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Michael Eichler

Personal Details

First Name:Michael
Middle Name:
Last Name:Eichler
Suffix:
RePEc Short-ID:pei32
[This author has chosen not to make the email address public]

Affiliation

(in no particular order)

Graduate School of Business and Economics (GSBE)
School of Business and Economics
Maastricht University

Maastricht, Netherlands
http://www.maastrichtuniversity.nl/SBE
RePEc:edi:meteonl (more details at EDIRC)

School of Business and Economics
Maastricht University

Maastricht, Netherlands
http://www.maastrichtuniversity.nl/sbe
RePEc:edi:femaanl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Machin, S. & Marie, O. & Vujic, S., 2012. "Youth crime and education expansion," Research Memorandum 036, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  2. Eichler, M. & Grothe, O. & Manner, H. & Türk, D.D.T., 2012. "Modeling spike occurrences in electricity spot prices for forecasting," Research Memorandum 029, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  3. Eichler, M. & Motta, G. & von Sachs, R., 2009. "Fitting dynamic factor models to non-stationary time series," Research Memorandum 002, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  4. Eichler, M. & Didelez, V., 2009. "On Granger-causality and the effect of interventions in time series," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

Articles

  1. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.
  2. Eichler, Michael & Motta, Giovanni & von Sachs, Rainer, 2011. "Fitting dynamic factor models to non-stationary time series," Journal of Econometrics, Elsevier, vol. 163(1), pages 51-70, July.
  3. Eichler, Michael, 2008. "Testing nonparametric and semiparametric hypotheses in vector stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 968-1009, May.
  4. Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, vol. 137(2), pages 334-353, April.
  5. Michael Eichler, 2007. "A Frequency-domain Based Test for Non-correlation between Stationary Time Series," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 65(2), pages 133-157, February.
  6. Mathias Drton & Michael Eichler, 2006. "Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 247-257, June.

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. Machin, S. & Marie, O. & Vujic, S., 2012. "Youth crime and education expansion," Research Memorandum 036, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Ward, Shannon & Williams, J. & van Ours, Jan, 2015. "Bad Behavior : Delinquency, Arrest and Early School Leaving," Discussion Paper 2015-040, Tilburg University, Center for Economic Research.
    2. Wang, Chuhong & Liu, Xingfei & Yan, Zizhong & Zhao, Yi, 2022. "Higher education expansion and crime: New evidence from China," China Economic Review, Elsevier, vol. 74(C).
    3. van der Steeg, Marc & van Elk, Roel & Webbink, Dinand, 2015. "Does intensive coaching reduce school dropout? Evidence from a randomized experiment," Economics of Education Review, Elsevier, vol. 48(C), pages 184-197.
    4. Nguyen, Hieu T.M., 2019. "Do more educated neighbourhoods experience less property crime? Evidence from Indonesia," International Journal of Educational Development, Elsevier, vol. 64(C), pages 27-37.
    5. James, Jonathan & Vujić, Sunčica, 2019. "From high school to the high chair: Education and fertility timing," Economics of Education Review, Elsevier, vol. 69(C), pages 1-24.
    6. Ignacio Munyo, 2015. "The Juvenile Crime Dilemma," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 18(2), pages 201-211, April.
    7. Huttunen, Kristiina & Pekkarinen, Tuomas & Uusitalo, Roope & Virtanen, Hanna, 2019. "Lost Boys: Access to Secondary Education and Crime," IZA Discussion Papers 12084, Institute of Labor Economics (IZA).
    8. Propper, Carol & Janke, Katharina & Johnston, David & Shields, Michael A, 2019. "The causal effect of education on chronic health conditions in the UK," CEPR Discussion Papers 14084, C.E.P.R. Discussion Papers.
    9. Nikhil Jha, 2021. "No time for crime? The effect of compulsory engagement on youth crime," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1571-1597, December.
    10. Bebonchu Atems, 2020. "Identifying the Dynamic Effects of Income Inequality on Crime," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 751-782, August.
    11. Roee Sarel, 2022. "Crime and punishment in times of pandemics," European Journal of Law and Economics, Springer, vol. 54(2), pages 155-186, October.
    12. Povilas Lastauskas & Eirini Tatsi, 2017. "Spatial Nexus in Crime and Unemployement in Times of Crisis," Bank of Lithuania Working Paper Series 39, Bank of Lithuania.
    13. James, Jonathan, 2015. "Health and education expansion," Economics of Education Review, Elsevier, vol. 49(C), pages 193-215.
    14. Brutti, Zelda & Montolio, Daniel, 2021. "Preventing criminal minds: Early education access and adult offending behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 97-126.
    15. Pengfei Jia & King Yoong Lim, 2021. "The stabilization role of police spending in a neo‐Keynesian economy with credit market imperfections," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(1), pages 103-125, February.
    16. Janke, Katharina & Johnston, David W. & Propper, Carol & Shields, Michael A., 2018. "The Causal Effect of Education on Chronic Health Conditions," IZA Discussion Papers 11353, Institute of Labor Economics (IZA).
    17. Marc van der Steeg & Roel van Elk & Dinand Webbink, 2012. "Does intensive coaching reduce school dropout?," CPB Discussion Paper 224, CPB Netherlands Bureau for Economic Policy Analysis.
    18. Gray, Daniel & Montagnoli, Alberto & Moro, Mirko, 2021. "Does education improve financial behaviors? Quasi-experimental evidence from Britain," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 481-507.
    19. Nordin , Martin, 2014. "Does Eligibility for Tertiary Education Affect Crime Rates? Quasi-Experimental Evidence," Working Papers 2014:14, Lund University, Department of Economics.
    20. Rud, I & Van Klaveren, C. & Groot, W. and Maassen van den Brink, H., 2013. "Education and Youth Crime: a Review of the Empirical Literature," Working Papers 48, Top Institute for Evidence Based Education Research.
    21. Lindgren, Karl-Oskar & Oskarsson, Sven & Persson, Mikael, 2016. "How does access to education influence political candidacy? Lessons from school openings in Sweden," Working Paper Series 2016:7, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    22. Brugård, Kaja Høiseth & Falch, Torberg, 2013. "Post-compulsory education and imprisonment," Labour Economics, Elsevier, vol. 23(C), pages 97-106.
    23. Aoki, Yu, 2014. "More Schooling, Less Youth Crime? Learning from an Earthquake in Japan," IZA Discussion Papers 8619, Institute of Labor Economics (IZA).
    24. Liu, Xingfei & Wang, Chuhong & Yan, Zizhong & Zhao, Yi, 2022. "Higher Education Expansion and Crime: New Evidence from China," Working Papers 2022-2, University of Alberta, Department of Economics.

  2. Eichler, M. & Grothe, O. & Manner, H. & Türk, D.D.T., 2012. "Modeling spike occurrences in electricity spot prices for forecasting," Research Memorandum 029, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Machin, Stephen & Marie, Olivier & Vujic, Suncica, 2012. "Youth Crime and Education Expansion," IZA Discussion Papers 6582, Institute of Labor Economics (IZA).
    2. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    3. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.
    4. Eichler, M. & Türk, D.D.T., 2012. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Research Memorandum 035, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    5. Volodymyr Korniichuk, 2012. "Forecasting extreme electricity spot prices," Cologne Graduate School Working Paper Series 03-14, Cologne Graduate School in Management, Economics and Social Sciences.

  3. Eichler, M. & Motta, G. & von Sachs, R., 2009. "Fitting dynamic factor models to non-stationary time series," Research Memorandum 002, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Ngai Hang Chan & Linhao Gao & Wilfredo Palma, 2022. "Simultaneous variable selection and structural identification for time‐varying coefficient models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 511-531, July.
    2. Marc Hallin & Charles Mathias & Hugues Pirotte & David Veredas, 2011. "Market liquidity as dynamic factors," Working Papers ECARES 163, 42-50, ULB -- Universite Libre de Bruxelles.
    3. Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    4. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    5. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2020. "Time-varying general dynamic factor models and the measurement of financial connectedness," LIDAM Reprints ISBA 2020015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    7. Martina Hengge & Seton Leonard, 2017. "Factor Models for Non-Stationary Series: Estimates of Monthly U.S. GDP," IHEID Working Papers 13-2017, Economics Section, The Graduate Institute of International Studies.
    8. von Sachs, Rainer, 2019. "Spectral Analysis of Multivariate Time Series," LIDAM Discussion Papers ISBA 2019008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Valdério Anselmo Reisen & Céline Lévy-Leduc & Edson Zambon Monte & Pascal Bondon, 2024. "A dimension reduction factor approach for multivariate time series with long-memory: a robust alternative method," Statistical Papers, Springer, vol. 65(5), pages 2865-2886, July.
    10. Markus Pelger & Ruoxuan Xiong, 2018. "State-Varying Factor Models of Large Dimensions," Papers 1807.02248, arXiv.org, revised Oct 2020.
    11. Duván Humberto Cataño & Carlos Vladimir Rodríguez-Caballero & Daniel Peña, 2019. "Wavelet Estimation for Dynamic Factor Models with Time-Varying Loadings," CREATES Research Papers 2019-23, Department of Economics and Business Economics, Aarhus University.
    12. Hautsch, Nikolaus & Kyj, Lada M. & Malec, Peter, 2011. "The merit of high-frequency data in portfolio allocation," CFS Working Paper Series 2011/24, Center for Financial Studies (CFS).

  4. Eichler, M. & Didelez, V., 2009. "On Granger-causality and the effect of interventions in time series," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Cited by:

    1. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    2. José Osvaldo De Sordi & Marco Antonio Conejero & Manuel Meireles, 2016. "Bibliometric indicators in the context of regional repositories: proposing the D-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 235-258, April.
    3. Louis Anthony (Tony) Cox, 2013. "Improving Causal Inferences in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 33(10), pages 1762-1771, October.

Articles

  1. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.

    Cited by:

    1. Maryniak, Paweł & Trück, Stefan & Weron, Rafał, 2019. "Carbon pricing and electricity markets — The case of the Australian Clean Energy Bill," Energy Economics, Elsevier, vol. 79(C), pages 45-58.
    2. Gambacciani, Marco & Paolella, Marc S., 2017. "Robust normal mixtures for financial portfolio allocation," Econometrics and Statistics, Elsevier, vol. 3(C), pages 91-111.
    3. Mustafa Gülerce & Gazanfer Ünal, 2018. "Electricity price forecasting using multiple wavelet coherence method: Comparison of ARMA versus VARMA," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-20, March.
    4. Pawel Maryniak & Stefan Trueck & Rafal Weron, 2016. "Carbon pricing, forward risk premiums and pass-through rates in Australian electricity futures markets," HSC Research Reports HSC/16/10, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    5. Yao, Haixiang & Chen, Ping & Li, Xun, 2016. "Multi-period defined contribution pension funds investment management with regime-switching and mortality risk," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 103-113.
    6. Sergei Kulakov, 2020. "X-Model: Further Development and Possible Modifications," Forecasting, MDPI, vol. 2(1), pages 1-16, February.
    7. Xu, Zheng, 2013. "Estimation of parametric homogeneous stochastic volatility pricing formulae based on option data," Economics Letters, Elsevier, vol. 120(3), pages 369-373.
    8. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    9. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    10. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    11. Roland Langrock & Timo Adam & Vianey Leos‐Barajas & Sina Mews & David L. Miller & Yannis P. Papastamatiou, 2018. "Spline‐based nonparametric inference in general state‐switching models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 179-200, August.
    12. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    13. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    14. Samet G nay, 2015. "Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic Model and Volatility Modeling for Oil Returns," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 979-985.
    15. Sapio, Alessandro & Spagnolo, Nicola, 2016. "Price regimes in an energy island: Tacit collusion vs. cost and network explanations," Energy Economics, Elsevier, vol. 55(C), pages 157-172.

  2. Eichler, Michael & Motta, Giovanni & von Sachs, Rainer, 2011. "Fitting dynamic factor models to non-stationary time series," Journal of Econometrics, Elsevier, vol. 163(1), pages 51-70, July.
    See citations under working paper version above.
  3. Eichler, Michael, 2008. "Testing nonparametric and semiparametric hypotheses in vector stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 968-1009, May.

    Cited by:

    1. Loubaton, Philippe & Rosuel, Alexis & Vallet, Pascal, 2023. "On the asymptotic distribution of the maximum sample spectral coherence of Gaussian time series in the high dimensional regime," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    2. Shibin Zhang & Xin M. Tu, 2022. "Tests for comparing time‐invariant and time‐varying spectra based on the Anderson–Darling statistic," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(3), pages 254-282, August.
    3. Leucht, Anne & Paparoditis, Efstathios & Rademacher, Daniel & Sapatinas, Theofanis, 2022. "Testing equality of spectral density operators for functional processes," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. Ruprecht Puchstein & Philip Preuß, 2016. "Testing for Stationarity in Multivariate Locally Stationary Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 3-29, January.
    5. Holger Dette & Efstathios Paparoditis, 2009. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 831-857, September.
    6. Jentsch, Carsten & Pauly, Markus, 2012. "A note on using periodogram-based distances for comparing spectral densities," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 158-164.
    7. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    8. Dimitrios Tsitsis & George Karavasilis & Alexandros Rigas, 2012. "Measuring the association of stationary point processes using spectral analysis techniques," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 23-47, March.
    9. Preuß, Philip & Hildebrandt, Thimo, 2013. "Comparing spectral densities of stationary time series with unequal sample sizes," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1174-1183.
    10. Sourav Das & Suhasini Subba Rao & Junho Yang, 2021. "Spectral methods for small sample time series: A complete periodogram approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 597-621, September.
    11. Dette, Holger & Hildebrandt, Thimo, 2012. "A note on testing hypotheses for stationary processes in the frequency domain," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 101-114, February.
    12. McElroy, Tucker & Holan, Scott, 2009. "A local spectral approach for assessing time series model misspecification," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 604-621, April.
    13. Shibin Zhang, 2023. "A copula spectral test for pairwise time reversibility," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(5), pages 705-729, October.
    14. Sundararajan, Raanju R., 2021. "Principal component analysis using frequency components of multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    15. Mahmoudi, Mohammad Reza, 2021. "A computational technique to classify several fractional Brownian motion processes," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    16. Dilip Nachane & Aditi Chaubal, 2022. "A Comparative Evaluation of Some DSP Filters vis-à-vis Commonly Used Economic Filters," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 161-190, September.
    17. Dette, Holger & Paparoditis, Efstathios, 2008. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Technical Reports 2008,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    18. Philip Preuss & Mathias Vetter & Holger Dette, 2013. "Testing Semiparametric Hypotheses in Locally Stationary Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 417-437, September.

  4. Eichler, Michael, 2007. "Granger causality and path diagrams for multivariate time series," Journal of Econometrics, Elsevier, vol. 137(2), pages 334-353, April.

    Cited by:

    1. Renault, Eric & Triacca, Umberto, 2015. "Causality and separability," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 1-5.
    2. Loperfido, Nicola, 2010. "A note on marginal and conditional independence," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1695-1699, December.
    3. Apergis, Nicholas & Bouras, Christos & Christou, Christina & Hassapis, Christis, 2018. "Multi-horizon wealth effects across the G7 economies," Economic Modelling, Elsevier, vol. 72(C), pages 165-176.
    4. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2022. "Temporal networks in the analysis of financial contagion," Working Paper Series 2667, European Central Bank.
    5. Majid M. Al-Sadoon, 2016. "Testing Subspace Granger Causality," Working Papers 850, Barcelona School of Economics.
    6. Chihying, Hsiao & Chen, Pu, 2007. "Learning Causal Relations in Multivariate Time Series Data," Economics Discussion Papers 2007-15, Kiel Institute for the World Economy (IfW Kiel).
    7. Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.
    8. Tsangyao Chang & Wen Yi Chen & Rangan Gupta & Duc Khuong Nguyen, 2013. "Are Stock Prices Related to Political Uncertainty Index in OECD Countries? Evidence from Bootstrap Panel Causality Test," Working Papers 2013-36, Department of Research, Ipag Business School.
    9. Tata Subba Rao & Granville Tunnicliffe Wilson & Michael Eichler & Rainer Dahlhaus & Johannes Dueck, 2017. "Graphical Modeling for Multivariate Hawkes Processes with Nonparametric Link Functions," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 225-242, March.
    10. Rohin Anhal, 2013. "Causality between GDP, Energy and Coal Consumption in India, 1970-2011: A Non-parametric Bootstrap Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 3(4), pages 434-446.
    11. Al-Sadoon, Majid M., 2014. "Geometric and long run aspects of Granger causality," Journal of Econometrics, Elsevier, vol. 178(P3), pages 558-568.
    12. Daniel Danau, 2018. "Prudence and preference for flexibility gain," Economics Working Paper Archive (University of Rennes & University of Caen) 2018-05, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS, revised May 2019.
    13. Eichler, Michael, 2008. "Testing nonparametric and semiparametric hypotheses in vector stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 968-1009, May.
    14. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2018. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2018-08, Department of Economics, University of Konstanz.
    15. Maria Blangiewicz & Krystyna Strzala, 2008. "Notes on a Forecasting Procedure," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 8, pages 75-84.
    16. Chen, Pu & Hsiao, Chih-Ying, 2010. "Looking behind Granger causality," MPRA Paper 24859, University Library of Munich, Germany.
    17. Colombi, R. & Giordano, S., 2012. "Graphical models for multivariate Markov chains," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 90-103.
    18. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Factorial Network Models To Improve P2P Credit Risk Management," MPRA Paper 92633, University Library of Munich, Germany.
    19. Rigana, Katerina & Wit, Ernst-Jan Camiel & Cook, Samantha, 2023. "A new way of measuring effects of financial crisis on contagion in currency markets," International Review of Financial Analysis, Elsevier, vol. 90(C).
    20. Xiandeng Jiang & Le Chang & Yanlin Shi, 2023. "Housing price diffusions in mainland China: evidence from a spatially penalized graphical VAR model," Empirical Economics, Springer, vol. 64(2), pages 765-795, February.
    21. Katerina Rigana & Ernst C. Wit & Samantha Cook, 2024. "Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk," Papers 2402.06032, arXiv.org.
    22. Ahelegbey, Daniel Felix & Giudici, Paolo, 2019. "Tree Networks to Assess Financial Contagion," MPRA Paper 92632, University Library of Munich, Germany.
    23. Roberto Colombi & Sabrina Giordano, 2013. "Monotone dependence in graphical models for multivariate Markov chains," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(7), pages 873-885, October.
    24. Calvo-Pardo, Hector & Mancini, Tullio & Olmo, Jose, 2021. "Granger causality detection in high-dimensional systems using feedforward neural networks," International Journal of Forecasting, Elsevier, vol. 37(2), pages 920-940.
    25. Abdelwahab Allali & Amor Oueslati & Abdelwahed Trabelsi, 2011. "Detection of Information Flow in Major International Financial Markets by Interactivity Network Analysis," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 18(3), pages 319-344, September.
    26. Ahelegbey, Daniel Felix, 2015. "The Econometrics of Bayesian Graphical Models: A Review With Financial Application," MPRA Paper 92634, University Library of Munich, Germany, revised 25 Apr 2016.
    27. Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
    28. Anna Zaremba & Tomaso Aste, 2014. "Measures of Causality in Complex Datasets with application to financial data," Papers 1401.1457, arXiv.org, revised Jun 2014.
    29. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    30. Javier Pérez & A. Sánchez, 2011. "Is there a signalling role for public wages? Evidence for the euro area based on macro data," Empirical Economics, Springer, vol. 41(2), pages 421-445, October.
    31. Katerina Rigana & Ernst-Jan Camiel Wit & Samantha Cook, 2021. "Using Network-based Causal Inference to Detect the Sources of Contagion in the Currency Market," Papers 2112.13127, arXiv.org.
    32. Eichler, M. & Didelez, V., 2009. "On Granger-causality and the effect of interventions in time series," Research Memorandum 003, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    33. Chen, Pu, 2010. "A time series causal model," MPRA Paper 24841, University Library of Munich, Germany.
    34. Tan Le & Franck Martin & Duc Nguyen, 2018. "Dynamic connectedness of global currencies: a conditional Granger-causality approach," Working Papers hal-01806733, HAL.
    35. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    36. Upadhyay, Shashankaditya & Banerjee, Anirban & Panigrahi, Prasanta K., 2020. "Causal evolution of global crisis in financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    37. Hongwei Chuang, 2015. "Correlation Persistence in Financial Markets: A Network Theory Approach," DSSR Discussion Papers 33, Graduate School of Economics and Management, Tohoku University.
    38. Erdost Torun & Afife Duygu Ayhan Akdeniz & Erhan Demireli & Simon Grima, 2022. "Long-Term US Economic Growth and the Carbon Dioxide Emissions Nexus: A Wavelet-Based Approach," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    39. Gao, Wei & Zhao, Hongxia, 2013. "Conditional independence graph for nonlinear time series and its application to international financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2460-2469.
    40. Torun, Erdost & Chang, Tzu-Pu & Chou, Ray Y., 2020. "Causal relationship between spot and futures prices with multiple time horizons: A nonparametric wavelet Granger causality test," Research in International Business and Finance, Elsevier, vol. 52(C).
    41. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    42. Wei Yao & Weikun Zhang & Wenxiu Li & Penglong Li, 2022. "Measurement and Evaluation of Convergence of Japan’s Marine Fisheries and Marine Tourism," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    43. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.
    44. Daniel Felix Ahelegbey, 2015. "The Econometrics of Networks: A Review," Working Papers 2015:13, Department of Economics, University of Venice "Ca' Foscari".
    45. Schreiber, Sven, 2013. "(When) does money growth help to predict Euro-area inflation at low frequencies?," Discussion Papers 2013/10, Free University Berlin, School of Business & Economics.
    46. Teye, Alfred Larm & Ahelegbey, Daniel Felix, 2017. "Detecting spatial and temporal house price diffusion in the Netherlands: A Bayesian network approach," Regional Science and Urban Economics, Elsevier, vol. 65(C), pages 56-64.
    47. Triacca, Umberto, 2018. "Granger causality between vectors of time series: A puzzling property," Statistics & Probability Letters, Elsevier, vol. 142(C), pages 39-43.
    48. Upadhyay, Shashankaditya & Mukherjee, Indranil & Panigrahi, Prasanta K., 2023. "Inner composition alignment networks reveal financial impacts of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

  5. Michael Eichler, 2007. "A Frequency-domain Based Test for Non-correlation between Stationary Time Series," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 65(2), pages 133-157, February.

    Cited by:

    1. Eichler, Michael, 2008. "Testing nonparametric and semiparametric hypotheses in vector stationary processes," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 968-1009, May.
    2. Dette, Holger & Hildebrandt, Thimo, 2012. "A note on testing hypotheses for stationary processes in the frequency domain," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 101-114, February.
    3. McElroy, Tucker & Holan, Scott, 2009. "A local spectral approach for assessing time series model misspecification," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 604-621, April.

  6. Mathias Drton & Michael Eichler, 2006. "Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 247-257, June.

    Cited by:

    1. Peña Jose M., 2020. "Unifying Gaussian LWF and AMP Chain Graphs to Model Interference," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 1-21, January.
    2. Søren Højsgaard & Steffen L. Lauritzen, 2008. "Graphical Gaussian models with edge and vertex symmetries," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 1005-1027, November.
    3. Fitch, A. Marie & Jones, Beatrix, 2012. "The cost of using decomposable Gaussian graphical models for computational convenience," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2430-2441.
    4. Peña Jose M., 2019. "Unifying Gaussian LWF and AMP Chain Graphs to Model Interference," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 1-21, January.

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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 4 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-ECM: Econometrics (2) 2009-02-22 2012-07-14
  2. NEP-ENE: Energy Economics (2) 2012-07-01 2012-07-14
  3. NEP-ETS: Econometric Time Series (1) 2009-02-22
  4. NEP-FOR: Forecasting (1) 2012-07-01
  5. NEP-HPE: History and Philosophy of Economics (1) 2009-02-22

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