošević, 2021. "Nuclear Power Risk Perception in Serbia: Fear of Exposure to Radiation vs. Social Benefits," Energies, MDPI, vol. 14(9), pages 1-19, April.
    18. Muhammad Imran & Xiangyang Liu & Rongyu Wang & Shah Saud & Yun Zhao & Muhammad Jalal Khan, 2022. "The Influence of Digital Economy and Society Index on Sustainable Development Indicators: The Case of European Union," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    19. Buhari DOĞAN & Osman DEĞER, 2018. "The role of economic growth and energy consumption on CO2 emissions in E7 countries," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(2(615), S), pages 231-246, Summer.

  4. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.

    Cited by:

    1. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.

  5. Hanan Naser, 2015. "Can Nuclear Energy Stimulates Economic Growth? Evidence from Highly Industrialised Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 164-173.

    Cited by:

    1. Naser, Hanan, 2014. "On the cointegration and causality between Oil market, Nuclear Energy Consumption, and Economic Growth: Evidence from Developed Countries," MPRA Paper 65252, University Library of Munich, Germany, revised 25 Mar 2015.
    2. Naser, Hanan, 2015. "Analysing the long-run relationship among oil market, nuclear energy consumption, and economic growth: An evidence from emerging economies," Energy, Elsevier, vol. 89(C), pages 421-434.
    3. Hazuki Ishida, 2018. "Can Nuclear Energy Contribute to the Transition Toward a Low-carbon Economy? The Japanese Case," International Journal of Energy Economics and Policy, Econjournals, vol. 8(1), pages 62-68.
    4. Adekoya, Oluwasegun B. & Ogunnusi, Timilehin P. & Oliyide, Johnson A., 2021. "Sector-by-sector non-renewable energy consumption shocks and manufacturing performance in the U.S.: Analysis of the asymmetric issue with nonlinear ARDL and the role of structural breaks," Energy, Elsevier, vol. 222(C).
    5. Ben-Salha, Ousama & Hkiri, Besma & Aloui, Chaker, 2018. "Sectoral energy consumption by source and output in the U.S.: New evidence from wavelet-based approach," Energy Economics, Elsevier, vol. 72(C), pages 75-96.
    6. Sergey Kashurnikov & Valeriy Prasolov & Vladimir Gorbanyov & Rodion Rogulin, 2020. "Nuclear Power Production: The Future or the Past?," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 131-141.

  6. Hanan Naser, 2014. "Oil Market, Nuclear Energy Consumption and Economic Growth: Evidence from Emerging Economies," International Journal of Energy Economics and Policy, Econjournals, vol. 4(2), pages 288-296.

    Cited by:

    1. Man-Keun Kim & Kangil Lee, 2015. "Dynamic Interactions between Carbon and Energy Prices in the U.S. Regional Greenhouse Gas Initiative," International Journal of Energy Economics and Policy, Econjournals, vol. 5(2), pages 494-501.
    2. Hanan Naser, 2015. "Can Nuclear Energy Stimulates Economic Growth? Evidence from Highly Industrialised Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 164-173.
    3. Naser, Hanan, 2014. "On the cointegration and causality between Oil market, Nuclear Energy Consumption, and Economic Growth: Evidence from Developed Countries," MPRA Paper 65252, University Library of Munich, Germany, revised 25 Mar 2015.
    4. Sheilla Nyasha & Yvonne Gwenhure & Nicholas M Odhiambo, 2018. "Energy consumption and economic growth in Ethiopia: A dynamic causal linkage," Energy & Environment, , vol. 29(8), pages 1393-1412, December.
    5. Uğur Akkoç & Anıl Akçağlayan & Gamze Kargın Akkoç, 2021. "The impacts of oil price shocks in Turkey: sectoral evidence from the FAVAR approach," Economic Change and Restructuring, Springer, vol. 54(4), pages 1147-1171, November.
    6. Naser, Hanan, 2015. "Analysing the long-run relationship among oil market, nuclear energy consumption, and economic growth: An evidence from emerging economies," Energy, Elsevier, vol. 89(C), pages 421-434.
    7. Tomas Vlcek & Martin Jirusek & James Henderson, 2015. "Risk Assessment in Construction Process in Nuclear Sector within the Central and Eastern Europe," International Journal of Energy Economics and Policy, Econjournals, vol. 5(2), pages 482-493.
    8. Jos Alberto Fuinhas & Ant nio Cardoso Marques & Alcino Pinto Couto, 2015. "Oil-Growth Nexus in Oil Producing Countries: Macro Panel Evidence," International Journal of Energy Economics and Policy, Econjournals, vol. 5(1), pages 148-163.
    9. Vladimir M. Cvetković & Adem Öcal & Yuliya Lyamzina & Eric K. Noji & Neda Nikolić & Goran Milošević, 2021. "Nuclear Power Risk Perception in Serbia: Fear of Exposure to Radiation vs. Social Benefits," Energies, MDPI, vol. 14(9), pages 1-19, April.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ENE: Energy Economics (3) 2015-07-04 2015-07-11 2017-04-02
  2. NEP-CIS: Confederation of Independent States (1) 2017-04-02
  3. NEP-FMK: Financial Markets (1) 2015-07-04
  4. NEP-FOR: Forecasting (1) 2015-07-04
  5. NEP-GRO: Economic Growth (1) 2015-07-11
  6. NEP-ORE: Operations Research (1) 2015-07-04

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Hanan Naser should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.