create a website

Application of time series models for heating degree day forecasting. (2020). Merve, Kuru ; Gulben, Calis.
In: Organization, Technology and Management in Construction.
RePEc:vrs:otamic:v:12:y:2020:i:1:p:2137-2146:n:8.

Full description at Econpapers || Download paper

Cited: 0

Citations received by this document

Cites: 36

References cited by this document

Cocites: 40

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

    This document has not been cited yet.

References

References cited by this document

  1. Özmen, A., Yılmaz, Y., & Weber, G. W. (2018). Natural gas consumption forecast with MARS and CMARS models for residential users. Energy Economics, 70, pp. 357–381. doi: 10.1016/j.eneco.2018.01.022.

  2. Amber, K. P., Ahmad, R., Aslam, M. W., Kousar, A., Usman, M., & Khan, M. S. (2018). Intelligent techniques for forecasting electricity consumption of buildings. Energy, 157, pp. 886–893. doi: 10.1016/j.energy.2018.05.155.

  3. Annual Electricity Report (2016). France.
    Paper not yet in RePEc: Add citation now
  4. Baldigara, T., & Koic, M. (2015). Modelling occupancy rates in croatian hotel industry. International Journal of Business Administration, 6(3), pp. 121–131. doi: 10.5430/ijba.v6n3p121.
    Paper not yet in RePEc: Add citation now
  5. Bolattürk, A. (2008). Optimum insulation thicknesses for building walls with respect to cooling and heating degree-hours in the warmest zone of Turkey. Building and Environment, 43(6), pp. 1055–1064. doi: 10.1016/j.buildenv.2007.02.014.
    Paper not yet in RePEc: Add citation now
  6. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control, 5th edn. Wiley.
    Paper not yet in RePEc: Add citation now
  7. Calis, G., Atalay, S. D., Kuru, M., & Mutlu, N. (2017). Forecasting occupancy for demand driven HVAC operations using time series analysis. Journal of Asian Architecture and Building Engineering, 16(3), pp. 655–660. doi: 10.3130/jaabe.16.655.
    Paper not yet in RePEc: Add citation now
  8. Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, pp. 198–213. doi: 10.1016/j.enbuild.2016.06.089.
    Paper not yet in RePEc: Add citation now
  9. D’Amico, A., Panno, D., Giuseppina, C., & Ferrari, S. (2019). Building energy demand assessment through heating degree days: The importance of a climatic dataset. Applied Energy, 242(December 2018), pp. 1285–1306. doi: 10.1016/J.APENERGY.2019.03.167.

  10. Dombayci, Ö. A. (2007). The environmental impact of optimum insulation thickness for external walls of buildings. Building and Environment, 42(11), pp. 3855–3859. doi: 10.1016/j.buildenv.2006.10.054.
    Paper not yet in RePEc: Add citation now
  11. Durmayaz, A., & Kadioglu, M. (2003). Heating energy requirements and fuel consumptions in the biggest city centers of Turkey. Energy Conversion and Management, 44(7), pp. 1177–1192. doi: 10.1016/S0196-8904(02)00097-3.
    Paper not yet in RePEc: Add citation now
  12. Durmayaz, A., Kadioglu, M., & En, Z. (2000). An application of the degree-hours method to estimate the residential heating energy requirement and fuel consumption in Istanbul. Energy, 25(12), pp. 1245–1256. doi: 10.1016/S0360-5442(00)00040-2.

  13. Elizbarashvili, M., Chartolani, G., & Khardziani, T. (2018). Variations and trends of heating and cooling degree-days in Georgia for 1961–1990 year period. Annals of Agrarian Science, 16(2), pp. 152–159. doi: 10.1016/j.aasci.2018.03.004.
    Paper not yet in RePEc: Add citation now
  14. EU Strategy for Heating and Cooling. (2019). Journal of Chemical Information and Modeling. Available at: https://ec.europa.eu/energy/en/topics/energy-efficiency/heating-and-cooling.
    Paper not yet in RePEc: Add citation now
  15. Fan, J. L., Hu, J. W., & Zhang, X. (2019). Impacts of climate change on electricity demand in China: An empirical estimation based on panel data. Energy, 170, pp. 880–888. doi: 10.1016/j.energy.2018.12.044.

  16. Idchabani, R., Garoum, M., & Khaldoun, A. (2015). Analysis and mapping of the heating and cooling degree-days for Morocco at variable base temperatures. International Journal of Ambient Energy, 36(4), pp. 190–198. doi: 10.1080/01430750.2013.842497.
    Paper not yet in RePEc: Add citation now
  17. IPCC (2007). In Climate Change 2007: Synthesis Report. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden P.J. & Hanson C.E. (eds). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
    Paper not yet in RePEc: Add citation now
  18. Kam, H. J., Sung, J. O., & Park, R. W. (2010). Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthcare Informatics Research, 16(3), pp. 158–165. doi: 10.4258/hir.2010.16.3.158.
    Paper not yet in RePEc: Add citation now
  19. Kohler, M., Blond, N., & Clappier, A. (2016). A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France). Applied Energy, 184, pp. 40–54. doi: 10.1016/j.apenergy.2016.09.075.

  20. Kurekci, N. A. (2016). Determination of optimum insulation thickness for building walls by using heating and cooling degree-day values of all Turkey’s provincial centers. Energy and Buildings, 118(825), pp. 197–213. doi: 10.1016/j.enbuild.2016.03.004.
    Paper not yet in RePEc: Add citation now
  21. Kuru, M., & Calis, G. (2019). Forecasting heating degree days for energy demand modeling. pp. 8–13.
    Paper not yet in RePEc: Add citation now
  22. Lewis, C. D. (1982). Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. Butterworth Scientific, London, Boston.
    Paper not yet in RePEc: Add citation now
  23. Li, X. X. (2018). Linking residential electricity consumption and outdoor climate in a tropical city. Energy, 157, pp. 734–743. doi: 10.1016/j.energy.2018.05.192.

  24. Meng, Q., & Mourshed, M. (2017). Degree-day based non-domestic building energy analytics and modelling should use building and type specific base temperatures. Energy and Buildings, 155, pp. 260–268. doi: 10.1016/j.enbuild.2017.09.034.
    Paper not yet in RePEc: Add citation now
  25. Mourshed, M. (2012). Relationship between annual mean temperature and degree-days. Energy and Buildings, 54, pp. 418–425. doi: 10.1016/j.enbuild.2012.07.024.
    Paper not yet in RePEc: Add citation now
  26. NCSS data analysis. (2019). Time Series and Forecasting Methods in NCSS. Available at https:www.ncss.com/software/ncss/time-series-and-forecasting-in-ncss/.
    Paper not yet in RePEc: Add citation now
  27. Neto, A. H., & Fiorelli, F. A. S. (2008). Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and Buildings, 40(12), pp. 2169–2176. doi: 10.1016/j.enbuild.2008.06.013.
    Paper not yet in RePEc: Add citation now
  28. OrtizBeviá, M. J., Sánchez-López, G., Alvarez-Garcìa, F. J., & Ruizdeelvira, A. (2012). Evolution of heating and cooling degree-days in Spain: Trends and interannual variability. Global and Planetary Change, 92–93, pp. 236–247. doi: 10.1016/j.gloplacha.2012.05.023.
    Paper not yet in RePEc: Add citation now
  29. Sarak, H., & Satman, A. (2003). The degree-day method to estimate the residential heating natural gas consumption in Turkey: A case study. Energy, 28(9), pp. 929–939. doi: 10.1016/S0360-5442(03)00035-5.

  30. Statistical Office of the European Union. (2019). Energy Statistics - Cooling and Heating Degree Days. Available at ec.europa.eu/eurostat/cache/metadata/fr/nrg_chdd_esms.htm.
    Paper not yet in RePEc: Add citation now
  31. The Statistical Office of the European Union. (2019). Available at https://ec.europa.eu/eurostat/web/products-datasets/product?code=nrg_chdd_m.
    Paper not yet in RePEc: Add citation now
  32. Trigaux, D., Oosterbosch, B., De Troyer, F., Allacker, K. (2017). A design tool to assess the heating energy demand and the associated financial and environmental impact in neighbourhoods. Energy and Buildings, 152, pp. 516–523. doi: 10.1016/j.enbuild.2017.07.057.
    Paper not yet in RePEc: Add citation now
  33. Weatheronline.co.uk. (2019). Heating Degree Days and Cooling Degree Days Indices. Available at www.weatheronline.co.uk/faq/hdd_cdd.html.
    Paper not yet in RePEc: Add citation now
  34. Witt, S. F., & Witt, C. A. (1992). Modeling and forecasting demand in tourism. Academic Press Ltd. Butterworth-Heinemann, London.
    Paper not yet in RePEc: Add citation now
  35. Wu, J., Reddy, T. A., & Claridge, D. (1992). Statistical Modeling of Daily Energy Consumption in Commercial Buildings Using Multiple Regression and Principal Component Analysis. In: Proceedings of the Eighth Symposium on Improving Building Systems in Hot and Humid Climates. Dalla, Texas, pp. 155–164. doi: 10.20595/jjbf.19.0_3.
    Paper not yet in RePEc: Add citation now
  36. Yu, J., Yang, J., Tian, L., & Liao, D. (2009). A study on optimum insulation thicknesses of external walls in hot summer and cold winter zone of China. Applied Energy, 86(11), pp. 2520–2529. doi: 10.1016/j.apenergy.2009.03.010.

Cocites

Documents in RePEc which have cited the same bibliography

  1. Dynamic hydrogen demand forecasting using hybrid time series models: Insights for renewable energy systems. (2025). Nikseresht, Ali.
    In: Renewable Energy.
    RePEc:eee:renene:v:244:y:2025:i:c:s0960148125003994.

    Full description at Econpapers || Download paper

  2. A study of asset and liability management applied to Brazilian pension funds. (2025). , Joao ; Falcao, Rodrigo ; Bernardino, Wilton ; Alves, Jos Jonas ; de Souza, Filipe Costa ; Ospina, Raydonal.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:322:y:2025:i:3:p:1059-1076.

    Full description at Econpapers || Download paper

  3. Forecasting Residential Heating and Electricity Demand with Scalable, High-Resolution, Open-Source Models. (2025). Drouin, Cailinn ; Lee, Stephen J.
    In: Papers.
    RePEc:arx:papers:2505.22873.

    Full description at Econpapers || Download paper

  4. Long-horizon predictions of credit default with inconsistent customers. (2024). Zhou, Ying ; Jin, Peng ; Chi, Guotai ; Dong, Bingjie.
    In: Technological Forecasting and Social Change.
    RePEc:eee:tefoso:v:198:y:2024:i:c:s0040162523006935.

    Full description at Econpapers || Download paper

  5. Real-time capacity cost obligations design in high-renewables energy markets. (2024). Fang, Xichen ; Liu, Shuangquan ; Chen, Qixin ; Zheng, Kedi ; Guo, Hongye.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:191:y:2024:i:c:s1364032123009619.

    Full description at Econpapers || Download paper

  6. Modeling sustainable bioethanol supply chain in Australia: A system dynamics approach. (2024). Taheri, Nima ; Pishvaee, Mir Saman ; Jahani, Hamed.
    In: Renewable Energy.
    RePEc:eee:renene:v:227:y:2024:i:c:s0960148124005469.

    Full description at Econpapers || Download paper

  7. A coordinated approach for a three-echelon solar-wind energy supply with government intervention. (2024). Matinfard, Sahar ; Yaghoubi, Saeed ; Manouchehrabadi, Maedeh Kharaji.
    In: Utilities Policy.
    RePEc:eee:juipol:v:86:y:2024:i:c:s0957178723002035.

    Full description at Econpapers || Download paper

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

    Full description at Econpapers || Download paper

  9. The black box of natural gas market: Past, present, and future. (2024). Oriani, Marco Ercole ; Goodell, John W ; Paltrinieri, Andrea ; Palma, Alessia.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:94:y:2024:i:c:s1057521924001923.

    Full description at Econpapers || Download paper

  10. Forecasting Petroleum Products Consumption in the Chadian Road Transport Sector using Optimised Grey Models. (2024). Ewodo-Amougou, Marcel Rodrigue ; Stevy, Jean Marie ; Tamba, Jean Gaston ; Sapnken, Flavian Emmanuel ; Jeutsa, Aubin Kinfack ; Acyl, Ahmat Khazali.
    In: International Journal of Energy Economics and Policy.
    RePEc:eco:journ2:2024-01-65.

    Full description at Econpapers || Download paper

  11. Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market. (2023). Zinchenko, Yuriy ; Weber, Gerhard-Wilhelm ; Ozmen, Aye.
    In: Annals of Operations Research.
    RePEc:spr:annopr:v:324:y:2023:i:1:d:10.1007_s10479-022-04993-w.

    Full description at Econpapers || Download paper

  12. Sparse regression modeling for short- and long‐term natural gas demand prediction. (2023). Ozmen, Aye.
    In: Annals of Operations Research.
    RePEc:spr:annopr:v:322:y:2023:i:2:d:10.1007_s10479-021-04089-x.

    Full description at Econpapers || Download paper

  13. A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling. (2023). Castello, Oleksandr ; Resta, Marina.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:12:p:4746-:d:1172227.

    Full description at Econpapers || Download paper

  14. A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed. (2023). Xu, Xiaoxiao ; Sun, Qiuwen ; Yu, Hao.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:182:y:2023:i:c:s1364032123002538.

    Full description at Econpapers || Download paper

  15. Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland. (2022). Cielik, Tomasz ; Narloch, Piotr ; Kogut, Krzysztof ; Szurlej, Adam.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:4:p:1393-:d:749334.

    Full description at Econpapers || Download paper

  16. Multi-Step Ahead Natural Gas Consumption Forecasting Based on a Hybrid Model: Case Studies in The Netherlands and the United Kingdom. (2022). Ma, Xin ; Zhang, Peng.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:19:p:7437-:d:937991.

    Full description at Econpapers || Download paper

  17. A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production. (2022). Akbal, Yildirim ; Unlu, Kamil Demirberk.
    In: Renewable Energy.
    RePEc:eee:renene:v:200:y:2022:i:c:p:832-844.

    Full description at Econpapers || Download paper

  18. Predicting the production and consumption of natural gas in China by using a new grey forecasting method. (2022). Sun, Yanrui ; Lao, Tongfei.
    In: Mathematics and Computers in Simulation (MATCOM).
    RePEc:eee:matcom:v:202:y:2022:i:c:p:295-315.

    Full description at Econpapers || Download paper

  19. A hybrid deep learning framework for predicting daily natural gas consumption. (2022). Shahzad, Khurram ; Kleme, Jii Jaromir ; Zheng, Jianqin ; Du, Jian ; Wang, Bohong ; Ali, Arshid Mahmood ; Liang, Yongtu ; Lu, Xinyi ; Liao, QI ; Varbanov, Petar Sabev ; Rashid, Muhammad Imtiaz.
    In: Energy.
    RePEc:eee:energy:v:257:y:2022:i:c:s0360544222015924.

    Full description at Econpapers || Download paper

  20. Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance. (2022). Wei, Nan ; Yin, Lihua ; Liu, Jinyuan ; Zeng, Fanhua ; Huang, Yuanyuan.
    In: Energy.
    RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221023380.

    Full description at Econpapers || Download paper

  21. Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?. (2022). Li, Xiafei ; Guo, Qiang ; Luo, Keyu.
    In: Energy Economics.
    RePEc:eee:eneeco:v:109:y:2022:i:c:s0140988322001244.

    Full description at Econpapers || Download paper

  22. Forecasting natural gas consumption using Bagging and modified regularization techniques. (2022). de Menezes, Lilian M ; Cyrino, Fernando Luiz ; Meira, Erick.
    In: Energy Economics.
    RePEc:eee:eneeco:v:106:y:2022:i:c:s0140988321006034.

    Full description at Econpapers || Download paper

  23. Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?. (2022). Zhao, Zhongchao ; Ding, Lili ; Wang, Lei.
    In: Applied Energy.
    RePEc:eee:appene:v:312:y:2022:i:c:s0306261922002100.

    Full description at Econpapers || Download paper

  24. Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings. (2021). Elhashmi, Rodwan ; Hallinan, Kevin P ; Alanezi, Abdulrahman.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:1:p:187-:d:473524.

    Full description at Econpapers || Download paper

  25. Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map. (2021). Soltanisarvestani, A ; Safavi, A A.
    In: Utilities Policy.
    RePEc:eee:juipol:v:72:y:2021:i:c:s0957178721000850.

    Full description at Econpapers || Download paper

  26. Forecast of natural gas consumption in the Asia-Pacific region using a fractional-order incomplete gamma grey model. (2021). Xiong, Pingping ; Li, Kailing ; Wang, Junjie ; Shu, Hui.
    In: Energy.
    RePEc:eee:energy:v:237:y:2021:i:c:s0360544221017813.

    Full description at Econpapers || Download paper

  27. Forecasting the daily natural gas consumption with an accurate white-box model. (2021). Wei, Nan ; Yin, Lihua ; Zeng, Fanhua ; Chan, Christine ; Li, Chao.
    In: Energy.
    RePEc:eee:energy:v:232:y:2021:i:c:s0360544221012846.

    Full description at Econpapers || Download paper

  28. Prediction of transportation energy demand: Multivariate Adaptive Regression Splines. (2021). Odur, Muhammed Yasin ; Sahraei, Mohammad Ali ; Duman, Hakan ; Eyduran, Ecevit.
    In: Energy.
    RePEc:eee:energy:v:224:y:2021:i:c:s036054422100339x.

    Full description at Econpapers || Download paper

  29. Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model. (2021). Li, Xingmei ; Zheng, Haofeng ; Yang, Fei.
    In: Applied Energy.
    RePEc:eee:appene:v:303:y:2021:i:c:s0306261921009910.

    Full description at Econpapers || Download paper

  30. Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm. (2021). Bampos, Zafeirios N ; Keranidis, Stratos D ; Biskas, Pandelis N ; Chatzis, Georgios V ; Tsoumalis, Georgios I.
    In: Applied Energy.
    RePEc:eee:appene:v:299:y:2021:i:c:s0306261921006760.

    Full description at Econpapers || Download paper

  31. Application of time series models for heating degree day forecasting. (2020). Merve, Kuru ; Gulben, Calis.
    In: Organization, Technology and Management in Construction.
    RePEc:vrs:otamic:v:12:y:2020:i:1:p:2137-2146:n:8.

    Full description at Econpapers || Download paper

  32. Knowledge accelerator by transversal competences and multivariate adaptive regression splines. (2020). Spychaa, Magorzata ; Graczyk-Kucharska, Magdalena ; Szafraski, Maciej ; Weber, Gerhard Wilhelm ; Ozmen, Ayse ; Goliki, Marek.
    In: Central European Journal of Operations Research.
    RePEc:spr:cejnor:v:28:y:2020:i:2:d:10.1007_s10100-019-00636-x.

    Full description at Econpapers || Download paper

  33. Analysis of top kayakers’ training-intensity distribution and physiological adaptation based on structural modelling. (2020). Dadelo, Stanislav ; Dadeliene, Ruta ; Pozniak, Natalija ; Sakalauskas, Leonidas.
    In: Annals of Operations Research.
    RePEc:spr:annopr:v:289:y:2020:i:2:d:10.1007_s10479-020-03560-5.

    Full description at Econpapers || Download paper

  34. US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model. (2020). Ma, Xin ; Azimi, Mohammadamin ; Lu, Hongfang.
    In: Energy.
    RePEc:eee:energy:v:194:y:2020:i:c:s0360544220300128.

    Full description at Econpapers || Download paper

  35. Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks. (2019). Laib, Oussama ; Khadir, Mohamed Tarek ; Mihaylova, Lyudmila.
    In: Energy.
    RePEc:eee:energy:v:177:y:2019:i:c:p:530-542.

    Full description at Econpapers || Download paper

  36. Impact of changes in crude oil trade network patterns on national economy. (2019). Gao, Xiangyun ; Sun, Qingru ; Zhou, Jinsheng ; Zheng, Huiling ; Liu, Donghui.
    In: Energy Economics.
    RePEc:eee:eneeco:v:84:y:2019:i:c:s0140988319302713.

    Full description at Econpapers || Download paper

  37. Using machine learning tools for forecasting natural gas consumption in the province of Istanbul. (2019). Zaim, Selim ; Ozuyar, Pinar Gokcin ; Beyca, Omer Faruk ; Tatoglu, Ekrem ; Ervural, Beyzanur Cayir.
    In: Energy Economics.
    RePEc:eee:eneeco:v:80:y:2019:i:c:p:937-949.

    Full description at Econpapers || Download paper

  38. Daily natural gas consumption forecasting via the application of a novel hybrid model. (2019). Li, Yang ; Peng, Xiaolong ; Wei, Nan ; Zeng, Fanhua.
    In: Applied Energy.
    RePEc:eee:appene:v:250:y:2019:i:c:p:358-368.

    Full description at Econpapers || Download paper

  39. The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China. (2019). Li, Wei ; Lu, Can.
    In: Applied Energy.
    RePEc:eee:appene:v:235:y:2019:i:c:p:685-698.

    Full description at Econpapers || Download paper

  40. Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting. (2018). Hong, Wei-Chiang ; Fan, Guo-Feng ; Wang, AN.
    In: Energies.
    RePEc:gam:jeners:v:11:y:2018:i:7:p:1625-:d:153721.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-10-06 01:21:10 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.