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Katarzyna Maciejowska

Personal Details

First Name:Katarzyna
Middle Name:
Last Name:Maciejowska
Suffix:
RePEc Short-ID:pma1510
+48607924451
Terminal Degree:2010 Department of Economics; European University Institute (from RePEc Genealogy)

Affiliation

(90%) Wydział Informatyki i Zarządzania
Politechnika Wrocławska

Wrocław, Poland
http://wiz.pwr.wroc.pl/

:


RePEc:edi:iopwrpl (more details at EDIRC)

(10%) Center for Economic Research and Graduate Education and Economics Institute (CERGE-EI)

Praha, Czech Republic
http://www.cerge-ei.cz/

: (+420) 224 005 123
(+420) 224 005 333
P.O. Box 882, Politickych veznu 7, 111 21 Praha 1
RePEc:edi:eiacacz (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Katarzyna Maciejowska & Arkadiusz Jedrzejewski & Anna Kowalska-Pyzalska & Rafal Weron, 2016. "Impact of social interactions on demand curves for innovative products," HSC Research Reports HSC/16/04, Hugo Steinhaus Center, Wroclaw University of Technology.
  2. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.
  3. Katarzyna Maciejowska & Arkadiusz Jedrzejewski & Anna Kowalska-Pyzalska & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Two faces of word-of-mouth: Understanding the impact of social interactions on demand curves for innovative products," HSC Research Reports HSC/15/09, Hugo Steinhaus Center, Wroclaw University of Technology.
  4. Katarzyna Maciejowska & Jakub Nowotarski, 2015. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," HSC Research Reports HSC/15/06, Hugo Steinhaus Center, Wroclaw University of Technology.
  5. Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," HSC Research Reports HSC/14/09, Hugo Steinhaus Center, Wroclaw University of Technology.
  6. Tao Hong & Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts," HSC Research Reports HSC/14/10, Hugo Steinhaus Center, Wroclaw University of Technology.
  7. Katarzyna Maciejowska, 2014. "Fundamental and speculative shocks, what drives electricity prices?," HSC Research Reports HSC/14/05, Hugo Steinhaus Center, Wroclaw University of Technology.
  8. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach," HSC Research Reports HSC/14/01, Hugo Steinhaus Center, Wroclaw University of Technology.
  9. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Modeling consumer opinions towards dynamic pricing: An agent-based approach," HSC Research Reports HSC/14/06, Hugo Steinhaus Center, Wroclaw University of Technology.
  10. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market," HSC Research Reports HSC/13/01, Hugo Steinhaus Center, Wroclaw University of Technology, revised 15 Apr 2013.
  11. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2013. "Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs," HSC Research Reports HSC/13/05, Hugo Steinhaus Center, Wroclaw University of Technology.
  12. Maciejowska, Katarzyna, 2013. "Assessing the number of components in a normal mixture: an alternative approach," MPRA Paper 50303, University Library of Munich, Germany.
  13. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Karol Suszczynski & Rafal Weron, 2013. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," HSC Research Reports HSC/13/10, Hugo Steinhaus Center, Wroclaw University of Technology.
  14. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships," HSC Research Reports HSC/13/11, Hugo Steinhaus Center, Wroclaw University of Technology.
  15. Katarzyna Maciejowska, 2010. "Common factors in nonstationary panel data with a deterministic trend - estimation and distribution theory," Economics Working Papers ECO2010/28, European University Institute.
  16. Katarzyna Maciejowska, 2010. "Estimation methods comparison of SVAR model with the mixture of two normal distributions - Monte Carlo analysis," Economics Working Papers ECO2010/27, European University Institute.
  17. Markku Lanne & Helmut Luetkepohl & Katarzyna Maciejowska, 2009. "Structural Vector Autoregressions with Markov Switching," Economics Working Papers ECO2009/06, European University Institute.

Articles

  1. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
  2. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
  3. Katarzyna Maciejowska & Rafał Weron, 2015. "Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships," Computational Statistics, Springer, vol. 30(3), pages 805-819, September.
  4. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
  5. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
  6. Katarzyna Maciejowska, 2010. "Estimation Methods Comparison of SVAR Models with a Mixture of Two Normal Distributions," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 2(4), pages 279-314, September.

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. Katarzyna Maciejowska & Arkadiusz Jedrzejewski & Anna Kowalska-Pyzalska & Rafal Weron, 2016. "Impact of social interactions on demand curves for innovative products," HSC Research Reports HSC/16/04, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Paul R. Nail & Katarzyna Sznajd-Weron, 2016. "The diamond model of social response within an agent-based approach," HSC Research Reports HSC/16/02, Hugo Steinhaus Center, Wroclaw University of Technology.

  2. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    2. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    4. Rick Steinert & Florian Ziel, 2018. "Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures," Papers 1801.10583, arXiv.org.

  3. Katarzyna Maciejowska & Jakub Nowotarski, 2015. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," HSC Research Reports HSC/15/06, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    2. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    3. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-22, August.
    4. Bartosz Uniejewski & Grzegorz Marcjasz & Rafal Weron, 2017. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting," HSC Research Reports HSC/17/02, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, Open Access Journal, vol. 11(5), pages 1-25, April.
    6. Jakub Nowotarski & Rafal Weron, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," HSC Research Reports HSC/16/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    8. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    9. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.

  4. Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," HSC Research Reports HSC/14/09, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    2. Agustín A. Sánchez de la Nieta & Virginia González & Javier Contreras, 2016. "Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming," Energies, MDPI, Open Access Journal, vol. 9(12), pages 1-19, December.
    3. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    4. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-22, August.
    5. Bartosz Uniejewski & Grzegorz Marcjasz & Rafal Weron, 2017. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting," HSC Research Reports HSC/17/02, Hugo Steinhaus Center, Wroclaw University of Technology.
    6. Bidong Liu & Jakub Nowotarski & Tao Hong & Rafal Weron, 2015. "Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts," HSC Research Reports HSC/15/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Tryggvi Jónsson & Pierre Pinson & Henrik Madsen & Henrik Aalborg Nielsen, 2014. "Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression," Energies, MDPI, Open Access Journal, vol. 7(9), pages 1-25, August.
    8. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    9. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
    10. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    11. Mosquera-López, Stephanía & Uribe, Jorge M. & Manotas-Duque, Diego Fernando, 2017. "Nonlinear empirical pricing in electricity markets using fundamental weather factors," Energy, Elsevier, vol. 139(C), pages 594-605.
    12. Jakub Nowotarski & Eran Raviv & Stefan Trueck & Rafal Weron, 2013. "An empirical comparison of alternate schemes for combining electricity spot price forecasts," HSC Research Reports HSC/13/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    13. Vijay, Avinash & Fouquet, Nicolas & Staffell, Iain & Hawkes, Adam, 2017. "The value of electricity and reserve services in low carbon electricity systems," Applied Energy, Elsevier, vol. 201(C), pages 111-123.
    14. Shao, Zhen & Yang, ShanLin & Gao, Fei & Zhou, KaiLe & Lin, Peng, 2017. "A new electricity price prediction strategy using mutual information-based SVM-RFE classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 330-341.
    15. Mawuli Segnon & Chi Keung Lau & Bernd Wilfling & Rangan Gupta, 2017. "Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data," CQE Working Papers 6117, Center for Quantitative Economics (CQE), University of Muenster.
    16. Jakub Nowotarski & Rafal Weron, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," HSC Research Reports HSC/16/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    17. Luigi Grossi & Fany Nan, 2017. "Forecasting electricity prices through robust nonlinear models," Working Papers 06/2017, University of Verona, Department of Economics.
    18. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    19. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    20. Xu, Bin & Lin, Boqiang, 2016. "A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?," Energy Policy, Elsevier, vol. 98(C), pages 328-342.
    21. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    22. Jakub Nowotarski & Rafał Weron, 2015. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," Computational Statistics, Springer, vol. 30(3), pages 791-803, September.
    23. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    24. Antonio Bello & Derek Bunn & Javier Reneses & Antonio Muñoz, 2016. "Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-15, November.
    25. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    26. Florian Ziel & Rick Steinert, 2017. "Probabilistic Mid- and Long-Term Electricity Price Forecasting," Papers 1703.10806, arXiv.org, revised May 2018.

  5. Tao Hong & Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts," HSC Research Reports HSC/14/10, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).

  6. Katarzyna Maciejowska, 2014. "Fundamental and speculative shocks, what drives electricity prices?," HSC Research Reports HSC/14/05, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Rafal Weron & Michal Zator, 2013. "Revisiting the relationship between spot and futures prices in the Nord Pool electricity market," HSC Research Reports HSC/13/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Paschen, Marius, 2016. "Dynamic analysis of the German day-ahead electricity spot market," Energy Economics, Elsevier, vol. 59(C), pages 118-128.
    3. Rafal Weron & Michal Zator, 2014. "A note on using the Hodrick-Prescott filter in electricity markets," HSC Research Reports HSC/14/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. Di Cosmo, Valeria & Malaguzzi Valeri, Laura, 2017. "Wind, Storage, Interconnection and the Cost of Electricity Generation," ESP: Energy Scenarios and Policy 253733, Fondazione Eni Enrico Mattei (FEEM).
    5. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    6. Jakub Nowotarski & Rafal Weron, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," HSC Research Reports HSC/16/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.

  7. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Modeling consumer opinions towards dynamic pricing: An agent-based approach," HSC Research Reports HSC/14/06, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Karol Suszczynski & Rafal Weron, 2013. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," HSC Research Reports HSC/13/10, Hugo Steinhaus Center, Wroclaw University of Technology.

  8. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market," HSC Research Reports HSC/13/01, Hugo Steinhaus Center, Wroclaw University of Technology, revised 15 Apr 2013.

    Cited by:

    1. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    2. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    3. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships," HSC Research Reports HSC/13/11, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. Jakub Nowotarski & Rafał Weron, 2015. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," Computational Statistics, Springer, vol. 30(3), pages 791-803, September.
    5. Jakub Nowotarski & Rafal Weron, 2014. "Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices," HSC Research Reports HSC/14/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    6. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.

  9. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2013. "Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs," HSC Research Reports HSC/13/05, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Karol Suszczynski & Rafal Weron, 2013. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," HSC Research Reports HSC/13/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. McCoy, Daire & Lyons, Sean, 2014. "The diffusion of electric vehicles: An agent-based microsimulation," MPRA Paper 54560, University Library of Munich, Germany.

  10. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Karol Suszczynski & Rafal Weron, 2013. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," HSC Research Reports HSC/13/10, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Katarzyna Maciejowska & Arkadiusz Jedrzejewski & Anna Kowalska-Pyzalska & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Two faces of word-of-mouth: Understanding the impact of social interactions on demand curves for innovative products," HSC Research Reports HSC/15/09, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Katarzyna Byrka & Arkadiusz Jedrzejewski & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Difficulty is critical: Psychological factors in modeling diffusion of green products and practices," HSC Research Reports HSC/15/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    3. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach," HSC Research Reports HSC/14/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. Anna Kowalska-Pyzalska, 2015. "Social acceptance of green energy and dynamic electricity tariffs - a short review," HSC Research Reports HSC/15/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. Alexopoulos, Thomas A., 2017. "The growing importance of natural gas as a predictor for retail electricity prices in US," Energy, Elsevier, vol. 137(C), pages 219-233.
    6. T. BALINT & F. LAMPERTI & A. Mandel & A. Roventini Author-Workplace-Name : OFCE-Sciences Pp and SKEMA Businees School & A. Sapio Author-Workplace-Name : Parathenope Universtiy of Naples, 2016. "Complexity and the Economics of Climate Change : a survey and look forward," Documents de Travail de l'OFCE 2016-23, Observatoire Francais des Conjonctures Economiques (OFCE).
    7. Niamir, Leila & Filatova, Tatiana & Voinov, Alexey & Bressers, Hans, 2018. "Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes," Energy Policy, Elsevier, vol. 118(C), pages 325-345.
    8. Weron, Tomasz & Kowalska-Pyzalska, Anna & Weron, Rafał, 2018. "The role of educational trainings in the diffusion of smart metering platforms: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 591-600.
    9. Kowalska-Pyzalska, Anna, 2018. "What makes consumers adopt to innovative energy services in the energy market? A review of incentives and barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3570-3581.
    10. Yalcintas, Melek & Hagen, William T. & Kaya, Abidin, 2015. "Time-based electricity pricing for large-volume customers: A comparison of two buildings under tariff alternatives," Utilities Policy, Elsevier, vol. 37(C), pages 58-68.
    11. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    12. Anna Kowalska-Pyzalska, 2016. "What makes consumers adopt to innovative energy services in the energy market?," HSC Research Reports HSC/16/09, Hugo Steinhaus Center, Wroclaw University of Technology.
    13. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Modeling consumer opinions towards dynamic pricing: An agent-based approach," HSC Research Reports HSC/14/06, Hugo Steinhaus Center, Wroclaw University of Technology.
    14. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    15. Rafik Nafkha & Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques," Energies, MDPI, Open Access Journal, vol. 11(3), pages 1-17, February.
    16. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    17. Tomas Balint & Francesco Lamperti & Antoine Mandel & Mauro Napoletano & Andrea Roventini & Sandro Sapio, 2017. "Complexity and the economics of climate change : a survey and a look foreward," Sciences Po publications info:hdl:2441/1nlv566svi8, Sciences Po.
    18. Cl'emence Alasseur & Ivar Ekeland & Romuald Elie & Nicol'as Hern'andez Santib'a~nez & Dylan Possamai, 2017. "An adverse selection approach to power pricing," Papers 1706.01934, arXiv.org, revised Jun 2018.

  11. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships," HSC Research Reports HSC/13/11, Hugo Steinhaus Center, Wroclaw University of Technology.

    Cited by:

    1. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    2. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    4. García-Martos, Carolina & Bastos, Guadalupe & Alonso Fernández, Andrés Modesto, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-21, July.
    6. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.
    7. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    8. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.

  12. Katarzyna Maciejowska, 2010. "Common factors in nonstationary panel data with a deterministic trend - estimation and distribution theory," Economics Working Papers ECO2010/28, European University Institute.

    Cited by:

    1. Matteo Barigozzi & Lorenzo Trapani, 2018. "Determining the dimension of factor structures in non-stationary large datasets," Papers 1806.03647, arXiv.org.

  13. Katarzyna Maciejowska, 2010. "Estimation methods comparison of SVAR model with the mixture of two normal distributions - Monte Carlo analysis," Economics Working Papers ECO2010/27, European University Institute.

    Cited by:

    1. Guarda, Paolo & Rouabah, Abdelaziz & Theal, John, 2012. "An MVAR framework to capture extreme events in macro-prudential stress tests," Working Paper Series 1464, European Central Bank.
    2. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).

  14. Markku Lanne & Helmut Luetkepohl & Katarzyna Maciejowska, 2009. "Structural Vector Autoregressions with Markov Switching," Economics Working Papers ECO2009/06, European University Institute.

    Cited by:

    1. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.
    2. Wenjuan Chen & Anton Velinov, 2012. "Do Japanese Stock Prices Reflect Macro Fundamentals?," SFB 649 Discussion Papers SFB649DP2012-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Helmut Herwartz & Martin Plödt, 2016. "Simulation Evidence on Theory-based and Statistical Identification under Volatility Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 94-112, February.
    4. Sun, Hang & Bos, Jaap W.B. & Li, Zhuo, 2017. "In the Nick of Time: A Heteroskedastic SVAR Model and Its Application to the Crude Oil Futures Market," Research Memorandum 019, Maastricht University, Graduate School of Business and Economics (GSBE).
    5. Anton Velinov, 2013. "Can Stock Price Fundamentals Properly be Captured?: Using Markov Switching in Heteroskedasticity Models to Test Identification Schemes," Discussion Papers of DIW Berlin 1350, DIW Berlin, German Institute for Economic Research.
    6. Helmut Lütkepohl & Thore Schlaak, 2017. "Choosing between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis," Discussion Papers of DIW Berlin 1672, DIW Berlin, German Institute for Economic Research.
    7. Emanuele BACCHIOCCHI, 2015. "On the Identification of Interdependence and Contagion of Financial Crises," Departmental Working Papers 2015-12, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    8. Camacho, Maximo & Pérez-Quirós, Gabriel, 2013. "Commodity prices and the business cycle in Latin America: Living and dying by commodities?," CEPR Discussion Papers 9367, C.E.P.R. Discussion Papers.
    9. Jan Pablo Burgard & Matthias Neuenkirch & Matthias Nöckel, 2018. "State-Dependent Transmission of Monetary Policy in the Euro Area," CESifo Working Paper Series 7074, CESifo Group Munich.
    10. Boris Blagov & Michael Funke & Richhild Moessner, 2015. "Modelling the time-variation in euro area lending spreads," BIS Working Papers 526, Bank for International Settlements.
    11. Emanuele BACCHIOCCHI, 2011. "Identification through heteroskedasticity: a likelihood-based approach," Departmental Working Papers 2011-19, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    12. Kohonen, Anssi, 2012. "On detection of volatility spillovers in simultaneously open stock markets," MPRA Paper 37504, University Library of Munich, Germany.
    13. Emanuele Bacchiocchi, 2017. "On the Identification of Interdependence and Contagion of Financial Crises," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(6), pages 1148-1175, December.
    14. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with smooth transition in variances," Journal of Economic Dynamics and Control, Elsevier, vol. 84(C), pages 43-57.
    15. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2016. "Bayesian Vector Autoregressions with Non-Gaussian Shocks," CReMFi Discussion Papers 5, CReMFi, School of Economics and Finance, QMUL.
    16. Juan Carlos Cuestas & Bo Tang, 2015. "Exchange Rate Changes and Stock Returns in China: A Markov Switching SVAR Approach," Working Papers 2015024, The University of Sheffield, Department of Economics.
    17. Guido Turnip, 2017. "Identification of Small Open Economy SVARs via Markov-Switching Heteroskedasticity," The Economic Record, The Economic Society of Australia, vol. 93(302), pages 465-483, September.
    18. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(02), pages 363-393, March.
    19. Helmut Lütkepohl & Aleksei Netsunajev, 2014. "Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market," Discussion Papers of DIW Berlin 1388, DIW Berlin, German Institute for Economic Research.
    20. Netsunajev, Aleksei, 2013. "Reaction to technology shocks in Markov-switching structural VARs: Identification via heteroskedasticity," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 51-62.
    21. Velinov, Anton, 2016. "On the importance of testing structural identification schemes and the potential consequences of incorrectly identified models," Annual Conference 2016 (Augsburg): Demographic Change 145581, Verein für Socialpolitik / German Economic Association.
    22. Dominik Bertsche & Robin Braun, 2018. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Working Paper Series of the Department of Economics, University of Konstanz 2018-03, Department of Economics, University of Konstanz.
    23. Thomas Dimpfl & Franziska J. Peter, 2015. "Price discovery in the markets for credit risk: A Markov switching approach," SFB 649 Discussion Papers SFB649DP2015-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    24. Sun, Hang, 2016. "Crisis-Contingent Dynamics of Connectedness: An SVAR-Spatial-Network “Tripod” Model with Thresholds," Research Memorandum 032, Maastricht University, Graduate School of Business and Economics (GSBE).
    25. Dungey, Mardi & Milunovich, George & Thorp, Susan & Yang, Minxian, 2012. "Endogenous crisis dating and contagion using smooth transition structural GARCH," Working Papers 15030, University of Tasmania, Tasmanian School of Business and Economics, revised 29 Aug 2012.
    26. Kohonen, Anssi, 2012. "Transmission of Government Default Risk in the Eurozone," MPRA Paper 43823, University Library of Munich, Germany.
    27. Klinger, Sabine & Weber, Enzo, 2015. "Detecting unemployment hysteresis : a simultaneous unobserved components model with Markov switching," IAB Discussion Paper 201528, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    28. Podstawski, Maximilian & Velinov, Anton, 2018. "The state dependent impact of bank exposure on sovereign risk," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 63-75.
    29. Grosse Steffen, Christoph & Podstawski, Maximilian, 2017. "Ambiguity and Time-Varying Risk Aversion in Sovereign Debt Markets," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168101, Verein für Socialpolitik / German Economic Association.
    30. Alessio Moneta & Doris Entner & Patrik O. Hoyer & Alex Coad, 2013. "Causal Inference by Independent Component Analysis: Theory and Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 705-730, October.
    31. Herwartz, Helmut & Lütkepohl, Helmut, 2014. "Structural vector autoregressions with Markov switching: Combining conventional with statistical identification of shocks," Journal of Econometrics, Elsevier, vol. 183(1), pages 104-116.
    32. Helmut Luetkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticity - A Comparison of Different Volatility Models," CESifo Working Paper Series 5308, CESifo Group Munich.
    33. Helmut Lütkepohl & Aleksei Netsunajev, 2012. "Disentangling Demand and Supply Shocks in the Crude Oil Market: How to Check Sign Restrictions in Structural VARs," Discussion Papers of DIW Berlin 1195, DIW Berlin, German Institute for Economic Research.
    34. Helmut Lütkepohl, 2012. "Reducing Confidence Bands for Simulated Impulse Responses," Discussion Papers of DIW Berlin 1235, DIW Berlin, German Institute for Economic Research.
    35. Helmut Lütkepohl & Anton Velinov, 2014. "Structural Vector Autoregressions: Checking Identifying Long-run Restrictions via Heteroskedasticity," SFB 649 Discussion Papers SFB649DP2014-009, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    36. Alexander Kriwoluzky, 2009. "Matching Theory and Data: Bayesian Vector Autoregression and Dynamic Stochastic General Equilibrium Models," Economics Working Papers ECO2009/29, European University Institute.
    37. Anton Velinov & Wenjuan Chen, 2014. "Are There Bubbles in Stock Prices?: Testing for Fundamental Shocks," Discussion Papers of DIW Berlin 1375, DIW Berlin, German Institute for Economic Research.
    38. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    39. Helmut Lütkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticy," SFB 649 Discussion Papers SFB649DP2015-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    40. Dmitry Kulikov & Aleksei Netsunajev, 2016. "Identifying Shocks in Structural VAR models via heteroskedasticity: a Bayesian approach," Bank of Estonia Working Papers wp2015-8, Bank of Estonia, revised 19 Feb 2016.
    41. Tamim Bayoumi & Trung T Bui, 2012. "Global Bonding; Do U.S. Bond and Equity Spillovers Dominate Global Financial Markets?," IMF Working Papers 12/298, International Monetary Fund.
    42. Kilian, Lutz, 2011. "Structural Vector Autoregressions," CEPR Discussion Papers 8515, C.E.P.R. Discussion Papers.
    43. Helmut Lütkepohl & Tomasz Woźniak, 2017. "Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity," Discussion Papers of DIW Berlin 1707, DIW Berlin, German Institute for Economic Research.
    44. Karamé, Frédéric, 2015. "Asymmetries and Markov-switching structural VAR," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 85-102.
    45. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    46. Winkelmann, Lars & Netsunajev, Aleksei, 2015. "International Transmissions of Inflation Expectations in a Markov Switching Structural VAR Model," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112900, Verein für Socialpolitik / German Economic Association.
    47. Aleksei Netsunajev & Katharina Glass, 2016. " Uncertainty and Employment Dynamics in the Euro Area and the US," SFB 649 Discussion Papers SFB649DP2016-002, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    48. Dmitry Kulikov & Aleksei Netsunajev, 2013. "Identifying monetary policy shocks via heteroskedasticity: a Bayesian approach," Bank of Estonia Working Papers wp2013-9, Bank of Estonia, revised 09 Dec 2013.
    49. Stefan Bruder, 2018. "Inference for structural impulse responses in SVAR-GARCH models," ECON - Working Papers 281, Department of Economics - University of Zurich.
    50. Vladimir Dombrovskii & Tatyana Obyedko, 2014. "Dynamic Investment Portfolio Optimization under Constraints in the Financial Market with Regime Switching using Model Predictive Control," Papers 1410.1136, arXiv.org.
    51. Kalli, Maria & Griffin, Jim E., 2018. "Bayesian nonparametric vector autoregressive models," Journal of Econometrics, Elsevier, vol. 203(2), pages 267-282.
    52. Emanuele BACCHIOCCHI & Luca FANELLI, 2012. "Identification in structural vector autoregressive models with structural changes," Departmental Working Papers 2012-16, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    53. YANG, Yukai, 2014. "Testing constancy of the error covariance matrix in vector models against parametric alternatives using a spectral decomposition," CORE Discussion Papers 2014017, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    54. Helmut Luetkepohl & George Milunovich, 2015. "Testing for Identification in SVAR-GARCH Models," SFB 649 Discussion Papers SFB649DP2015-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    55. Emanuele Bacchiocchi & Efrem Castelnuovo & Luca Fanelli, 2014. "Gimme a break! Identification and estimation of the macroeconomic effects of monetary policy shocks in the U.S," "Marco Fanno" Working Papers 0181, Dipartimento di Scienze Economiche "Marco Fanno".
    56. Daniel Bierbaumer & Malte Rieth & Anton Velinov, 2018. "Nonlinear Intermediary Pricing in the Oil Futures Market," Discussion Papers of DIW Berlin 1722, DIW Berlin, German Institute for Economic Research.
    57. Helmut Lütkepohl & Aleksei Netsunajev, 2018. "The Relation between Monetary Policy and the Stock Market in Europe," Discussion Papers of DIW Berlin 1729, DIW Berlin, German Institute for Economic Research.
    58. Helmut Lütkepohl & George Milunovich, 2015. "Testing for Identification in SVAR-GARCH Models: Reconsidering the Impact of Monetary Shocks on Exchange Rates," Discussion Papers of DIW Berlin 1455, DIW Berlin, German Institute for Economic Research.
    59. Bouakez, Hafedh & Essid, Badye & Normandin, Michel, 2013. "Stock returns and monetary policy: Are there any ties?," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 33-50.
    60. Efrem Castelnuovo, 2016. "Monetary policy shocks and Cholesky VARs: an assessment for the Euro area," Empirical Economics, Springer, vol. 50(2), pages 383-414, March.
    61. Helmut Lütkepohl, 2012. "Identifying Structural Vector Autoregressions via Changes in Volatility," Discussion Papers of DIW Berlin 1259, DIW Berlin, German Institute for Economic Research.
    62. Kohonen, Anssi, 2013. "On detection of volatility spillovers in overlapping stock markets," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 140-158.
    63. Dieter Nautz & Aleksei Netsunajev & Till Strohsal, 2016. "Aggregate Employment, Job Polarization and Inequalities: A Transatlantic Perspective," SFB 649 Discussion Papers SFB649DP2016-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    64. Markku Lanne & Jani Luoto, 2016. "Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression," CREATES Research Papers 2016-04, Department of Economics and Business Economics, Aarhus University.
    65. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2014. "What do VARs Tell Us about the Impact of a Credit Supply Shock? An Empirical Analysis," Working Papers 716, Queen Mary University of London, School of Economics and Finance.
    66. Aleksei Netšunajev & Lars Winkelmann, 2016. "International dynamics of inflation expectations," SFB 649 Discussion Papers SFB649DP2016-019, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    67. Velinov, Anton & Chen, Wenjuan, 2015. "Do stock prices reflect their fundamentals? New evidence in the aftermath of the financial crisis," Journal of Economics and Business, Elsevier, vol. 80(C), pages 1-20.
    68. Puonti, Päivi, 2016. "Fiscal multipliers in a structural VEC model with mixed normal errors," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 144-154.
    69. Emanuele BACCHIOCCHI, 2011. "Identification in structural VAR models with different volatility regimes," Departmental Working Papers 2011-39, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    70. Markku Lanne & Mika Meitz & Pentti Saikkonen, 2015. "Identification and estimation of non-Gaussian structural vector autoregressions," CREATES Research Papers 2015-16, Department of Economics and Business Economics, Aarhus University.
    71. Nautz, Dieter & Netsunajew, Aleksei & Strohsal, Till, 2017. "The Anchoring of Inflation Expectations in the Short and in the Long Run," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168075, Verein für Socialpolitik / German Economic Association.
    72. Herwartz, Helmut, 2014. "Structural analysis with independent innovations," Center for European, Governance and Economic Development Research Discussion Papers 208, University of Goettingen, Department of Economics.
    73. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, Elsevier.
    74. Philip Arestis & Michail Karoglou & Kostas Mouratidis, 2016. "Monetary Policy Preferences of the EMU and the UK," Manchester School, University of Manchester, vol. 84(4), pages 528-550, July.
    75. Noel Gaston & Gulasekaran Rajaguru, 2015. "A Markov-switching structural vector autoregressive model of boom and bust in the Australian labour market," Empirical Economics, Springer, vol. 49(4), pages 1271-1299, December.

Articles

  1. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    See citations under working paper version above.
  2. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    See citations under working paper version above.
  3. Katarzyna Maciejowska & Rafał Weron, 2015. "Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships," Computational Statistics, Springer, vol. 30(3), pages 805-819, September.
    See citations under working paper version above.
  4. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    See citations under working paper version above.
  5. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    See citations under working paper version above.
  6. Katarzyna Maciejowska, 2010. "Estimation Methods Comparison of SVAR Models with a Mixture of Two Normal Distributions," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 2(4), pages 279-314, September.

    Cited by:

    1. Guarda, Paolo & Rouabah, Abdelaziz & Theal, John, 2012. "An MVAR framework to capture extreme events in macro-prudential stress tests," Working Paper Series 1464, European Central Bank.

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NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 16 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 (11) 2013-03-02 2013-05-24 2013-11-16 2014-01-10 2014-03-15 2014-05-09 2014-05-09 2014-07-13 2014-08-02 2015-04-02 2015-05-16. Author is listed
  2. NEP-CMP: Computational Economics (6) 2013-05-24 2013-11-16 2014-03-15 2014-05-09 2015-10-17 2016-03-29. Author is listed
  3. NEP-ECM: Econometrics (6) 2009-02-28 2010-07-17 2010-07-17 2013-10-25 2014-07-13 2014-08-02. Author is listed
  4. NEP-FOR: Forecasting (6) 2013-03-02 2014-01-10 2014-07-13 2014-08-02 2015-04-02 2015-05-16. Author is listed
  5. NEP-MKT: Marketing (4) 2013-05-24 2014-05-09 2015-10-17 2016-03-29. Author is listed
  6. NEP-ETS: Econometric Time Series (3) 2009-02-28 2010-07-17 2010-07-17
  7. NEP-ORE: Operations Research (3) 2009-02-28 2010-07-17 2014-01-10
  8. NEP-HME: Heterodox Microeconomics (2) 2013-05-24 2014-03-15
  9. NEP-INO: Innovation (2) 2015-10-17 2016-03-29
  10. NEP-REG: Regulation (2) 2013-05-24 2014-05-09
  11. NEP-CBA: Central Banking (1) 2009-02-28
  12. NEP-COM: Industrial Competition (1) 2015-10-17
  13. NEP-CWA: Central & Western Asia (1) 2013-05-24
  14. NEP-ENV: Environmental Economics (1) 2013-05-24
  15. NEP-MAC: Macroeconomics (1) 2014-05-09
  16. NEP-SOC: Social Norms & Social Capital (1) 2016-03-29

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