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Zhiwei Shen

Personal Details

First Name:Zhiwei
Middle Name:
Last Name:Shen
Suffix:
RePEc Short-ID:psh810
[This author has chosen not to make the email address public]
http://www.agrar.hu-berlin.de/en/institut-en/departments/daoe/abl-en/ma-en/shen-en/standardseite-en

Affiliation

(50%) Sonderforschungsbereich 649: Ökonomisches Risiko
Wirtschaftswissenschaftliche Fakultät
Humboldt-Universität Berlin

Berlin, Germany
http://sfb649.wiwi.hu-berlin.de/
RePEc:edi:sohubde (more details at EDIRC)

(50%) Department für Agrarökonomie
Institut für Agrar- und Gartenbauwissenschaften
Humboldt-Universität Berlin

Berlin, Germany
http://www.agrar.hu-berlin.de/fakultaet/departments/daoe
RePEc:edi:iahubde (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Helbing, Georg & Shen, Zhiwei & Odening, Martin & Ritter, Matthias, 2017. "Estimating location values of agricultural land," SFB 649 Discussion Papers 2017-002, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  2. Shen, Zhiwei, 2016. "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wageningen, The Netherlands 249984, European Association of Agricultural Economists.
  3. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  4. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  5. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2014. "Designing an index for assessing wind energy potential," SFB 649 Discussion Papers 2014-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  6. Osipenko, Maria & Shen, Zhiwei & Odening, Martin, 2014. "Is there a demand for multi-year crop insurance?," SFB 649 Discussion Papers 2014-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  7. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2014. "Designing an index for assessing wind energy potential," SFB 649 Discussion Papers 2014-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  8. Shen, Zhiwei & Odening, Martin & Okhrin, Ostap, 2013. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," SFB 649 Discussion Papers 2013-030, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  9. Shen, Zhiwei & Odening, Martin & Okhrin, Ostap, 2013. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149431, Agricultural and Applied Economics Association.
  10. Shen, Zhiwei & Odening, Martin, 2012. "Coping with Systemic Risk in Index-based Crop Insurance," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122555, European Association of Agricultural Economists.

    repec:hum:wpaper:sfb649dp2014-025 is not listed on IDEAS

Articles

  1. Zhiwei Shen & Martin Odening & Ostap Okhrin, 2016. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(2), pages 237-269.
  2. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
  3. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
  4. Maria Osipenko & Zhiwei Shen & Martin Odening, 2015. "Is there a demand for multi-year crop insurance?," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 75(1), pages 92-102, May.
  5. Martin Odening & Zhiwei Shen, 2014. "Challenges of insuring weather risk in agriculture," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 74(2), pages 188-199, July.
  6. Zhiwei Shen & Martin Odening, 2013. "Coping with systemic risk in index-based crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 44(1), pages 1-13, January.

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. Helbing, Georg & Shen, Zhiwei & Odening, Martin & Ritter, Matthias, 2017. "Estimating location values of agricultural land," SFB 649 Discussion Papers 2017-002, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Ritter, Matthias & Hüttel, Silke & Odening, Martin & Seifert, Stefan, 2020. "Revisiting the relationship between land price and parcel size in agriculture," Land Use Policy, Elsevier, vol. 97(C).
    2. Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2019. "Bodenwertermittlung mit statistischen Methoden [Land value appraisal using statistical methods]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 5(1), pages 131-154, November.
    3. Kolbe, Jens & Schulz, Rainer & Wersing, Martin & Werwatz, Axel, 2019. "Land value appraisal using statistical methods," FORLand Working Papers 07 (2019), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    4. Ritter, Matthias & Hüttel, Silke & Odening, Martin & Seifert, Stefan, 2019. "Revisiting the relationship between land price and parcel size," FORLand Working Papers 08 (2019), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    5. Myrna, Olena, 2023. "Competition in online land lease auctions in Ukraine: Reduced-form estimation," Land Use Policy, Elsevier, vol. 125(C).
    6. Xin, Liangjie & Li, Xiubin, 2019. "Rental rates of grain land for consolidated plots and their determinants in present-day China," Land Use Policy, Elsevier, vol. 86(C), pages 421-426.

  2. Shen, Zhiwei, 2016. "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wageningen, The Netherlands 249984, European Association of Agricultural Economists.

    Cited by:

    1. Ritter, Matthias & Helbing, Georg & Shen, Zhiwei & Odening, Martin, 2017. "Estimating Location Values of Agricultural Land," 57th Annual Conference, Weihenstephan, Germany, September 13-15, 2017 261985, German Association of Agricultural Economists (GEWISOLA).
    2. Yong Liu & Alan P. Ker, 2021. "Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 231-257, March.

  3. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Spatial and temporal assessments of complementarity for renewable energy resources in China," Energy, Elsevier, vol. 177(C), pages 262-275.
    2. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    3. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    4. Lucheroni, Carlo & Boland, John & Ragno, Costantino, 2019. "Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models," Applied Energy, Elsevier, vol. 239(C), pages 1226-1241.
    5. Sherzod N. Tashpulatov, 2021. "Modeling and Estimating Volatility of Day-Ahead Electricity Prices," Mathematics, MDPI, vol. 9(7), pages 1-11, March.
    6. Fu, Yang & Zheng, Zeyu, 2020. "Volatility modeling and the asymmetric effect for China’s carbon trading pilot market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    7. Fu, Wenlong & Zhang, Kai & Wang, Kai & Wen, Bin & Fang, Ping & Zou, Feng, 2021. "A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM," Renewable Energy, Elsevier, vol. 164(C), pages 211-229.
    8. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
    9. Gong, Xu & Wen, Fenghua & Xia, X.H. & Huang, Jianbai & Pan, Bin, 2017. "Investigating the risk-return trade-off for crude oil futures using high-frequency data," Applied Energy, Elsevier, vol. 196(C), pages 152-161.
    10. Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
    11. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    12. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
    13. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    14. Hugo T. V. Gouveia & Murilo A. Souza & Aida A. Ferreira & Jonata C. de Albuquerque & Otoni Nóbrega Neto & Milde Maria da Silva Lira & Ronaldo R. B. de Aquino, 2023. "Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(6), pages 1-15, March.
    15. Bonou, Alexandra & Laurent, Alexis & Olsen, Stig I., 2016. "Life cycle assessment of onshore and offshore wind energy-from theory to application," Applied Energy, Elsevier, vol. 180(C), pages 327-337.
    16. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
    17. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    18. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    19. Sherzod N. Tashpulatov, 2021. "The Impact of Regulatory Reforms on Demand Weighted Average Prices," Mathematics, MDPI, vol. 9(10), pages 1-15, May.
    20. Liu, Liuchen & Zhu, Tong & Pan, Yu & Wang, Hai, 2017. "Multiple energy complementation based on distributed energy systems – Case study of Chongming county, China," Applied Energy, Elsevier, vol. 192(C), pages 329-336.
    21. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.

  4. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Spatial and temporal assessments of complementarity for renewable energy resources in China," Energy, Elsevier, vol. 177(C), pages 262-275.
    2. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
    3. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    4. Lucheroni, Carlo & Boland, John & Ragno, Costantino, 2019. "Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models," Applied Energy, Elsevier, vol. 239(C), pages 1226-1241.
    5. Sherzod N. Tashpulatov, 2021. "Modeling and Estimating Volatility of Day-Ahead Electricity Prices," Mathematics, MDPI, vol. 9(7), pages 1-11, March.
    6. Fu, Yang & Zheng, Zeyu, 2020. "Volatility modeling and the asymmetric effect for China’s carbon trading pilot market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    7. Fu, Wenlong & Zhang, Kai & Wang, Kai & Wen, Bin & Fang, Ping & Zou, Feng, 2021. "A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM," Renewable Energy, Elsevier, vol. 164(C), pages 211-229.
    8. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
    9. Gong, Xu & Wen, Fenghua & Xia, X.H. & Huang, Jianbai & Pan, Bin, 2017. "Investigating the risk-return trade-off for crude oil futures using high-frequency data," Applied Energy, Elsevier, vol. 196(C), pages 152-161.
    10. Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
    11. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    12. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
    13. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    14. Hugo T. V. Gouveia & Murilo A. Souza & Aida A. Ferreira & Jonata C. de Albuquerque & Otoni Nóbrega Neto & Milde Maria da Silva Lira & Ronaldo R. B. de Aquino, 2023. "Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(6), pages 1-15, March.
    15. Bonou, Alexandra & Laurent, Alexis & Olsen, Stig I., 2016. "Life cycle assessment of onshore and offshore wind energy-from theory to application," Applied Energy, Elsevier, vol. 180(C), pages 327-337.
    16. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
    17. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    18. Wasilewski, J. & Baczynski, D., 2017. "Short-term electric energy production forecasting at wind power plants in pareto-optimality context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 177-187.
    19. Sherzod N. Tashpulatov, 2021. "The Impact of Regulatory Reforms on Demand Weighted Average Prices," Mathematics, MDPI, vol. 9(10), pages 1-15, May.
    20. Liu, Liuchen & Zhu, Tong & Pan, Yu & Wang, Hai, 2017. "Multiple energy complementation based on distributed energy systems – Case study of Chongming county, China," Applied Energy, Elsevier, vol. 192(C), pages 329-336.
    21. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.

  5. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2014. "Designing an index for assessing wind energy potential," SFB 649 Discussion Papers 2014-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Murthy, K.S.R. & Rahi, O.P., 2016. "Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India," Renewable Energy, Elsevier, vol. 99(C), pages 1137-1145.
    2. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    4. Ritter, Matthias & Deckert, Lars, 2015. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," SFB 649 Discussion Papers 2015-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Li, Jiale & Yu, Xiong (Bill), 2018. "Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis," Energy, Elsevier, vol. 147(C), pages 1092-1107.
    6. Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    7. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices," Applied Energy, Elsevier, vol. 238(C), pages 1179-1191.
    8. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    9. Engelhorn, Thorsten & Müsgens, Felix, 2018. "How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany," Energy Economics, Elsevier, vol. 72(C), pages 542-557.
    10. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    11. Mohamed Elnaggar & Ezzaldeen Edwan & Matthias Ritter, 2017. "Wind Energy Potential of Gaza Using Small Wind Turbines: A Feasibility Study," Energies, MDPI, vol. 10(8), pages 1-13, August.
    12. Laudari, R. & Sapkota, B. & Banskota, K., 2018. "Validation of wind resource in 14 locations of Nepal," Renewable Energy, Elsevier, vol. 119(C), pages 777-786.
    13. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    14. Alina Wilke & Paul J.J. Welfens, 2020. "Urban Wind Energy Production Potential: New Opportunities," EIIW Discussion paper disbei287, Universitätsbibliothek Wuppertal, University Library.
    15. Melzer, Awdesch & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2017. "Pricing Green Financial Products," SFB 649 Discussion Papers 2017-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Ramirez Camargo, Luis & Gruber, Katharina & Nitsch, Felix, 2019. "Assessing variables of regional reanalysis data sets relevant for modelling small-scale renewable energy systems," Renewable Energy, Elsevier, vol. 133(C), pages 1468-1478.
    17. Tajeddin, Alireza & Fazelpour, Farivar, 2016. "Towards realistic design of wind dams: An innovative approach to enhance wind potential," Applied Energy, Elsevier, vol. 182(C), pages 282-298.
    18. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    19. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).

  6. Osipenko, Maria & Shen, Zhiwei & Odening, Martin, 2014. "Is there a demand for multi-year crop insurance?," SFB 649 Discussion Papers 2014-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Ying-Erh Chen & Barry K Goodwin, 2015. "Policy Design of Multi-Year Crop Insurance Contracts with Partial Payments," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-15, December.

  7. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2014. "Designing an index for assessing wind energy potential," SFB 649 Discussion Papers 2014-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Murthy, K.S.R. & Rahi, O.P., 2016. "Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India," Renewable Energy, Elsevier, vol. 99(C), pages 1137-1145.
    2. Shen, Zhiwei & Ritter, Matthias, 2015. "Forecasting volatility of wind power production," SFB 649 Discussion Papers 2015-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Matthias Ritter & Simone Pieralli & Martin Odening, 2017. "Neighborhood Effects in Wind Farm Performance: A Regression Approach," Energies, MDPI, vol. 10(3), pages 1-16, March.
    4. Ritter, Matthias & Deckert, Lars, 2015. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," SFB 649 Discussion Papers 2015-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Li, Jiale & Yu, Xiong (Bill), 2018. "Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis," Energy, Elsevier, vol. 147(C), pages 1092-1107.
    6. Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    7. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Levelized income loss as a metric of the adaptation of wind and energy storage to variable prices," Applied Energy, Elsevier, vol. 238(C), pages 1179-1191.
    8. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    9. Engelhorn, Thorsten & Müsgens, Felix, 2018. "How to estimate wind-turbine infeed with incomplete stock data: A general framework with an application to turbine-specific market values in Germany," Energy Economics, Elsevier, vol. 72(C), pages 542-557.
    10. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    11. Mohamed Elnaggar & Ezzaldeen Edwan & Matthias Ritter, 2017. "Wind Energy Potential of Gaza Using Small Wind Turbines: A Feasibility Study," Energies, MDPI, vol. 10(8), pages 1-13, August.
    12. Laudari, R. & Sapkota, B. & Banskota, K., 2018. "Validation of wind resource in 14 locations of Nepal," Renewable Energy, Elsevier, vol. 119(C), pages 777-786.
    13. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    14. Alina Wilke & Paul J.J. Welfens, 2020. "Urban Wind Energy Production Potential: New Opportunities," EIIW Discussion paper disbei287, Universitätsbibliothek Wuppertal, University Library.
    15. Melzer, Awdesch & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2017. "Pricing Green Financial Products," SFB 649 Discussion Papers 2017-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Ramirez Camargo, Luis & Gruber, Katharina & Nitsch, Felix, 2019. "Assessing variables of regional reanalysis data sets relevant for modelling small-scale renewable energy systems," Renewable Energy, Elsevier, vol. 133(C), pages 1468-1478.
    17. Tajeddin, Alireza & Fazelpour, Farivar, 2016. "Towards realistic design of wind dams: An innovative approach to enhance wind potential," Applied Energy, Elsevier, vol. 182(C), pages 282-298.
    18. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    19. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).

  8. Shen, Zhiwei & Odening, Martin & Okhrin, Ostap, 2013. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," SFB 649 Discussion Papers 2013-030, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Nguyen, Giang & Nguyen, Trung Thanh, 2020. "Exposure to weather shocks: A comparison between self-reported record and extreme weather data," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 117-138.
    2. Erwin Bulte & Rein Haagsma, 2021. "The Welfare Effects of Index-Based Livestock Insurance: Livestock Herding on Communal Lands," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 78(4), pages 587-613, April.
    3. Poeschel, Friedrich, 2012. "Assortative matching through signals," IAB-Discussion Paper 201215, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. Fabio Gaetano Santeramo, 2018. "Imperfect information and participation in insurance markets: evidence from Italy," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 78(2), pages 183-194, February.
    5. Fabio G Santeramo, 2019. "I Learn, You Learn, We Gain Experience in Crop Insurance Markets," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(2), pages 284-304, June.
    6. Yong Liu & A. Ford Ramsey, 2023. "Incorporating historical weather information in crop insurance rating," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(2), pages 546-575, March.
    7. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.

  9. Shen, Zhiwei & Odening, Martin & Okhrin, Ostap, 2013. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149431, Agricultural and Applied Economics Association.

    Cited by:

    1. Nguyen, Giang & Nguyen, Trung Thanh, 2020. "Exposure to weather shocks: A comparison between self-reported record and extreme weather data," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 117-138.
    2. Erwin Bulte & Rein Haagsma, 2021. "The Welfare Effects of Index-Based Livestock Insurance: Livestock Herding on Communal Lands," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 78(4), pages 587-613, April.
    3. Poeschel, Friedrich, 2012. "Assortative matching through signals," IAB-Discussion Paper 201215, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. Fabio Gaetano Santeramo, 2018. "Imperfect information and participation in insurance markets: evidence from Italy," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 78(2), pages 183-194, February.
    5. Fabio G Santeramo, 2019. "I Learn, You Learn, We Gain Experience in Crop Insurance Markets," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(2), pages 284-304, June.
    6. Yong Liu & A. Ford Ramsey, 2023. "Incorporating historical weather information in crop insurance rating," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(2), pages 546-575, March.
    7. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.

  10. Shen, Zhiwei & Odening, Martin, 2012. "Coping with Systemic Risk in Index-based Crop Insurance," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122555, European Association of Agricultural Economists.

    Cited by:

    1. Wu, Yang-Che, 2020. "Equilibrium in natural catastrophe insurance market under disaster-resistant technologies, financial innovations and government interventions," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 116-128.
    2. Cao, Xiaofeng & Okhrin, Ostap & Odening, Martin & Ritter, Matthias, 2014. "Modelling spatiotemporal variability of temperature," SFB 649 Discussion Papers 2014-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Lavorato, Mateus & Braga, Marcelo José, 2021. "On the Risk Efficiency of a Weather Index Insurance Product for the Brazilian Semi-Arid Region," 2021 Conference, August 17-31, 2021, Virtual 315193, International Association of Agricultural Economists.
    4. Li, Hong & Porth, Lysa & Tan, Ken Seng & Zhu, Wenjun, 2021. "Improved index insurance design and yield estimation using a dynamic factor forecasting approach," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 208-221.
    5. Yong Liu & Alan P. Ker, 2021. "Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 231-257, March.
    6. Martínez-Salgueiro, Andrea & Tarrazón-Rodón, María-Antonia, 2020. "Is diversification effective in reducing the systemic risk implied by a market for weather index-based insurance in Spain?," MPRA Paper 119924, University Library of Munich, Germany, revised 19 May 2021.
    7. Xiaotao Li & Jinzheng Ren & Beibei Niu & Haiping Wu, 2020. "Grain Area Yield Index Insurance Ratemaking Based on Time–Space Risk Adjustment in China," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    8. Shuoli Zhao & Chengyan Yue, 2020. "Risk preferences of commodity crop producers and specialty crop producers: An application of prospect theory," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 359-372, May.

Articles

  1. Zhiwei Shen & Martin Odening & Ostap Okhrin, 2016. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(2), pages 237-269.
    See citations under working paper version above.
  2. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    See citations under working paper version above.
  3. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    See citations under working paper version above.
  4. Maria Osipenko & Zhiwei Shen & Martin Odening, 2015. "Is there a demand for multi-year crop insurance?," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 75(1), pages 92-102, May.
    See citations under working paper version above.
  5. Martin Odening & Zhiwei Shen, 2014. "Challenges of insuring weather risk in agriculture," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 74(2), pages 188-199, July.

    Cited by:

    1. B. W. Mazviona, 2022. "Maize Index Insurance And Management Of Climate Change In A Developing Economy," Strategic decisions and risk management, Real Economy Publishing House, vol. 12(4).
    2. Martin Odening & Carsten Croonenbroeck & Rainer Kühl & Jörg Müller & Norbert Hirschauer & Oliver Mußhoff & Frank Offermann, 2018. "Extremwetterlage und Dürreschäden: Sind staatliche Hilfen für die Landwirtschaft erforderlich?," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(20), pages 03-15, October.
    3. A. Ford Ramsey & Barry K. Goodwin, 2019. "Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    4. Robert Dankiewicz & Olena Prokopchuk & Mykhaylo Malyovanyi, 2021. "Architectonics of Complex Modernization of Agricultural Insurance Market of Ukraine in Conditions of Transformation Processes," Oblik i finansi, Institute of Accounting and Finance, issue 2, pages 74-84, June.
    5. Abrego, Adriana & Guizar, Isai, 2017. "Resilience of Agricultural Microfinance Institutions to Rainfall Shocks," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258031, Agricultural and Applied Economics Association.

  6. Zhiwei Shen & Martin Odening, 2013. "Coping with systemic risk in index-based crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 44(1), pages 1-13, January.
    See citations under working paper version above.

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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 7 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-AGR: Agricultural Economics (5) 2012-05-15 2013-06-04 2014-04-29 2016-12-11 2017-01-29. Author is listed
  2. NEP-IAS: Insurance Economics (4) 2012-05-15 2013-06-04 2014-04-29 2016-12-11
  3. NEP-ENE: Energy Economics (2) 2015-01-31 2015-05-22
  4. NEP-DCM: Discrete Choice Models (1) 2014-04-29
  5. NEP-FOR: Forecasting (1) 2015-05-22
  6. NEP-RMG: Risk Management (1) 2015-05-22
  7. NEP-UPT: Utility Models and Prospect Theory (1) 2012-05-15

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