Krzysztof Drachal

Personal Details

First Name:	Krzysztof
Middle Name:
Last Name:	Drachal
Suffix:
RePEc Short-ID:	pdr170
	[This author has chosen not to make the email address public]

Affiliation

Wydział Nauk Ekonomicznych
Uniwersytet Warszawski

Warszawa, Poland
http://www.wne.uw.edu.pl/
RePEc:edi:fesuwpl (more details at EDIRC)

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.

Articles

1. Mosab I. Tabash & Suhaib Anagreh & Bilal Haider Subhani & Mamdouh Abdulaziz Saleh Al-Faryan & Krzysztof Drachal, 2023. "Tourism, Remittances, and Foreign Investment as Determinants of Economic Growth: Empirical Evidence from Selected Asian Economies," Economies, MDPI, vol. 11(2), pages 1-15, February.

   Cited by:

   1. Mohammed Shahedur Rahman, 2023. "Impact of Remittance on Gross Domestic Product (GDP) growth in Bangladesh: An overview from 2000 to 2020," International Journal of Science and Business, IJSAB International, vol. 28(1), pages 183-192.

2. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.

   Cited by:

   1. Lozano Navarro, Francisco-Javier & Idrovo Aguirre, Byron, 2023. "Shocks regulatorios al mercado inmobiliario de Chile: ¿Cuánto del IVA a la vivienda se transfiere a precio de venta? [Regulatory shocks to housing market: How much of the Chilean VAT on housing sal," MPRA Paper 120017, University Library of Munich, Germany.

3. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).

   Cited by:

   1. Marek Vochozka & Svatopluk Janek & Zuzana Rowland, 2023. "Coffee as an Identifier of Inflation in Selected US Agglomerations," Forecasting, MDPI, vol. 5(1), pages 1-17, January.
   2. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
   3. Li, Ranran, 2023. "Forecasting energy spot prices: A multiscale clustering recognition approach," Resources Policy, Elsevier, vol. 81(C).
   4. Jonathan Berrisch & Florian Ziel, 2022. "Distributional modeling and forecasting of natural gas prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1065-1086, September.
   5. 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).
   6. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.

4. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).

   Cited by:

   1. Hasnain Iftikhar & Aimel Zafar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
   2. Xu, Nuo & Kasimov, Ikboljon & Wang, Yanan, 2022. "Unlocking private investment as a new determinant of green finance for renewable development in China," Renewable Energy, Elsevier, vol. 198(C), pages 1121-1130.
   3. Vicknair, David & Tansey, Michael & O'Brien, Thomas E., 2022. "Measuring fossil fuel reserves: A simulation and review of the U.S. Securities and Exchange Commission approach," Resources Policy, Elsevier, vol. 79(C).
   4. Hapau Razvan Gabriel, 2023. "Capital Market Volatility During Crises: Oil Price Insights, VIX Index, and Gold Price Analysis," Management & Marketing, Sciendo, vol. 18(3), pages 290-314, September.
   5. Chang, Lei & Taghizadeh-Hesary, Farhad & Saydaliev, Hayot Berk, 2022. "How do ICT and renewable energy impact sustainable development?," Renewable Energy, Elsevier, vol. 199(C), pages 123-131.
   6. Ahmet Faruk Aysan & Ali Polat & Hasan Tekin & Ahmet Tunali, 2022. "The Ascent of Geopolitics: Scientometric Analysis and Ramifications of Geopolitical Risk," Working Papers hal-03638273, HAL.
      • Aysan, Ahmet Faruk & Polat, Ali Yavuz & Tekin, Hasan & Tunalı, Ahmet Semih, 2022. "The Ascent of Geopolitics: Scientometric Analysis and Ramifications of Geopolitical Risk," MPRA Paper 112741, University Library of Munich, Germany.
   7. Wu, Junhao & Dong, Jinghan & Wang, Zhaocai & Hu, Yuan & Dou, Wanting, 2023. "A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast," Resources Policy, Elsevier, vol. 83(C).
   8. Liu, Yang & Dilanchiev, Azer & Xu, Kaifei & Hajiyeva, Aytan Merdan, 2022. "Financing SMEs and business development as new post Covid-19 economic recovery determinants," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 554-567.
   9. Witold Chmielarz & Marek Zborowski & Mesut Atasever & Jin Xuetao & Justyna Szpakowska, 2023. "The Role of ICT in Creating the Conscious Development of Green Energy Applications in Times of Crisis: Comparison of Poland, Türkiye and People's Republic of China," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 492-519.
   10. Sadiq, Muhammad & Lin, Chia-Yang & Wang, Kuan-Ting & Trung, Lam Minh & Duong, Khoa Dang & Ngo, Thanh Quang, 2022. "Commodity dynamism in the COVID-19 crisis: Are gold, oil, and stock commodity prices, symmetrical?," Resources Policy, Elsevier, vol. 79(C).
   11. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
   12. Turgut Yokuş, 2024. "Early Warning Systems for World Energy Crises," Sustainability, MDPI, vol. 16(6), pages 1-18, March.

5. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.

   Cited by:

   1. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification," Mathematics, MDPI, vol. 10(20), pages 1-21, October.
   2. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
   3. Kuang-Sheng Liu & Iskandar Muda & Ming-Hung Lin & Ngakan Ketut Acwin Dwijendra & Gaylord Carrillo Caballero & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room," Sustainability, MDPI, vol. 15(2), pages 1-14, January.

6. Krzysztof Drachal, 2020. "Forecasting unemployment rate in Poland with dynamic model averaging and internet searches," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 23(4), pages 368-389.

   Cited by:

   1. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

7. Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.

   Cited by:

   1. Anastasiia Pankratova, 2024. "Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 32-52, March.

8. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.

   Cited by:

   1. Zhiling Xu & Hualing Deng & Qiufeng Wu, 2021. "Prediction of Soybean Price Trend via a Synthesis Method With Multistage Model," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 12(4), pages 1-13, October.
   2. Yan Guo & Xiaonan Hu & Zepeng Wang & Wei Tang & Deyu Liu & Yunzhong Luo & Hongxiang Xu, 2021. "The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(11), pages 457-467.
   3. Jesus Crespo Cuaresma & Jaroslava Hlouskova & Michael Obersteiner, 2021. "Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1245-1273, November.
   4. Tserenpurev Chuluunsaikhan & Ga-Ae Ryu & Kwan-Hee Yoo & HyungChul Rah & Aziz Nasridinov, 2020. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea," Agriculture, MDPI, vol. 10(11), pages 1-22, October.

9. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).

   Cited by:

   1. Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
   2. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
   3. Yishun Liu & Chunhua Yang & Keke Huang & Weiping Liu, 2023. "A Multi-Factor Selection and Fusion Method through the CNN-LSTM Network for Dynamic Price Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
   4. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.

10. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.

    Cited by:

    1. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
    2. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    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. Ali Jadidzadeh & Mobin Mirzababaei & Apostolos Serletis, 2022. "Oil Prices and the Hydrocarbon Markets: A Review," Energies, MDPI, vol. 15(17), pages 1-9, August.

11. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.

    Cited by:

    1. Peter Buchholz & Friedrich-W. Wellmer & Dennis Bastian & Maren Liedtke, 2020. "Leaning against the wind: low-price benchmarks for acting anticyclically in the metal markets," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 33(1), pages 81-100, July.
    2. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    3. Yan Guo & Dezhao Tang & Wei Tang & Senqi Yang & Qichao Tang & Yang Feng & Fang Zhang, 2022. "Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    4. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.

12. Krzysztof Drachal, 2018. "Exchange Rate and Oil Price Interactions in Selected CEE Countries," Economies, MDPI, vol. 6(2), pages 1-21, May.

    Cited by:

    1. Adrian Neacsa & Jianu Daniel Muresan & Marian Catalin Voica & Otilia Manta & Mihail Vincentiu Ivan, 2023. "Oil Price—A Sensor for the Performance of Romanian Oil Manufacturing Companies," Energies, MDPI, vol. 16(5), pages 1-18, February.
    2. Witold Orzeszko, 2021. "Nonlinear Causality between Crude Oil Prices and Exchange Rates: Evidence and Forecasting," Energies, MDPI, vol. 14(19), pages 1-16, September.

13. 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.

    Cited by:

    1. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
    2. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
    3. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    4. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    5. Kliber, Agata & Łęt, Blanka, 2022. "Degree of connectedness and the transfer of news across the oil market and the European stocks," Energy, Elsevier, vol. 239(PC).
    6. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    7. Shian-Chang Huang & Cheng-Feng Wu, 2018. "Energy Commodity Price Forecasting with Deep Multiple Kernel Learning," Energies, MDPI, vol. 11(11), pages 1-16, November.

14. Krzysztof DRACHAL, 2017. "Volatility Clustering, Leverage Effects and Risk-Return Tradeoff in the Selected Stock Markets in the CEE Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-53, September.

    Cited by:

    1. Zouhaier Dhifaoui, 2022. "Determinism and Non-linear Behaviour of Log-return and Conditional Volatility: Empirical Analysis for 26 Stock Markets," South Asian Journal of Macroeconomics and Public Finance, , vol. 11(1), pages 69-94, June.
    2. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Phouphet Kyophilavong, 2021. "Nonlinearities and Chaos: A New Analysis of CEE Stock Markets," Mathematics, MDPI, vol. 9(7), pages 1-13, March.

15. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.

    Cited by:

    1. Razmi, Seyedeh Fatemeh & Razmi, Seyed Mohammad Javad, 2023. "The role of stock markets in the US, Europe, and China on oil prices before and after the COVID-19 announcement," Resources Policy, Elsevier, vol. 81(C).
    2. Zhao, Yiran & Gao, Xiangyun & An, Haizhong & Xi, Xian & Sun, Qingru & Jiang, Meihui, 2020. "The effect of the mined cobalt trade dependence Network's structure on trade price," Resources Policy, Elsevier, vol. 65(C).
    3. Robert A. Hill & Paulo M. M. Rodrigues, 2022. "Forgetting approaches to improve forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1356-1371, November.
       • Paulo M.M. Rodrigues & Robert Hill, 2022. "Forgetting Approaches to Improve Forecasting," Working Papers w202208, Banco de Portugal, Economics and Research Department.
    4. Theo Notteboom & Thanos Pallis & Jean-Paul Rodrigue, 2021. "Disruptions and resilience in global container shipping and ports: the COVID-19 pandemic versus the 2008–2009 financial crisis," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 179-210, June.
    5. 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.
    6. Xuluo Yin & Jiangang Peng & Tian Tang, 2018. "Improving the Forecasting Accuracy of Crude Oil Prices," Sustainability, MDPI, vol. 10(2), pages 1-9, February.
    7. Jan Prüser, 2019. "Adaptive learning from model space," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 29-38, January.
    8. 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).
    9. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    10. Sakar Hasan Hamza & Qingna Li, 2023. "The Dynamics of US Gasoline Demand and Its Prediction: An Extended Dynamic Model Averaging Approach," Energies, MDPI, vol. 16(12), pages 1-13, June.
    11. Bedoui, Rihab & Braiek, Sana & Guesmi, Khaled & Chevallier, Julien, 2019. "On the conditional dependence structure between oil, gold and USD exchange rates: Nested copula based GJR-GARCH model," Energy Economics, Elsevier, vol. 80(C), pages 876-889.
    12. Hardik A. Marfatia & Rangan Gupta & Esin Cakan, 2019. "Dynamic Impact of the U.S. Monetary Policy on Oil Market Returns and Volatility," Working Papers 201916, University of Pretoria, Department of Economics.
        • Marfatia, Hardik A. & Gupta, Rangan & Cakan, Esin, 2021. "Dynamic impact of the U.S. monetary policy on oil market returns and volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 159-169.
    13. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
        • Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 81568, University Library of Munich, Germany.
        • Steel, Mark F. J., 2017. "Model Averaging and its Use in Economics," MPRA Paper 90110, University Library of Munich, Germany, revised 16 Nov 2018.
    14. 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.
    15. Oglend, Atle, 2022. "The commodities/equities beta term-structure," Journal of Commodity Markets, Elsevier, vol. 28(C).
    16. 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.
    17. 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.
    18. 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).
    19. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    20. Ernie Hendrawaty & Rialdi Azhar & Fajrin Satria Dwi Kesumah & Sari Indah Oktanti Sembiring & Mega Metalia, 2021. "Modelling and Forecasting Crude Oil Prices during COVID-19 Pandemic," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 149-154.
    21. 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).
    22. Guo, Jingjun & Zhao, Zhengling & Sun, Jingyun & Sun, Shaolong, 2022. "Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework," Resources Policy, Elsevier, vol. 77(C).
    23. 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.
    24. Li, Mingchen & Cheng, Zishu & Lin, Wencan & Wei, Yunjie & Wang, Shouyang, 2023. "What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 123(C).
    25. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    26. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    27. 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.
    28. 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).
    29. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    30. Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.
    31. Beckmann, Joscha & Czudaj, Robert L. & Arora, Vipin, 2020. "The relationship between oil prices and exchange rates: Revisiting theory and evidence," Energy Economics, Elsevier, vol. 88(C).
    32. 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.
    33. Krzysztof Drachal, 2022. "Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression," Energies, MDPI, vol. 16(1), pages 1-29, December.
    34. Jiaying Peng & Zhenghui Li & Benjamin M. Drakeford, 2020. "Dynamic Characteristics of Crude Oil Price Fluctuation—From the Perspective of Crude Oil Price Influence Mechanism," Energies, MDPI, vol. 13(17), pages 1-19, August.
    35. 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.
    36. Ding, Hui & Huang, Yisu & Wang, Jiqian, 2023. "Have the predictability of oil changed during the COVID-19 pandemic: Evidence from international stock markets," International Review of Financial Analysis, Elsevier, vol. 87(C).
    37. 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.
    38. 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.
    39. 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).
    40. 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).
    41. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    42. 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.
    43. Linlin Zhao & Jasper Mbachu & Zhansheng Liu, 2019. "Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development," Sustainability, MDPI, vol. 11(9), pages 1-18, April.
    44. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    45. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
    46. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    47. 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.
    48. 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.
    49. Theodosios Perifanis, 2019. "Detecting West Texas Intermediate (WTI) Prices’ Bubble Periods," Energies, MDPI, vol. 12(14), pages 1-16, July.
    50. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
    51. 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).
    52. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    53. 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).
    54. 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.
    55. Michail Filippidis & George Filis & Georgios Magkonis & Panagiotis Tzouvanas, 2023. "Evaluating robust determinants of the WTI/Brent oil price differential: A dynamic model averaging analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 807-825, June.
    56. 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.
    57. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    58. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
    59. Trabelsi, Nader & Tiwari, Aviral Kumar & Hammoudeh, Shawkat, 2022. "Spillovers and directional predictability between international energy commodities and their implications for optimal portfolio and hedging," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    60. 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).
    61. Dong, Xiyong & Yoon, Seong-Min, 2023. "Effect of weather and environmental attentions on financial system risks: Evidence from Chinese high- and low-carbon assets," Energy Economics, Elsevier, vol. 121(C).
    62. Lin, Boqiang & Su, Tong, 2021. "Do China's macro-financial factors determine the Shanghai crude oil futures market?," International Review of Financial Analysis, Elsevier, vol. 78(C).

16. Krzysztof DRACHAL, 2015. "The Structural Stability of a One-Day Risk Premium in View of the Recent Financial Crisis," Expert Journal of Economics, Sprint Investify, vol. 3(2), pages 136-142.

    Cited by:

    1. Tomas Heryan & Jan Ziegelbauer, 2016. "Volatility Of Yields Of Government Bonds Among Giips Countries During The Sovereign Debt Crisis In The Euro Area," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 11(1), pages 61-74, March.

17. Drachal Krzysztof, 2014. "Property Prices and Regional Labor Markets in Poland," The European Journal of Applied Economics, Sciendo, vol. 11(1), pages 5-15, April.

    Cited by:

    1. David Mazáček & Jiří Panoš, 2022. "Key determinants of new residential real estate prices in Prague," FFA Working Papers 5.002, Prague University of Economics and Business, revised 11 Apr 2023.
    2. Krzysztof DRACHAL, 2015. "Cointegration of Property Prices in Poland," Expert Journal of Economics, Sprint Investify, vol. 3(1), pages 1-4.
    3. Chuanhao Tian & Wenjun Ji & Sijin Chen & Jinqun Wu, 2020. "The Time and Spatial Effects of A “City-County Merger” on Housing Prices—Evidence from Fuyang," Sustainability, MDPI, vol. 12(4), pages 1-26, February.

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