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(1974). A cost effectiveness analysis of alternative air quality control strategies. Journal of Environmental Economics and Management 1: 237–250\nAtkinson S.E. and Tietenberg T. (1982). The empirical properties of two classes of designs for transferable discharge permits. Journal of Environmental Economics and Management 9: 101–121\nBurtraw D. (1996). The SO2 emissions trading program: Cost savings with allowance trades. Contemporary Economic Policy 14: 79–94\nCho S.H., Chen Z., Yen S.T. and Eastwood D.B. (2006). Estimating effects of an urban growth boundary on land development. Journal of Agricultural and Applied Economics 38(2): 303–314\nConover, C. (2003). A review and synthesis of the cost and benefits of health service regulations. Duke University.\nCormen T.H., Leiserson C.E., Rivest R.L. and Stein C. (2001). Introduction to algorithms. MIT Press, Cambridge\nDales J.H. (1968). Pollution, property and prices. University of Toronto Press, Toronto\nHennessy D.A. and Lapan H.E. (2003). Technology asymmetries, group algebra and multiplant cost minimization. Economic Inquiry 41(1): 183–192\nInternational Business Machines (IBM) (2007). Logistics optimization tools. Retrieved from the internet on October 7, 2007 at http:\u002F\u002Fwww.research.ibm.com\u002Ftrl\u002Fprojects\u002Foptsim\u002Flogiopt\u002Findex_e.htm\nKeeley M. and Elzinga K. (2003). Uniform gasoline price regulation: Consequences for consumer welfare. International Journal of the Economics of Business 10(2): 157–168\nKeohane, N. (2006). Cost savings from allowance trading in the 1990 clean air act: Estimates from a choice-based model. In C. Kolstad & J. Freeman (Eds.), Moving to markets in environmental regulation: Lessons from twenty years of experience. Oxford University Press.\nKling C.L. (1994). Environmental benefits from marketable discharge permits or an ecological vs. economical perspective on marketable permits. Ecological Economics 11: 57–64\nMasson R.T. and DeBrock L.M. (1980). The structural effects of state regulation of retail fluid milk prices. The Review of Economics and Statistics 62(2): 254–262\nMcGartland A.M. and Oates W.E. (1985). Marketable permits for the prevention of environmental deterioration. Journal of Environmental Economics and Management 12: 207–228\nMontgomery W.D. (1972). Markets in licenses and efficient pollution control programs. Journal of Economic Theory 5: 395–418\nO’Ryan R.E. (1996). Cost-effective policies to improve urban air quality in Santiago, Chile. Journal of Environmental Economics and Management 31: 302–313\nPorter D.R., Marsh L.L. and Salvesen D.A. (Eds.) (1996). Mitigation banking: Theory and practice. Island Press, Washington\nPosner R. A. (1975). The social costs of monopoly and regulation. Journal of Political Economy 83: 807–828\nSeskin E.P., Anderson R.J. and Reid R.O. (1983). An empirical analysis of economic strategies for controlling air pollution. Journal of Environmental Economics 10: 112–124\nShadbegian, R., Gray, W., & Morgan, C. (2006). A spatial analysis of the consequences of the SO 2 trading program. Paper presented at Market Mechanisms and Incentives: Applications to Environmental Policy, EPA NCEE and NCER conference, Washington, October.\nWhitehead, J. R. (2006). Assessing the potential for water quality trading in the Bear River watershed. Master’s Thesis, Department of Economics, Utah State University.\nZingales, L. (2004). The costs and benefits of financial market regulation. ECGI-Law Working Paper No. 21\u002F2004. Available at SSRN: http:\u002F\u002Fwww.ssrn.com\u002Fabstract=536682",{"EN":149},"Two heuristic algorithms are developed to handle combinatorial and discrete complications persistent in markets where total quantity is regulated. The algorithms account for trader heterogeneity, which could either be innate (as exists in a wide class of environmental problems) or imposed by the regulator to simultaneously achieve a social goal, such as distributional equity. In addition, the algorithms account for discreteness in the production technology of the tradable commodity. 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(2013). Euler equations for the estimation of dynamic discrete choice structural models. Advances in Econometrics, 31, 3–44.\nAguirregabiria, Victor, & Mira, Pedro. (2002). Swapping the nested fixed point algorithm: A class of estimators for discrete markov decision models. Econometrica, 70(4), 1519–43.\nAguirregabiria, Victor, & Mira, Pedro. (2010). Dynamic discrete choice structural models: A survey. Journal of Econometrics, 156(1), 38–67.\nArcidiacono, Peter, & Miller, Robert A. (2011). Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity. Econometrica, 79(6), 1823–1867.\nBarber, Michael and Christopher Ferrall 2021. College choice, credit constraints and educational attainment, in progress, current version available from https:\u002F\u002Fferrall.github.io\u002FOODP\u002F\nEckstein, Zvi, & Wolpin, Kenneth. (1989). The specification and estimation of dynamic stochastic discrete choice models: A survey. Journal of Human Resources, 24(4), 562–598.\nFerrall, Christopher 2003. Estimation and Inference in Social Experiments, manuscript, Queen’s University working paper .\nFerrall, Christopher. (2005). solving finite mixture models: Efficient computation in economics under serial and parallel execution. Computational Economics, 25, 343–379.\nFerrall, Christopher 2021. Was Harold Zurcher Myopic After All? Replicating Rust’s Engine Replacement Estimates, Queen’s University working paper 1467, https:\u002F\u002Fwww.econ.queensu.ca\u002Fresearch\u002Fworking-papers\u002F1467.\nHotz, V. Joseph., & Miller, Robert A. (1993). Conditional choice probabilities and the estimation of dynamic models. The Review of Economic Studies, 60(3), 497–529.\nImai, Susumu, Jain, Neelan, & Ching, Andrew. (2009). Bayesian estimation of dynamic discrete choice models. Econometrica, 77(6), 1865–1899.\nKasahara, Hiroyuki, & Shimotsu, Katsumi. (2012). Sequential Estimation of Structural Models With a Fixed Point Constraint. Econometrica, 80(5), 2303–2319.\nKeane, Michael P., & Wolpin, Kenneth I. (1994). The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte carlo evidence. The Review of Economics and Statistics, 76(4), 648–672.\nKeane, Michael P., Petra E. Todd, and Kenneth I. Wolpin 2010. The structural estimation of behavioral models: Discrete choice dynamic programming methods and applications. In: Orley Ashenfelter and David Card (Eds.), Handbook of labor economics, Volume 4a. pp. 331–461.\nKirby, R. A. (2017). Toolkit for value function iteration. Computational Economics, 49, 1–15.\nKirby, R. A 2021. Quantitative macro: Lessons learnt from fourteen replications, manuscript, University of Wellington.\nMaCurdy, Thomas. (1981). An empirical model of labor supply in a life-cycle setting. Journal of Political Economy, 89(6), 1059–1085.\nRust, John. (1987). Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher. Econometrica, 55(5), 999–1033.\nRust, John 2000. Nested fixed point algorithm documentation manual, version 6, https:\u002F\u002Feditorialexpress.com\u002Fjrust\u002Fnfxp.pdf.\nWolpin, Kenneth. (1984). An estimable dynamic stochastic model of fertility and child mortality. Journal of Political Economy, 92(5), 852–874.",{"EN":248},"This paper discusses how to design, solve and estimate dynamic programming models using the open source package niqlow. Reasons are given for why such a package has not appeared earlier and why the object-oriented approach followed by niqlow seems essential. An example is followed that starts with basic coding then expands the model and applies different solution methods to finally estimate parameters from data. The niqlow approach is used to organize the empirical DP literature differently from traditional surveys which may make it more accessible to new researchers. 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B., Aouni, B., & Fayedh, R. E. (2007). Multi-objective stochastic programming for portfolio selection. European Journal of Operational Research, 177(3), 1811–1823.\nAliprantis, C., Brown, D., & Werner, J. (2000). Minimum-cost portfolio insurance. Journal of Economic Dynamics and Control, 24(11–12), 1703–1719.\nBlomvall, J., & Lindberg, P. (2003). Back-testing the performance of an actively managed option portfolio at the Swedish stock market, 1990–1999. Journal of Economic Dynamics and Control, 27(6), 1099–1112.\nBrennan, M., & Cao, H. (1996). Information, trade, and derivative securities. Review of Financial Studies, 9(1), 163–208.\nDupacová, J., Gröwe-Kuska, N., & Römisch, W. (2003). Scenario reduction in stochastic programming: An approach using probability metrics. Mathematical Programming, Series A, 95, 493–511.\nFerstl, R., & Weissensteiner, A. (2010). Cash management using multi-stage stochastic programming. Quantitative Finance, 10(2), 209–219.\nFerstl, R., & Weissensteiner, A. (2011). Asset-liability management under time-varying investment opportunities. Journal of Banking and Finance, 35(1), 182–192.\nFollmer, H., & Sondermann, D. (1986). Hedging of non-redundant contingent claims. In W. Hildenbrand & A. Mas-Colell (Eds.), Contributions to mathematical economics (pp. 205–223). Amsterdam: North-Holland.\nGaivoronski, A. A., Krylov, S., & Van der Wijst, N. (2005). Optimal portfolio selection and dynamic benchmark traking. European Journal of Operational Research, 163(1), 115–131.\nGao, P. W. (2009). Options strategies with the risk adjustment. European Journal of Operation Research, 192, 975–980.\nGeyer, A., Hanke, M., & Weissensteiner, A. (2011). Scenario tree generation and multi-asset financial optimization problems. Working paper. Available at SSRN. http:\u002F\u002Fssrn.com\u002Fabstract=1857411 or http:\u002F\u002Fdx.doi.org\u002F10.2139\u002Fssrn.1857411.\nGondzio, J., Kouwenberg, R., & Vorst, T. (2003). Hedging options under transaction costs and stochastic volatility. Journal of Economic Dynamics and Control, 27(6), 1045–1068.\nGrinold, R. C. (1999). Mean-variance and scenario-based approaches to portfolio selection. The Journal of Portfolio Management, 25(2), 10–22.\nGrinblatt, M., & Titman, S. (1993). Performance measurement without benchmarks: An examination of mutual fund returns. Journal of Business, 66(1), 47–68.\nHarrison, J. M., & Pliska, S. R. (1981). Martingales and stochastic integrals in the theory of continuous trading. Stochastic Processes and their Applications, 11, 215–260.\nHaugh, M., & Lo, A. (2001). Asset allocation and derivatives. Quantitative Finance, 1(1), 45–72.\nHeitsch, H., & Römisch, W. (2003). Scenario reduction algorithms in stochastic programming. Computational Optimization and Applications, 24, 187–206.\nHochreiter, R., & Pflug, G. Ch. (2007). Financial scenario generation for stochastic multi-stage decision processes as facility location problems. Annals of Operations Research, 152(1), 257–272.\nHorasanh, M. (2008). Hedging strategy for a portfolio of options and stocks with linear programming. Applied Mathematics and Computation, 199, 804–810.\nHøyland, K., Kaut, M., & Stein, W. (2003). A heuristic for moment-matching scenario generation. Computational Optimization and Applications, 24(2–3), 169–185.\nHøyland, K., & Wallance, S. W. (2001). Generating scenario trees for multistage decision problems. Management Science, 47(2), 295–307.\nHuang, D. S., Zhu, S. S., Fabozzi, F. J., & Fukushima, M. (2008). Portfolio selection with uncertain exit time: A robust CVaR approach. Journal of Economic Dynamics and Control, 32(2), 594–623.\nHuang, D. S., Zhu, S. S., Fabozzi, F. J., & Fukushima, M. (2010). Portfolio selection under distributional uncertainty: A relative robust CVaR approach. European Journal of Operational Research, 203(1), 185–194.\nKlaassen, P. (2002). Comment on “Generating scenario trees for multistage decision problems”. Management Science, 48(11), 1512–1516.\nKorn, R., & Trautmann, S. (1999). Optimal control of option portfolios and applications. OR Spektrum, 21, 123–146.\nLiu, J., & Pan, J. (2003). Dynamics derivative strategies. Journal of Financial Economics, 69(3), 401–430.\nMerton, R., Scholes, M., & Gladstone, M. (1978). The return and risk of alternative call option portfolio investment strategies. Journal of Business, 51, 183–242.\nMuck, M. (2010). Trading strategies with partial access to the derivatives market. Journal of Banking and Finance, 34(6), 1288–1298.\nNeuberger, A., & Hodges, S. (2002). How large are the benefits from using options? Journal of Financial and Quantitative Analysis, 37(2), 202–220.\nPapahristodoulou, C. (2004). Option strategies with linear programming. European Journal of Operation Research, 157, 246–256.\nPflug, G. C. (2001). Optimal scenario tree generation for multiperiod financial planning. Mathematical Programming, 89(2), 251–271.\nRasmussen, K. M., & Clausen, J. (2007). Mortgage loan portfolio optimization using multi-stage stochastic programming. Journal of Economic Dynamics and Control, 31(3), 742–766.\nScheuenstuhl, G., & Zagst, R. (2008). Integrated portfolio management with options. European Journal of Operational Research, 185(3), 1477–1500.\nSortino, F. A., & Van der Meer, R. (1991). Downside risk: Capturing what’s at stake in investment situations. Journal of Portfolio Management, 17(4), 27–31.\nSinha, P., & Johar, A. (2010). Hedging Greeks for a portfolio of options using linear and quadratic programming. The Journal of Prediction Markets, 4(1), 17–26.\nSortina, F., Van der Meer, R., & Plantinga, A. (1999). The Dutch triangle—A framework to measure upside potential relative to downside risk. Journal of Portfolio Management, 26(1), 50–58.\nTopaloglou, N., Vladimirou, H., & Zenios, S. A. (2002). CVaR models with selective hedging for international asset allocation. Journal of Banking and Finance, 26(7), 1535–1561.\nTopaloglou, N., Vladimirou, H., & Zenios, S. A. (2008a). A dynamic stochastic programming model for international portfolio management. European Journal of Operational Research, 185, 1501–1524.\nTopaloglou, N., Vladimirou, H., & Zenios, S. A. (2008b). Pricing options on scenario trees. Journal of Banking and Finance, 32(2), 283–298.\nTopaloglou, N., Vladimirou, H., & Zenios, S. A. (2011). Optimizing international portfolios with options and forwards. Journal of Banking and Finance, 35(12), 3188–3201.\nZhao, Y., & Ziemba, W. (2008). Calculating risk neutral probabilities and optimal portfolio policies in a dynamic investment model with downside risk control. European Journal of Operational Research, 185(3), 1525–1540.",{"EN":323},"In this paper, we develop a multi-stage stochastic programming model for dynamic international portfolio risk management with options in an integrated view. Upon scenario trees, the model can automatically compute the optimal hedging strategies, which provides rolling and dynamic decisions for how much option positions should be established and how much should be liquidated, while simultaneously allocating the corresponding underlying assets. Extensive numerical analyses strongly verify the effectiveness of the model, especially in market downturns, and support the computational feasibility and performance of the model.",{"EN":325},"International Assets Allocation with Risk Management via Multi-Stage Stochastic Programming",{"VOID":327},"10.1007\u002Fs10614-013-9365-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10614-013-9365-z",[330,347],{"id":331,"sortIndex":21,"researcher":20,"roles":332,"affiliations":333,"properties":344},"4a711049-e0d0-45b4-a1d3-13c60c92b8b5",[162],[334],{"id":20,"sortIndex":21,"affiliation":335,"properties":20},{"id":336,"createTime":337,"updateTime":338,"relativeEntities":339,"slug":340,"properties":341,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"b4794992-ddd6-4050-8009-4441c13b92ae","2024-01-16T08:58:31.732+00:00","2025-01-02T17:08:42.388+00:00",[],"School-of-Economics-and-Management-Beihang-University-Beijing-China",{"title":342},{"VI":343},"School of Economics and Management, Beihang University, Beijing, China",{"title":345},{"VI":346},"Libo Yin",{"id":348,"sortIndex":129,"researcher":20,"roles":349,"affiliations":350,"properties":356},"b032540d-aef9-46db-a09f-95791d562895",[162],[351],{"id":20,"sortIndex":21,"affiliation":352,"properties":20},{"id":336,"createTime":337,"updateTime":338,"relativeEntities":353,"slug":340,"properties":354,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":355},{"VI":343},{"title":357},{"VI":358},"Liyan Han",{"url":328,"publisher":360,"properties":395},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":361,"slug":10,"properties":362,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":366,"manageAffiliations":367,"indexDatabases":368,"url":120,"thumbnailPath":20,"statistic":390,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":363,"eissn":364,"title":365},{"VOID":13},{"VOID":15},{"EN":17},[],[],[369,376,383],{"id":103,"indexDatabase":370,"url":79,"indexYears":20,"academicFieldIds":375,"indexDatabaseRanking":20},{"id":105,"createTime":106,"updateTime":107,"relativeEntities":371,"label":372,"description":373,"key":114,"publicationTags":374,"standard":20},[],{"EN":110,"VI":110},{"VI":112,"EN":113},[116,78],[118,119],{"id":64,"indexDatabase":377,"url":79,"indexYears":20,"academicFieldIds":382,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":378,"label":379,"description":380,"key":75,"publicationTags":381,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81],{"id":83,"indexDatabase":384,"url":96,"indexYears":97,"academicFieldIds":389,"indexDatabaseRanking":101},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":385,"label":386,"description":387,"key":93,"publicationTags":388,"standard":20},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100],{"impactFactor":21,"impactFactorByYear":391,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":123,"totalPublicationByYear":392,"totalCitation":21,"totalCitationByYear":393,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":394,"hindexLast5Year":21,"hindex":21},{},{"1997":125,"1998":126,"1999":127,"2000":125,"2001":127,"2002":128,"2003":59,"2010":129,"2011":130,"2017":129,"2020":129,"2021":129,"2022":131,"2023":129},{},{},{"volume":396,"pages":398},{"VOID":397},"55",{"VOID":399},"383-405","2013-03-12",2013,{"id":403,"createTime":404,"updateTime":405,"relativeEntities":406,"slug":407,"properties":408,"entityType":154,"verifyStatus":155,"verifyTime":405,"verifyNote":156,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":415,"fullTextUrl":20,"authors":416,"publicationType":193,"publisherRelationship":444,"citationCount":20,"citationInfo":20,"publishDate":485,"publishYear":486,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":237},"5ecd5739-1417-4169-8aa0-4af88479ce4a","2024-02-11T15:01:39.344+00:00","2024-12-17T23:57:41.278+00:00",[],"Robust-Evolutionary-Algorithm-Design-for-Socio-Economic-Simulation-Some-Comments",{"references":409,"title":411,"doi":413},{"VOID":410},"Alkemade F., La Poutré J.A., Amman H.M. (2006) Robust evolutionary algorithm design for socio-economic simulation. Computational Economics 28(4): 355–370 doi:10.1007\u002Fs10614-006-9051-5\nAlkemade F., La Poutré J.A., Amman H.M. (2007) On social learning and robust evolutionary algorithm design in the Cournot oligopoly game. Computational Intelligence 23(2): 162–175 doi:10.1111\u002Fj.1467-8640.2007.00300.x\nAlkemade, F., La Poutré, J. A., & Amman, H. M. (2008). Robust evolutionary algorithm design for socio-economic simulation: A correction. Computational Economics.\nArifovic J. (1994) Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics & Control 18(1): 3–28 doi:10.1016\u002F0165-1889(94)90067-1\nArthur, W. B. (1994). Increasing returns and path dependence in the economy. University of Michigan Press.\nBeyer, H.-G. (2001). The theory of evolution strategies. Springer.\nDawid, H. (1996). Adaptive learning by genetic algorithms. Number 441 in Lecture notes in economics and mathematical systems. Springer.\nDuffy, J. (2006). Agent-based models and human subject experiments. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics (Vol. 2, pp. 949–1011). Elsevier.\nKandori M., Mailath G.J., Rob R. (1993) Learning, mutation, and long run equilibria in games. Econometrica 61(1): 29–56 doi:10.2307\u002F2951777\nNix A.E., Vose M.D. (1992) Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence 5(1): 79–88 doi:10.1007\u002FBF01530781\nVega-Redondo F. (1997) The evolution of Walrasian behavior. Econometrica 65(2): 375–384 doi:10.2307\u002F2171898\nWeibull, J. W. (1995). Evolutionary game theory. MIT Press.",{"EN":412},"Robust Evolutionary Algorithm Design for Socio-Economic Simulation: Some Comments",{"VOID":414},"10.1007\u002Fs10614-008-9148-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10614-008-9148-0",[417,432],{"id":418,"sortIndex":129,"researcher":20,"roles":419,"affiliations":420,"properties":429},"24289cce-5554-438f-a634-e3427df09fa6",[162],[421],{"id":20,"sortIndex":21,"affiliation":422,"properties":20},{"id":423,"createTime":424,"updateTime":424,"relativeEntities":425,"slug":20,"properties":426,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"cc215301-4834-4dc9-a8a0-3c3a93302bb3","2023-12-07T17:05:13.680+00:00",[],{"title":427},{"VI":428},"Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands",{"title":430},{"VI":431},"Nees Jan van Eck",{"id":433,"sortIndex":21,"researcher":20,"roles":434,"affiliations":435,"properties":441},"473b41aa-420b-46f9-acf5-40017e0f4c3d",[162],[436],{"id":20,"sortIndex":21,"affiliation":437,"properties":20},{"id":423,"createTime":424,"updateTime":424,"relativeEntities":438,"slug":20,"properties":439,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":440},{"VI":428},{"title":442},{"VI":443},"Ludo Waltman",{"url":415,"publisher":445,"properties":480},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":446,"slug":10,"properties":447,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":451,"manageAffiliations":452,"indexDatabases":453,"url":120,"thumbnailPath":20,"statistic":475,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":448,"eissn":449,"title":450},{"VOID":13},{"VOID":15},{"EN":17},[],[],[454,461,468],{"id":103,"indexDatabase":455,"url":79,"indexYears":20,"academicFieldIds":460,"indexDatabaseRanking":20},{"id":105,"createTime":106,"updateTime":107,"relativeEntities":456,"label":457,"description":458,"key":114,"publicationTags":459,"standard":20},[],{"EN":110,"VI":110},{"VI":112,"EN":113},[116,78],[118,119],{"id":64,"indexDatabase":462,"url":79,"indexYears":20,"academicFieldIds":467,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":463,"label":464,"description":465,"key":75,"publicationTags":466,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81],{"id":83,"indexDatabase":469,"url":96,"indexYears":97,"academicFieldIds":474,"indexDatabaseRanking":101},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":470,"label":471,"description":472,"key":93,"publicationTags":473,"standard":20},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100],{"impactFactor":21,"impactFactorByYear":476,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":123,"totalPublicationByYear":477,"totalCitation":21,"totalCitationByYear":478,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":479,"hindexLast5Year":21,"hindex":21},{},{"1997":125,"1998":126,"1999":127,"2000":125,"2001":127,"2002":128,"2003":59,"2010":129,"2011":130,"2017":129,"2020":129,"2021":129,"2022":131,"2023":129},{},{},{"volume":481,"pages":483},{"VOID":482},"33",{"VOID":484},"103-105","2008-06-13",2008,{"id":488,"createTime":489,"updateTime":490,"relativeEntities":491,"slug":492,"properties":493,"entityType":154,"verifyStatus":155,"verifyTime":490,"verifyNote":156,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":502,"fullTextUrl":20,"authors":503,"publicationType":193,"publisherRelationship":563,"citationCount":20,"citationInfo":20,"publishDate":604,"publishYear":605,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":237},"3a9bb57b-037b-438f-97ec-31f7346ca822","2024-01-16T10:26:35.292+00:00","2025-01-08T23:53:59.522+00:00",[],"Do-Energy-and-Banking-CDS-Sector-Spreads-Reflect-Financial-Risks-and-Economic-Policy-Uncertainty-A-Time-Scale-Decomposition-Approach",{"references":494,"abstract":496,"title":498,"doi":500},{"VOID":495},"Abid, F., & Naifar, N. (2006). The determinants of credit default swap rates: An explanatory study. International Journal of Theoretical and Applied Finance, 9(1), 23–42.\nAlexander, C., & Kaeck, A. (2008). Regime dependent determinants of credit default swap spreads. Journal of Banking & Finance, 32, 1008–1021.\nAlter, A., & Schuler, Y. S. (2012). Credit spread interdependencies of European states and banks during the financial crisis. Journal of Banking & Finance, 36(12), 3444–3468.\nAntonakakis, N., Chatziantoniour, L., & Filis, G. (2013). Dynamic co-movements of stock market returns, implied volatility and policy uncertainty. Economics Letters, 120(1), 87–92.\nBroto, C., & Pérez-Quirós, G. (2015). Disentangling contagion among sovereign CDS spreads during the European debt crisis. Journal of Empirical Finance, 32, 165–179.\nBystrom, H. (2006). Credit default swaps and equity prices: The iTraxx CDS index market. Financial Analysts Journal, 62, 65–76.\nChen, L. H., Hammoudeh, S., & Yuan, Y. (2011). 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K., Mutascu, M. L., & Albulescu, C. T. (2013). The influence of the international oil prices on the real effective exchange rate in Romania in a wavelet transform framework. Energy Economics, 40, 714–733.\nWagner, W. (2008). The homogenization of the financial system and financial crises. Journal of Financial Intermediation, 17, 330–356.\nWisniewski, T. P., & Lambe, B. J. (2015). Does economic policy uncertainty drive CDS spreads? International Review of Financial Analysis, 42, 447–458.\nZhang, B. Y., Zhou, H., & Zhu, H. (2009). Explaining credit default swap spreads with the equity volatility and jump risks of individual firms. Review of Financial Studies, 22(12), 5099–5131.\nZhu, H. (2006). An empirical comparison of credit spreads between the bond market and the credit default swap market. Journal of Financial Services Research, 29(3), 211–235.",{"EN":497},"\nThe aim of this study is to investigate the dynamics of the co-movements of energy and banking sector credit default swaps (CDS) spreads with global financial and economic policy uncertainty and risk factors in the United States and Europe. We first employ the standard quantile regression approach to examine the co-movement dynamics under different credit market conditions. The empirical results show that the standard quantile regression fails to capture the co-movement between the U.S. and European CDS spreads and the economic policy uncertainty and risk factors for all quantiles.\n However, after decomposing the raw series at various scales of resolution, using the wavelet approach to capture the structure, we find that the financial and energy related CDS spreads co-move with the economic policy uncertainty and stock market risk in the intermediate and lower frequency domain. That is, there is a dependency in the intermediate and higher time scales or the intermediate and long investment horizons for both the United States and Europe. However, there are positive effects of the stock market risk index on the U.S. banking CDS spreads, which remain almost similar across quantiles, but the effects are asymmetric for the European banking spreads. The study also provides policy implications.",{"EN":499},"Do Energy and Banking CDS Sector Spreads Reflect Financial Risks and Economic Policy Uncertainty? A Time-Scale Decomposition Approach",{"VOID":501},"10.1007\u002Fs10614-018-9838-1","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10614-018-9838-1",[504,519,534],{"id":505,"sortIndex":21,"researcher":20,"roles":506,"affiliations":507,"properties":516},"f6dadba7-05e3-4be1-b856-bb3ddbcb4f9a",[162],[508],{"id":20,"sortIndex":21,"affiliation":509,"properties":20},{"id":510,"createTime":511,"updateTime":511,"relativeEntities":512,"slug":20,"properties":513,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"662bc7c2-a15f-403e-99ba-302bd2de67fc","2024-02-05T23:37:24.734+00:00",[],{"title":514},{"VI":515},"Department of Finance and Investment, College of Economics and Administrative Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia",{"title":517},{"VI":518},"Nader Naifar",{"id":520,"sortIndex":130,"researcher":20,"roles":521,"affiliations":522,"properties":531},"25597cf6-79dd-48e3-9fc1-c8c8c1cb8bdf",[162],[523],{"id":20,"sortIndex":21,"affiliation":524,"properties":20},{"id":525,"createTime":526,"updateTime":526,"relativeEntities":527,"slug":20,"properties":528,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"19283959-1f0a-480d-a38f-acbb5bb79e1a","2024-01-16T10:26:35.346+00:00",[],{"title":529},{"VI":530},"Energy and Development Sustainability, Montpellier Business School, Montpellier, France",{"title":532},{"VI":533},"Aviral Kumar Tiwari",{"id":535,"sortIndex":129,"researcher":20,"roles":536,"affiliations":537,"properties":560},"228ef9c4-8773-404a-b9a9-377d83afa0f8",[162],[538,550],{"id":539,"sortIndex":129,"affiliation":540,"properties":549},"72de2140-ff41-4a9a-8cc3-cd1a3962e9cf",{"id":541,"createTime":542,"updateTime":543,"relativeEntities":544,"slug":545,"properties":546,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"16a4aed9-488d-4a7c-9d2a-0363cd9ff863","2023-12-07T20:54:15.197+00:00","2025-01-02T06:38:19.116+00:00",[],"Montpellier-Business-School-Montpellier-France",{"title":547},{"VI":548},"Montpellier Business School, Montpellier, France",{},{"id":20,"sortIndex":21,"affiliation":551,"properties":20},{"id":552,"createTime":553,"updateTime":554,"relativeEntities":555,"slug":556,"properties":557,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"0c841cd2-b8e9-44b7-9aba-b70c19505ff2","2024-01-16T06:37:25.593+00:00","2025-06-12T01:20:01.124+00:00",[],"LeBow-College-of-Business-Drexel-University-Philadelphia-USA",{"title":558},{"VI":559},"LeBow College of Business, Drexel University, Philadelphia, USA",{"title":561},{"VI":562},"Shawkat Hammoudeh",{"url":502,"publisher":564,"properties":599},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":565,"slug":10,"properties":566,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":570,"manageAffiliations":571,"indexDatabases":572,"url":120,"thumbnailPath":20,"statistic":594,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":567,"eissn":568,"title":569},{"VOID":13},{"VOID":15},{"EN":17},[],[],[573,580,587],{"id":103,"indexDatabase":574,"url":79,"indexYears":20,"academicFieldIds":579,"indexDatabaseRanking":20},{"id":105,"createTime":106,"updateTime":107,"relativeEntities":575,"label":576,"description":577,"key":114,"publicationTags":578,"standard":20},[],{"EN":110,"VI":110},{"VI":112,"EN":113},[116,78],[118,119],{"id":64,"indexDatabase":581,"url":79,"indexYears":20,"academicFieldIds":586,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":582,"label":583,"description":584,"key":75,"publicationTags":585,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81],{"id":83,"indexDatabase":588,"url":96,"indexYears":97,"academicFieldIds":593,"indexDatabaseRanking":101},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":589,"label":590,"description":591,"key":93,"publicationTags":592,"standard":20},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100],{"impactFactor":21,"impactFactorByYear":595,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":123,"totalPublicationByYear":596,"totalCitation":21,"totalCitationByYear":597,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":598,"hindexLast5Year":21,"hindex":21},{},{"1997":125,"1998":126,"1999":127,"2000":125,"2001":127,"2002":128,"2003":59,"2010":129,"2011":130,"2017":129,"2020":129,"2021":129,"2022":131,"2023":129},{},{},{"volume":600,"pages":602},{"VOID":601},"54",{"VOID":603},"507-534","2018-07-26",2018,{"id":607,"createTime":608,"updateTime":609,"relativeEntities":610,"slug":611,"properties":612,"entityType":154,"verifyStatus":155,"verifyTime":609,"verifyNote":156,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":621,"fullTextUrl":20,"authors":622,"publicationType":193,"publisherRelationship":652,"citationCount":20,"citationInfo":20,"publishDate":693,"publishYear":694,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":237},"b2114bba-59b2-49dd-b414-9b7a4db3d49f","2023-12-08T08:19:38.836+00:00","2025-02-24T23:53:18.978+00:00",[],"A-logic-programming-approach-to-revealed-preference-theory",{"references":613,"abstract":615,"title":617,"doi":619},{"VOID":614},"Afriat, S., 1967, The construction of a utility function from expenditure data, Internat. Economic Rev. 8, 67–77.\nAfriat, S., 1972, The theory of international comparison of real income and prices, in Daly, J.D. (ed.) International Comparisons of Prices and Output, National Bureau of Economic Research, New York.\nAfriat, S., 1973, On a system of inequalities in demand analysis: an extension of the classical method, Internat. Economic Rev. 14, 460–472.\nChiappori, P.-A. and Rochet, J.-C., 1987, Revealed preferences and differentiable demand, Econometrica 55, 687–691.\nClocksin, W.F. and Mellish, C.S., 1984, Programming in Prolog, 2nd edn. Springer-Verlag.\nDiewert, W.E., 1973, Afriat and revealed preference theory, Rev. Economic Studies 40, 419–425.\nDiewert, W.E. and Parkan, C., 1978, Test for consistency of consumer data and nonparametric index numbers, University of British Columbia, Working Paper 78-27.\nHouthakker, H.S., 1950, Revealed preference and utility function, Economica 17, 159–174.\nKoo, A., 1963, An empirical test of revealed preference theory, Econometrica 31, 646–664.\nKoo, A., 1971, Revealed preference — a structural analysis, Econometrica 39, 89–97.\nRichter, M.K., 1966, Revealed preference theory, Econometrica 34, 635–645.\nSamuelson, P.A., 1947, Foundations of Economic Analysis, Harvard University Press, Cambridge, Mass.\nSterling, L. and Shapiro, E., 1986, The Art of Prolog: Advanced Programming Techniques, MIT Press.\nVarian, H.R., 1982, The nonparametric approach to demand analysis, Econometrica 50, 945–973.\nVarian, H.R., 1983, Nonparametric tests of consumer behavior, Rev. Economic Studies 50, 99–110.\nVarian, H.R., 1987, Testing for optimal choice behavior, University of Michigan Working Paper.",{"EN":616},"This paper shows the direct translation of revealed preference theory into a logic program. Tests of exact and approximate rationality based on the economic theory of revealed preference are presented in PROLOG. A special case of homothetic preference of the consumer behavior is also considered.",{"EN":618},"A logic programming approach to revealed preference theory",{"VOID":620},"10.1007\u002FBF00427158","http:\u002F\u002Flink.springer.com\u002F10.1007\u002FBF00427158",[623,640],{"id":624,"sortIndex":21,"researcher":20,"roles":625,"affiliations":626,"properties":637},"2be45b47-343b-4f63-b5e1-c4b206ed2155",[162],[627],{"id":20,"sortIndex":21,"affiliation":628,"properties":20},{"id":629,"createTime":630,"updateTime":631,"relativeEntities":632,"slug":633,"properties":634,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"c05f1f3e-dc13-4a75-8359-7a30ed8d17eb","2023-11-24T07:09:35.096+00:00","2024-07-24T22:45:19.069+00:00",[],"Department-of-Economics-Portland-State-University-Portland-USA",{"title":635},{"VI":636},"Department of Economics, Portland State University, Portland, USA",{"title":638},{"VI":639},"Kuan-Pin Lin",{"id":641,"sortIndex":129,"researcher":20,"roles":642,"affiliations":643,"properties":649},"3443faf4-249c-482a-b238-2b96f2037c01",[162],[644],{"id":20,"sortIndex":21,"affiliation":645,"properties":20},{"id":629,"createTime":630,"updateTime":631,"relativeEntities":646,"slug":633,"properties":647,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":648},{"VI":636},{"title":650},{"VI":651},"Stan Perry",{"url":621,"publisher":653,"properties":688},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":654,"slug":10,"properties":655,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":659,"manageAffiliations":660,"indexDatabases":661,"url":120,"thumbnailPath":20,"statistic":683,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":656,"eissn":657,"title":658},{"VOID":13},{"VOID":15},{"EN":17},[],[],[662,669,676],{"id":103,"indexDatabase":663,"url":79,"indexYears":20,"academicFieldIds":668,"indexDatabaseRanking":20},{"id":105,"createTime":106,"updateTime":107,"relativeEntities":664,"label":665,"description":666,"key":114,"publicationTags":667,"standard":20},[],{"EN":110,"VI":110},{"VI":112,"EN":113},[116,78],[118,119],{"id":64,"indexDatabase":670,"url":79,"indexYears":20,"academicFieldIds":675,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":671,"label":672,"description":673,"key":75,"publicationTags":674,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81],{"id":83,"indexDatabase":677,"url":96,"indexYears":97,"academicFieldIds":682,"indexDatabaseRanking":101},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":678,"label":679,"description":680,"key":93,"publicationTags":681,"standard":20},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100],{"impactFactor":21,"impactFactorByYear":684,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":123,"totalPublicationByYear":685,"totalCitation":21,"totalCitationByYear":686,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":687,"hindexLast5Year":21,"hindex":21},{},{"1997":125,"1998":126,"1999":127,"2000":125,"2001":127,"2002":128,"2003":59,"2010":129,"2011":130,"2017":129,"2020":129,"2021":129,"2022":131,"2023":129},{},{},{"volume":689,"pages":691},{"VOID":690},"1",{"VOID":692},"97-111","1988-01-01",1988,{"id":696,"createTime":697,"updateTime":698,"relativeEntities":699,"slug":700,"properties":701,"entityType":154,"verifyStatus":155,"verifyTime":698,"verifyNote":156,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":710,"fullTextUrl":20,"authors":711,"publicationType":193,"publisherRelationship":753,"citationCount":20,"citationInfo":20,"publishDate":794,"publishYear":795,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":237},"a3c75a53-d310-43d3-a804-7685c2d6e66c","2023-12-12T22:35:43.663+00:00","2025-02-04T23:53:12.712+00:00",[],"Exploring-US-Business-Cycles-with-Bivariate-Loops-Using-Penalized-Spline-Regression",{"references":702,"abstract":704,"title":706,"doi":708},{"VOID":703},"Akaike H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6): 716–723. doi:10.1109\u002FTAC.1974.1100705 MR0423716.\nAkritas M., Politis D. (2003) Recent advances and trends in nonparametric statistics. Elsevier, Amsterdam\nAtkinson A. (1969) The timescale of economic models: How long is the long run?. Review of Economic Studies 36: 137–152\nBaxter M., King R. (1999) Measuring business cycles: Approximate band-pass filters for economic time series. The Review of Economics and Statistics 81(4): 575–593\nBreslow N. E., Clayton D. G. (1993) Approximate inference in generalized linear mixed model. Journal of the American Statistical Association 88: 9–25\nBrockwell P. J., Davis R. A. (1987) Time series: Theory and methods. Springer, Berlin\nBurns A. F., Mitchell W. C. (1946) Measuring business cycles. National Bureau of Economic Research, New York\nChiarella C., Flaschel P., Franke R. (2005) Foundations for a disequilibrium theory of the business cycle. Qualitative analysis and quantitative assessment. Cambridge University Press, Cambridge\nChristiano L. J., Fitzgerald T. J. (2003) The band pass filter. International Economic Review 44(2): 435–465\nCurrie I., Durban M. (2002) Flexible smoothing with P-splines: A unified approach. Statistical Modelling 2: 333–349\nde Boor C. (1978) A practical guide to splines. Springer, Berlin\nDurban M., Currie I. (2003) A note on P-spline additive models with correlated errors. Computational Statistics 18: 251–262\nEilers P., Marx B. (1996) Flexible smoothing with b-splines and penalties. Statistical Science 11(2): 89–121\nEinbeck J., Tutz G., Evers L. (2005) Local principle curves. Statistics and Computing 15(4): 301–313\nEubank R. L. (1989) Spline smoothing and nonparametric regression. Dekker, New York\nFan J., Gijbels I. (1996) Local polynomial modelling and its applications. Chapman & Hall, London\nFan J., Yao Q. (2003) Nonlinear time series: Nonparametric and parametric methods. Springer, New York\nFisher N. (1995) Statistical analysis of circular data (3rd ed.). Cambridge University Press, Cambridge\nFlaschel, P., Kauermann, G., & Teuber, T. (2008). Long cycles in employment, inflation and real unit wage costs. Qualitative analysis and quantitative assessment. American Journal of Applied Sciences, 69–77.\nFriedman M. (1968) The role of monetary policy. American Economic Review 58: 1–17\nGallegati, M., Ramsey, J. B., & Semmler, W. (2006). The decomposition of the inflation-unemployment relationship by time scale using wavelets. In C. Chiarella, R. Franke, P. Flaschel, & W. Semmler (Eds.), Quantitative and empirical analysis on nonlinear dynamic macromodels, Vol. 277 of Quantitative and empirical analysis of nonlinear dynamic models (Chap. 4, pp. 93–112). Amsterdam: Elsevier.\nGoodwin R. (1967) A growth cycle. In: Feinstein C. (eds) Socialism, capitalism and economic growth. Cambridge University Press, Cambridge, pp 54–58\nHärdle W., Marron J. S. (1991) Bootstrap simultaneous error bars for nonparametric regression. The Annals of Statistics 19(2): 778–796\nHarvey A., Jaeger A. (1993) Detrending, stylized facts and the business cycle. Journal of Applied Econometrics 8: 231–247\nHastie T., Stützle W. (1989) Principle curves. Journal of the American Statistical Association 84: 502–516\nHastie T., Tibshirani R. (1990) Generalized additive models. Chapman and Hall, London\nHärdle W., Lütkepohl H., Chen R. (1997) A review of nonparametric time series analysis. International Statistical Review 65: 49–72\nHodrick R., Prescott E. (1997) Postwar U.S. Business Cycles: An empirical investigation. Journal of Money, Credit, and Banking 29: 1–16\nKauermann G. (2005) Penalised spline fitting in multivariable survival models with varying coefficients. Computational Statistics and Data Analysis 49: 169–186\nKauermann G., Krivobokova T., Fahrmeir L. (2009) Some asymptotic results on generalized penalized spline smoothing. Journal of the Royal Statistical Society, Series B 71(2): 487–503\nKauermann, G., Krivobokova, T., & Semmler, W. (2011). Filtering time series with penalized splines. Studies in Nonlinear Dynamics and Econometrics, 15(2), Article 2.\nKoopmans T. C. (1947) Measurement without theory. Review of Economic Statistics 29: 161–172\nKrivobokova T., Kauermann G. (2007) A note on penalized spline smoothing with correlated errors. Journal of the American Statistical Association 102: 1328–1337\nKydland, F. E., & Prescott, E. C. (1990). Business cycles: Real facts and a monetary myth. Federal Reserve Bank of Minneapolis Quarterly Review, 14(Spring), 3–18.\nLi Y., Ruppert D. (2008) On the asymptotics of penalized splines. Biometrika 95: 415–436\nLindstrom M., Bates D. (1990) Nonlinear mixed-effects models for repeated measures data. Biometrics 46: 673–687\nLong J., Plosser C. (1983) Real business cycles. Journal of Political Economy 91(1): 39–69\nLotka A. (1925) Elements of physical biology. Williams and Wilkins Co, Baltimore\nLucas, R. E. J. (1977). Understanding business cycles. In: K. Brunner, & A. H. Meltzer (Ed.) Stabilization of the domestic and international economy, Vol. 5 of Carnegie–Rochester Conference series on Public policy (pp. 7–29). Amsterdam: North-Holland.\nMao W., Zhao L. (2003) Free-knot polynomial splines with confidence intervals. Journal of the Royal Statistical Society, Series B 65: 901–919\nOpsomer J., Wang Y., Yang Y. (2001) Nonparametric regression with correlated errors. Statistical Science 16: 134–153\nO’Sullivan F. (1986) A statistical perspective on ill-posed inverse problems (c\u002Fr: P519–527). Statistical Science 1: 502–518\nPaige R., Trindade A. (2010) The Hodrick–Prescott filter: A special case of penalized spline smoothing. Electronic Journal of Statistics 4: 856–874\nPedregal D., Young P. (2001) Some comments on the use and abuse of the Hodrick–Prescott filter. Review on Economic Cycles, International Association of Economic Cycles 3(1): 93–104\nProietti T. (2005) Forecasting and signal extraction with misspecified models. Journal of Forecasting 24: 539–556\nRuppert D. (2002) Selecting the number of knots for penalized splines. Journal of Computational and Graphical Statistics 11: 735–757\nRuppert D. (2004) Statistics and finance—An introduction. Springer, New York\nRuppert D., Wand M., Carroll R. (2003) Semiparametric regression. Cambridge University Press, Cambridge\nRuppert D., Wand M., Carroll R. (2009) Semiparametric regression during 2003–2007. Journal of the American Statistical Association 3: 1193–1256\nSchlicht E. (2005) Estimating the smoothing parameter in the so-called Hodrick–Prescott filter. Journal of the Japanese Statistical Society 35(1): 99–119\nSolow R. (1990) Goodwin’s growth cycle: Reminiscence and rumination. In: Velupillai K. (eds) Nonlinear and multisectoral macrodynamics. Essays in Honour of Richard Goodwin. Macmillan, London, pp 31–41\nStock J. H., Watson M. W. (1999) Business cycle fluctuations in us macroeconomic Vol. 1A. In: Taylor J. B., Woodford M. (eds) Handbook of macroeconomics. Elsevier, North-Holland, pp 3–64\nTschernig R. (2004) Nonparametric time series modelling. In: Lütkepohl H., Krätzig M. (eds) Applied time series econometrics. Cambridge University Press, New York\nVolterra V. (1926) Fluctuations in the abundance of a species considered mathematically. Nature 118: 558–560\nWahba G. (1990) Spline models for observational data. SIAM, Philadelphia\nWand M. (2003) Smoothing and mixed models. Computational Statistics 18: 223–249\nWelham S., Cullis B., Kenward M., Thompson R. (2006) The analysis of longitudinal data using mixed model L-splines. Biometrics 62: 392–401\nWood S. N. (2006) Generalized additive models: An introduction with R. Chapman and Hall\u002FCRC, Boca Raton",{"EN":705},"The phrase business cycle is usually used for short term fluctuations in macroeconomic time series. In this paper we focus on the estimation of business cycles in a bivariate manner by fitting two series simultaneously. The underlying model is thereby nonparametric in that no functional form is prespecified but smoothness of the functions are assumed. The functions are then estimated using penalized spline estimation. The bivariate approach will allow to compare business cycles, check and compare phase lengths and visualize this in forms of loops in a bivariate way. Moreover, the focus is on separation of long and short phase fluctuation, where only the latter is the classical business cycle while the first is better known as Friedman or Goodwin cycle, respectively. Again, we use nonparametric models and fit the functional shape with penalized splines. For the separation of long and short phase components we employ an Akaike criterion.",{"EN":707},"Exploring US Business Cycles with Bivariate Loops Using Penalized Spline Regression",{"VOID":709},"10.1007\u002Fs10614-011-9262-2","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10614-011-9262-2",[712,729,741],{"id":713,"sortIndex":130,"researcher":20,"roles":714,"affiliations":715,"properties":726},"e4b47147-b1b0-419e-a2d0-5021206a0b75",[162],[716],{"id":20,"sortIndex":21,"affiliation":717,"properties":20},{"id":718,"createTime":719,"updateTime":720,"relativeEntities":721,"slug":722,"properties":723,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"563ec586-61f8-4525-be60-a0ce8b672fd5","2024-01-14T03:16:31.837+00:00","2026-06-19T01:24:21.400+00:00",[],"Bielefeld-University-Bielefeld-Germany",{"title":724},{"VI":725},"Bielefeld University, Bielefeld, Germany",{"title":727},{"VI":728},"Peter Flaschel",{"id":730,"sortIndex":21,"researcher":20,"roles":731,"affiliations":732,"properties":738},"53b4b714-acf0-46fa-a17d-dd272618c60c",[162],[733],{"id":20,"sortIndex":21,"affiliation":734,"properties":20},{"id":718,"createTime":719,"updateTime":720,"relativeEntities":735,"slug":722,"properties":736,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":737},{"VI":725},{"title":739},{"VI":740},"Göran Kauermann",{"id":742,"sortIndex":129,"researcher":20,"roles":743,"affiliations":744,"properties":750},"f0c2275b-ac4d-4a88-9b11-c73d6147b052",[162],[745],{"id":20,"sortIndex":21,"affiliation":746,"properties":20},{"id":718,"createTime":719,"updateTime":720,"relativeEntities":747,"slug":722,"properties":748,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":749},{"VI":725},{"title":751},{"VI":752},"Timo Teuber",{"url":710,"publisher":754,"properties":789},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":755,"slug":10,"properties":756,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":760,"manageAffiliations":761,"indexDatabases":762,"url":120,"thumbnailPath":20,"statistic":784,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":757,"eissn":758,"title":759},{"VOID":13},{"VOID":15},{"EN":17},[],[],[763,770,777],{"id":103,"indexDatabase":764,"url":79,"indexYears":20,"academicFieldIds":769,"indexDatabaseRanking":20},{"id":105,"createTime":106,"updateTime":107,"relativeEntities":765,"label":766,"description":767,"key":114,"publicationTags":768,"standard":20},[],{"EN":110,"VI":110},{"VI":112,"EN":113},[116,78],[118,119],{"id":64,"indexDatabase":771,"url":79,"indexYears":20,"academicFieldIds":776,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":772,"label":773,"description":774,"key":75,"publicationTags":775,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81],{"id":83,"indexDatabase":778,"url":96,"indexYears":97,"academicFieldIds":783,"indexDatabaseRanking":101},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":779,"label":780,"description":781,"key":93,"publicationTags":782,"standard":20},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100],{"impactFactor":21,"impactFactorByYear":785,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":123,"totalPublicationByYear":786,"totalCitation":21,"totalCitationByYear":787,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":788,"hindexLast5Year":21,"hindex":21},{},{"1997":125,"1998":126,"1999":127,"2000":125,"2001":127,"2002":128,"2003":59,"2010":129,"2011":130,"2017":129,"2020":129,"2021":129,"2022":131,"2023":129},{},{},{"volume":790,"pages":792},{"VOID":791},"39",{"VOID":793},"409-427","2011-03-25",2011,{"id":797,"createTime":798,"updateTime":799,"relativeEntities":800,"slug":801,"properties":802,"entityType":154,"verifyStatus":155,"verifyTime":799,"verifyNote":156,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":811,"fullTextUrl":20,"authors":812,"publicationType":193,"publisherRelationship":828,"citationCount":20,"citationInfo":20,"publishDate":869,"publishYear":870,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":237},"853a6fd2-2ab8-4e2f-950a-beb5a5b1a762","2024-02-06T01:16:34.247+00:00","2025-01-03T23:52:01.464+00:00",[],"Production-Games-under-Uncertainty",{"references":803,"abstract":805,"title":807,"doi":809},{"VOID":804},"Allen, B. (1996). Cooperative theory with incomplete information. Federal Reserve Bank of Minneapolis Research Department Staff Report 225, December.\nBondareva, O.N. (1962). The Core of an n-person game (in Russian). Vestnik Leningradskogo Universiteta, Seriia Matematika, Mekaniki i Astronomii, 13, 141–142.\nCharnes, A. and Granot, D. (1973). Prior solutions: Extensions of convex nucleus solutions to chance-constrained games. Proceedings of the Computer Science and Statistics Seventh Symposium at Iowa State University, Oct., pp. 323–332.\nCharnes, A. and Granot, D. (1976). Coalitional and chance-constrained solutions to n-person games, I: The prior satisficing nucleolus. SIAM Journal of Applied Mathematics, 31(2), 358–367.\nCharnes, A. and Granot, D. (1977). Coalitional and chance-constrained solutions to n-person games, II: Two-stage solutions. Operations Research, 25(6), 1013–1019.\nGranot, D. (1986). A generalized linear production model: A unifying model. Mathematical Programming, 43, 212–222.\nGranot, D. (1977). Cooperative games in stochastic characteristic function form. Management Science, 23(6), 621–630.\nKall, P. and Wallace, S.W. (1994). Stochastic Programming. John Wiley & Sons, Chichester.\nKalai, E. and Zemel, E. (1982). Generalized network problems yielding totally balanced games. Operations Research, 30(5), 998–1008.\nMyerson, R.B. (1984). Cooperative games with incomplete information. International Journal of Game Theory, 13(2), 69–96.\nOwen, G. (1975). On the core of linear production games. Mathematical Programming, 9, 358–370.\nRavindran, A., Phillips, D.T., and Solberg, J.J. (1987). Operations Research. 2nd ed., John Wiley & Sons, New York.\nRockafellar, R.T. (1970). Convex Analysis. Princeton University Press, Princeton.\nSamet, D. and Zemel, E. (1994). On the core and dual set of linear programming games. Mathematics of Opreations Research, 9(2), 309–316.\nShapley, L.S. (1967). On balanced sets and cores. Naval Research Logistics Quarterly, 14, 453–460.\nShubik, M. (1982). Game Theory in the Social Sciences. MIT Press, Cambridge, Massachusetts.\nSuijs, J. (1998). Cooperative Decision Making in a Stochastic Environment. Ph.D. Dissertation, Center for Economic Research, Tilburg University, The Netherlands.\nWilliams, H.P. (1985). Model Building in Mathematical Programming. 2nd ed., John Wiley & Sons, Chichester.\nWilson, R.B. (1978). Information, efficiency and the core of an economy. Econometrica, 46, 807–816.\nYannelis, N.C. (1991). The core of an economy with differential information. Economic Theory, 1, 183–198.",{"EN":806},"The main objects below are transferable-utility games in which each agent faces an optimization problem, briefly called production planning, constrained by his resource endowment. Coalitions can pool members' resources. Such production games are here extended to accommodate uncertainty about events not known ex ante. Planning then takes the form of two-stage stochastic programming. Core solutions are sought, described, and computed via aggregate dual programs. The analysis is motivated by practical applications. Examples include stochastic production and regional distribution with random demand and supply, illustrated by a numerical example.",{"EN":808},"Production Games under Uncertainty",{"VOID":810},"10.1023\u002FA:1008720525884","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1008720525884",[813],{"id":814,"sortIndex":21,"researcher":20,"roles":815,"affiliations":816,"properties":825},"2cdd6aab-2963-4851-a422-5f02925ba33f",[162],[817],{"id":20,"sortIndex":21,"affiliation":818,"properties":20},{"id":819,"createTime":820,"updateTime":820,"relativeEntities":821,"slug":20,"properties":822,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"b7859eaf-d190-4550-b3c5-8a1cef48fa2e","2024-02-06T01:16:34.267+00:00",[],{"title":823},{"VI":824},"Department of Economics, University of Bergen, Norway E-mail",{"title":826},{"VI":827},"Maria Sandsmark",{"url":811,"publisher":829,"properties":864},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":830,"slug":10,"properties":831,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":835,"manageAffiliations":836,"indexDatabases":837,"url":120,"thumbnailPath":20,"statistic":859,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":832,"eissn":833,"title":834},{"VOID":13},{"VOID":15},{"EN":17},[],[],[838,845,852],{"id":103,"indexDatabase":839,"url":79,"indexYears":20,"academicFieldIds":844,"indexDatabaseRanking":20},{"id":105,"createTime":106,"updateTime":107,"relativeEntities":840,"label":841,"description":842,"key":114,"publicationTags":843,"standard":20},[],{"EN":110,"VI":110},{"VI":112,"EN":113},[116,78],[118,119],{"id":64,"indexDatabase":846,"url":79,"indexYears":20,"academicFieldIds":851,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":847,"label":848,"description":849,"key":75,"publicationTags":850,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81],{"id":83,"indexDatabase":853,"url":96,"indexYears":97,"academicFieldIds":858,"indexDatabaseRanking":101},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":854,"label":855,"description":856,"key":93,"publicationTags":857,"standard":20},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100],{"impactFactor":21,"impactFactorByYear":860,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":123,"totalPublicationByYear":861,"totalCitation":21,"totalCitationByYear":862,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":863,"hindexLast5Year":21,"hindex":21},{},{"1997":125,"1998":126,"1999":127,"2000":125,"2001":127,"2002":128,"2003":59,"2010":129,"2011":130,"2017":129,"2020":129,"2021":129,"2022":131,"2023":129},{},{},{"volume":865,"pages":867},{"VOID":866},"14",{"VOID":868},"237-253","1999-12-01",1999,{"id":872,"createTime":873,"updateTime":874,"relativeEntities":875,"slug":876,"properties":877,"entityType":154,"verifyStatus":155,"verifyTime":874,"verifyNote":156,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":888,"fullTextUrl":20,"authors":889,"publicationType":193,"publisherRelationship":945,"citationCount":20,"citationInfo":20,"publishDate":981,"publishYear":982,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":237},"5d398a7e-d469-431c-a4fe-59727490cd6f","2024-04-06T17:48:31.081+00:00","2025-01-30T23:51:26.700+00:00",[],"Debt-Stabilisation-and-Dynamic-Interaction-Between-Monetary-Authority-and-National-Fiscal-Authorities",{"references":878,"keywords":880,"abstract":882,"title":884,"doi":886},{"VOID":879},"Bacchiocchi, A., & Giombini, G. (2021). An optimal control problem of monetary policy. Discrete and Continuous Dynamical Systems B, 26(11), 5769–5786.\nBacchiocchi, A., Bellocchi, A., Bischi, G.I., Travaglini, G. A non-linear model of publicdebt with bonds and money finance. Economia Politica (2023). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40888-023-00310-1\nBarsky, R., Justiniano, A., & Melosi, L. (2014). The natural rate of interest and its usefulness for monetary policy. American Economic Review, 104(5), 37–43.\nBeetsma, R., & Bovenberg, L. (1999). Does monetary unification lead to excessive debt accumulation? Journal of Public Economics, 74(3), 299–325.\nBeetsma, R., & Bovenberg, L. (2005). Structural distortions and decentralized fiscal policies in EMU. Journal of Money, Credit and Banking, 37(6), 1001–1018.\nBeetsma, R., & Bovenberg, L. (2006). Political shocks and public debt: The case for a conservative central bank revisited. Journal of Economic Dynamics and Control, 30(11), 1857–1883.\nBlanchard, O. (2017). Macroeconomics. Pearson Education Limited.\nBlanchard, O., & Galí, J. (2007). Real wage rigidities and the New Keynesian model. Journal of Money, Credit and Banking, 39(s1), 35–65.\nBlanchard, O., Leandro, A., Merler, S., & Zettelmeyer, J. (2018). Impact of Italy’s Draft Budget on Growth and Fiscal Solvency. Working Paper Policy Brief, Peterson Institute for International Economics (PIIE), PB18-24.\nBofinger, P., & Mayer, E. (2007). Monetary and fiscal policy interaction in the Euro area with different assumptions on the Phillips curve. Open Economies Review, 18(3), 291–305.\nBofinger, P., Mayer, E., & Wollmershäuser, T. (2006). The BMW model: A new framework for teaching monetary economics. Journal of Economic Education, 37(1), 98–117.\nBrand, C., Bielecki, M., & Penalver, A. (2018). The natural rate of interest: Estimates, drivers, and challenges to monetary policy. Working Paper Occasional Paper, European Central Bank (ECB), December 217.\nChortareas, G., & Mavrodimitrakis, C. (2021). Policy conflict, coordination, and leadership in a monetary union under imperfect instrument substitutability. Journal of Economic Behavior & Organization, 183(March), 342–361.\nDixit, A., & Lambertini, L. (2001). Monetary–fiscal policy interactions and commitment versus discretion in a monetary union. European Economic Review, 45(4–6), 977–987.\nDixit, A., & Lambertini, L. (2003). Symbiosis of monetary and fiscal policies in a monetary union. Journal of International Economics, 60(2), 235–247.\nDixit, A., & Lambertini, L. (2003). Interactions of commitment and discretion in monetary and fiscal policies. American Economic Review, 93(5), 1522–1542.\nFadali, S., & Fisioli, A. (2013). Digital control engineering analysis and design. Elsevier.\nForesti, P. (2015). Monetary and debt-concerned fiscal policies interaction in monetary unions. International Economics and Economic Policy, 12, 541–552.\nForesti, P. (2018). Monetary and fiscal policies interaction in monetary unions. Journal of Economic Surveys, 32(1), 226–248.\nKempf, H., & Von Thadden, L. (2013). When do cooperation and commitment matter in a monetary union? Journal of International Economics, 91(2), 252–262.\nMavrodimitrakis, C. (2022). Debt stabilization and financial stability in a monetary union: Market versus authority-based preventive solutions. International Journal of Finance & Economics, 27(2), 2582–2599.\nPurificato, F., & Sodini, M. (2023). Debt stabilisation and dynamic interaction between monetary and fiscal policy: In medio stat virtus. Communications in Nonlinear Science and Numerical Simulation, 118, 106980.\nPuu, T. (1991). Chaos in duopoly pricing. Chaos, Solitons and Fractals, 1(6), 573–581.\nTabellini, G. (1986). Money, debt and deficits in a dynamic game. Journal of Economic dynamics and Control, 10(4), 427–442.\nWoodford, M. (2003). Interest and prices. Foundations of a theory of monetary policy. Princeton University Press.",{"EN":881},"",{"EN":883},"The main aim of the present research is to consider a monetary union’s economy consisting of N countries, N fiscal authorities (one for each country) and a single monetary authority. The fiscal authorities want to stabilise output and public debt through the primary government balance, and they can exhibit heterogeneous preferences about the trade-off between output and debt stability. Unlike these, the monetary authority has the aim of price and output stability. They play a non-cooperative policy game, in which they independently and simultaneously choose monetary and fiscal instruments to pursue their goals. In a dynamic setting, each authority must choose its policy instrument prevailing in the next period without knowing—at the end of each period—the choice of other authorities. By assuming static expectations, the present work shows the possibility of several dynamic outcomes. First, there exists one Nash equilibrium representing the optimal level for the macro economy; this equilibrium is stable if the average weight that fiscal authorities assign to output stability is not excessively high; therefore, this result holds even if some authorities are less willing to promote debt stabilisation. Second, in addition to this equilibrium, there exist other Nash equilibria representing steady-state values for macroeconomic variables that differ from the targets adopted by the authorities; these equilibria emerge and are stable if the authorities’ preference for output stability is even greater and with a higher degree of heterogeneity compared to the previous case. Third, the parameters of the model matter to determine the stability properties of the equilibria, and the analysis shows the possibility of nonlinear dynamics.",{"EN":885},"Debt Stabilisation and Dynamic Interaction Between Monetary Authority and National Fiscal Authorities",{"VOID":887},"10.1007\u002Fs10614-024-10561-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10614-024-10561-0",[890,907,923],{"id":891,"sortIndex":129,"researcher":20,"roles":892,"affiliations":893,"properties":904},"73f6eae9-aa21-4dfe-95b9-776484414072",[162],[894],{"id":20,"sortIndex":21,"affiliation":895,"properties":20},{"id":896,"createTime":897,"updateTime":898,"relativeEntities":899,"slug":900,"properties":901,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"020366a5-f597-425a-a87e-b5c8cf565ed6","2024-04-06T17:48:31.113+00:00","2025-06-11T15:26:40.487+00:00",[],"Department-of-Law-University-of-Naples-Federico-II-Naples-Italy",{"title":902},{"VI":903},"Department of Law, University of Naples “Federico II”, Naples, Italy",{"title":905},{"VI":906},"Francesco Purificato",{"id":908,"sortIndex":21,"researcher":20,"roles":909,"affiliations":910,"properties":920},"33f29090-9431-42d8-a13c-14dcc4810726",[162],[911],{"id":20,"sortIndex":21,"affiliation":912,"properties":20},{"id":913,"createTime":914,"updateTime":914,"relativeEntities":915,"slug":916,"properties":917,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"ee138b97-8fe4-41e4-9c8f-14beaf3fdc3b","2024-04-06T17:48:31.091+00:00",[],"Department-of-Law-University-of-Pisa-Pisa-Italy",{"title":918},{"VI":919},"Department of Law, University of Pisa, Pisa, Italy",{"title":921},{"VI":922},"Luca Gori",{"id":924,"sortIndex":130,"researcher":20,"roles":925,"affiliations":926,"properties":942},"b84e580c-8448-47ea-905d-d6fc08c9ac51",[162],[927,932],{"id":20,"sortIndex":21,"affiliation":928,"properties":20},{"id":896,"createTime":897,"updateTime":898,"relativeEntities":929,"slug":900,"properties":930,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":931},{"VI":903},{"id":20,"sortIndex":21,"affiliation":933,"properties":20},{"id":934,"createTime":935,"updateTime":936,"relativeEntities":937,"slug":938,"properties":939,"entityType":48,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"0bda74a7-64f6-4f6f-a35c-f330dcaf872f","2024-04-06T17:48:31.117+00:00","2025-06-12T00:42:19.118+00:00",[],"Department-of-Finance-Faculty-of-Economics-Technical-University-of-Ostrava-Ostrava-Czech-Republic",{"title":940},{"VI":941},"Department of Finance, Faculty of Economics, Technical University of Ostrava, Ostrava, Czech Republic",{"title":943},{"VI":944},"Mauro Sodini",{"url":20,"publisher":946,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":947,"slug":10,"properties":948,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":952,"manageAffiliations":953,"indexDatabases":954,"url":120,"thumbnailPath":20,"statistic":976,"gsStatistic":20,"type":134,"analyzePriority":20},[],{"issn":949,"eissn":950,"title":951},{"VOID":13},{"VOID":15},{"EN":17},[],[],[955,962,969],{"id":103,"indexDatabase":956,"url":79,"indexYears":20,"academicFieldIds":961,"indexDatabaseRanking":20},{"id":105,"createTime":106,"updateTime":107,"relativeEntities":957,"label":958,"description":959,"key":114,"publicationTags":960,"standard":20},[],{"EN":110,"VI":110},{"VI":112,"EN":113},[116,78],[118,119],{"id":64,"indexDatabase":963,"url":79,"indexYears":20,"academicFieldIds":968,"indexDatabaseRanking":20},{"id":66,"createTime":67,"updateTime":68,"relativeEntities":964,"label":965,"description":966,"key":75,"publicationTags":967,"standard":20},[],{"EN":71,"VI":71},{"VI":73,"EN":74},[77,78],[81],{"id":83,"indexDatabase":970,"url":96,"indexYears":97,"academicFieldIds":975,"indexDatabaseRanking":101},{"id":85,"createTime":86,"updateTime":87,"relativeEntities":971,"label":972,"description":973,"key":93,"publicationTags":974,"standard":20},[],{"EN":90,"VI":90},{"EN":90,"VI":92},[95],[99,100],{"impactFactor":21,"impactFactorByYear":977,"i10Index":21,"i10IndexLast5Year":21,"totalPublication":123,"totalPublicationByYear":978,"totalCitation":21,"totalCitationByYear":979,"totalCitationPerPublication":21,"totalCitationPerPublicationByYear":980,"hindexLast5Year":21,"hindex":21},{},{"1997":125,"1998":126,"1999":127,"2000":125,"2001":127,"2002":128,"2003":59,"2010":129,"2011":130,"2017":129,"2020":129,"2021":129,"2022":131,"2023":129},{},{},"2024-03-16",2024,{"id":984,"createTime":985,"updateTime":986,"relativeEntities":987,"slug":988,"properties":989,"entityType":154,"verifyStatus":155,"verifyTime":986,"verifyNote":156,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":998,"fullTextUrl":20,"authors":999,"publicationType":193,"publisherRelationship":1064,"citationCount":20,"citationInfo":20,"publishDate":1104,"publishYear":1105,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":237},"06b341db-14f1-4623-afe0-29428d4827ab","2023-11-25T11:56:46.237+00:00","2025-02-17T23:50:23.030+00:00",[],"Low-Complexity-Algorithmic-Trading-by-Feedforward-Neural-Networks",{"references":990,"abstract":992,"title":994,"doi":996},{"VOID":991},"Anagnostopoulos, K. P., & Mamanis, G. (2011). The mean-variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms. Expert Systems with Applications, 38, 14208–14217.\nBanerjee, O., El Ghaoui, L., & d’Aspremont, A. (2008). Model selection through sparse maximum likelihood estimation. Journal of Machine Learning Research, 9, 485–516.\nD’Aspremont, A. (2011). Identifying small mean-reverting portfolios. Quantitative Finance, 11(3), 351–364.\nD’Aspremont, A., Banerjee, O., & El Ghaoui, L. (2008). First-order methods for sparce covariance selection. SIAM Journal on Matrix Analysis and its Applications, 30(1), 56–66.\nFunahashi, K. I. (1989). On the approximate realization of continuous mappings by neural networks. Neural Networks, 2, 183–192.\nHaykin, S. (1999). Neural Networks: A comprehensive foundation (2nd ed.). Upper Saddle River, NJ, USA: Prentice Hall PTR.\nHornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366.",{"EN":993},"In this paper, novel neural based algorithms are developed for electronic trading on financial time series. The proposed method is estimation based and trading actions are carried out after estimating the forward conditional probability distribution. The main idea is to introduce special encoding schemes on the observed prices in order to obtain an efficient estimation of the forward conditional probability distribution performed by a feedforward neural network. Based on these estimations, a trading signal is launched if the probability of price change becomes significant which is measured by a quadratic criterion. The performance analysis of our method tested on historical time series (NASDAQ\u002FNYSE stocks) has demonstrated that the algorithm is profitable. 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