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Literatura académica sobre el tema "Nonlinear Structural Vector AutoRegressions"

Autor: Grafiati

Publicado: 11 de marzo de 2023

Última modificación: 19 de julio de 2025

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Artículos de revistas sobre el tema "Nonlinear Structural Vector AutoRegressions"

1

Harris, Glen R. "Markov Chain Monte Carlo Estimation of Regime Switching Vector Autoregressions." ASTIN Bulletin 29, no. 1 (1999): 47–79. http://dx.doi.org/10.2143/ast.29.1.504606.

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AbstractFinancial time series data are typically found to possess leptokurtic frequency distributions, time varying volatilities, outliers and correlation structures inconsistent with linear generating processes, nonlinear dependence, and dependencies between series that are not stable over time. Regime Switching Vector Autoregressions are of interest because they are capable of explaining the observed features of the data, can capture a variety of interactions between series, appear intuitively reasonable, are vector processes, and are now tractable.This paper considers a vector autoregressio

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2

IWATA, SHIGERU, and SHU WU. "MACROECONOMIC SHOCKS AND THE FOREIGN EXCHANGE RISK PREMIA." Macroeconomic Dynamics 10, no. 4 (2006): 439–66. http://dx.doi.org/10.1017/s136510050606007x.

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In this paper we empirically examine the sources of the volatility of the foreign exchange risk premia. Using a nonlinear structural Vector Autoregression (VAR) model based on no-arbitrage condition to identify various macroeconomic shocks and the foreign exchange risk premia, we find that more than 80% of the volatilities of the currency risk premia can be accounted for by the standard macroeconomic shocks that drive output and inflation. By explicitly modelling the currency risk premia in the VAR system, we also offer a potential reconciliation for the seemingly contradicting observations fr

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3

Kumar, Nikeel, Ronald Ravinesh Kumar, Radika Kumar, and Peter Josef Stauvermann. "Is the tourism–growth relationship asymmetric in the Cook Islands? Evidence from NARDL cointegration and causality tests." Tourism Economics 26, no. 4 (2019): 658–81. http://dx.doi.org/10.1177/1354816619859712.

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We examine whether tourism sector development measured by visitor arrivals per capita has asymmetric growth effects in the Cook Islands using quarterly data from 2010Q1 to 2016Q3. Asymmetric cointegration, long-run elasticities, and dynamic multipliers are estimated using the nonlinear autoregressive distributed lag model developed by Shin et al. Asymmetric causality testing is done using the asymmetric vector autoregression approach with insights from Hatemi-J. We identify structural breaks using the Lee and Strazicich multiple endogenous structural break unit root test. The results indicate

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4

Iwata, Shigeru, and Shu Wu. "A NOTE ON FOREIGN EXCHANGE INTERVENTIONS AT ZERO INTEREST RATES." Macroeconomic Dynamics 16, no. 5 (2012): 802–17. http://dx.doi.org/10.1017/s1365100512000120.

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This note uses a nonlinear structural vector autoregression model to empirically investigate the effectiveness of official foreign exchange (FX) interventions in an economy when interest rates are constrained to the zero level, based on Japanese data in the 1990s. The model allows us to estimate the effects of FX interventions operating through different channels. We find that FX interventions are still capable of influencing the foreign exchange rate in a zero-interest-rate environment, even though their effects are greatly reduced by the zero lower bound on interest rates. Our results sugges

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5

Stock, James H., and Mark W. Watson. "Vector Autoregressions." Journal of Economic Perspectives 15, no. 4 (2001): 101–15. http://dx.doi.org/10.1257/jep.15.4.101.

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This paper critically reviews the use of vector autoregressions (VARs) for four tasks: data description, forecasting, structural inference, and policy analysis. The paper begins with a review of VAR analysis, highlighting the differences between reduced-form VARs, recursive VARs and structural VARs. A three variable VAR that includes the unemployment rate, price inflation and the short term interest rate is used to show how VAR methods are used for the four tasks. The paper concludes that VARs have proven to be powerful and reliable tools for data description and forecasting, but have been les

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6

Branch, William A., Troy Davig, and Bruce McGough. "ADAPTIVE LEARNING IN REGIME-SWITCHING MODELS." Macroeconomic Dynamics 17, no. 5 (2012): 998–1022. http://dx.doi.org/10.1017/s1365100511000800.

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We study adaptive learning in economic environments subject to recurring structural change. Stochastically evolving institutional and policymaking features can be described by regime-switching models with parameters that evolve according to finite state Markov processes. We demonstrate that in nonlinear models of this form, the presence of sunspot equilibria implies two natural schemes for learning the conditional means of endogenous variables: under mean value learning, agents condition on a sunspot variable that captures the self-fulfilling serial correlation in the equilibrium, whereas unde

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7

Kim, Tae Seog. "Contagion Effects of U.S. Business Cycle Regimes on the Korean Economy." Academic Society of Global Business Administration 22, no. 2 (2025): 207–36. https://doi.org/10.38115/asgba.2025.22.2.207.

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This study aims to empirically analyze the spillover effects of U.S. business cycle regime transitions on Korea’s real economy and financial markets. Using a Markov Switching Vector Autoregression (MS-VAR) model, U.S. economic regimes—expansion and recession—are identified endogenously, and the resulting regime-switching probabilities are introduced as exogenous variables into Markov Switching Regression and Structural VAR (SVAR) models. These models are used to evaluate the dynamic responses of key Korean macroeconomic indicators, including industrial production, consumer prices, unemployment

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8

Lanne, Markku, and Helmut Lütkepohl. "Structural Vector Autoregressions With Nonnormal Residuals." Journal of Business & Economic Statistics 28, no. 1 (2010): 159–68. http://dx.doi.org/10.1198/jbes.2009.06003.

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Zha, Tao. "Block recursion and structural vector autoregressions." Journal of Econometrics 90, no. 2 (1999): 291–316. http://dx.doi.org/10.1016/s0304-4076(98)00045-1.

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Lanne, Markku, Helmut Lütkepohl, and Katarzyna Maciejowska. "Structural vector autoregressions with Markov switching." Journal of Economic Dynamics and Control 34, no. 2 (2010): 121–31. http://dx.doi.org/10.1016/j.jedc.2009.08.002.

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Tesis sobre el tema "Nonlinear Structural Vector AutoRegressions"

1

Schlaak, Thore [Verfasser]. "Essays on Structural Vector Autoregressions Identified Through Time-Varying Volatility / Thore Schlaak." Berlin : Freie Universität Berlin, 2019. http://d-nb.info/1202996515/34.

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2

Pereira, Manuel Bernardo Videira Coutinho Rodrigues. "Effects of fiscal policy: measurement issues and structural change." Doctoral thesis, Instituto Superior de Economia e Gestão, 2011. http://hdl.handle.net/10400.5/3431.

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Doutoramento em Economia<br>Considerable uncertainty surrounds the macroeconomic effects of fiscal policy. The re-search presented in this dissertation firstly aims at improving on the methods used to measure such effects - which feature vector autoregressions (VARs) as the basic tool. The investigation is partly carried out using structural VARs. The methodological innova¬tions in that part concern the joint identification of fiscal shocks vis-a-vis monetary policy shocks and the estimation of a model with time-varying parameters using a non-recursive identification scheme. I also use reduced

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3

Azarskov, V. N., O. U. Kurganskyi, O. V. Ermolaeva, and G. I. Rudyuk. "Structural Identification Algorithm Based on Results of Multidimensional Nonlinear Stabilization Plant Test." Thesis, Kyiv, "Osvita Ukrainy", 2015. http://er.nau.edu.ua/handle/NAU/28365.

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Figueres, Juan Manuel. "Nonlinear Effects of Macroeconomic Shocks." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3421777.

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This thesis investigates the nonlinear macroeconomic effects of fiscal and uncertainty shocks. It comprises three contained chapters, each one of them being self-contained. In each chapter, theoretical predictions coming from theoretical models are presented and discussed. Such predictions are then tested using state-of-the-art econometric techniques. The first chapter is titled “News in State-Dependent Fiscal Multipliers: The Role of Confidence”. This study scrutinizes the role of consumer confidence in determining the real effects that unanticipated (news) government spending shocks hav

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Pellegrino, Giovanni. "Uncertainty and Monetary Policy: Assessing their Nonlinear Interactions." Doctoral thesis, 2016. http://hdl.handle.net/11562/936395.

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Questa tesi esamina le interazioni tra l'incertezza e la politica monetaria mediante l'utilizzo di metodi econometrici non lineari. Si compone di tre capitoli distinti. Il primo capitolo si occupa degli effetti degli stimoli inattesi (shock da ora in avanti) di politica monetaria condizionali a diversi livelli di incertezza. Sulla base della letteratura teorica, diverse spiegazioni si pensa siano in grado di ridurre l'efficacia della politica monetaria in tempi incerti (ad esempio, l'esistenza di opzioni reali, un differente comportamento price-maker delle imprese e un maggiore risparmio prec

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6

NETŠUNAJEV, Aleksei. "Structural vector autoregressions with Markov switching : identification via heteroskedasticity." Doctoral thesis, 2013. http://hdl.handle.net/1814/26775.

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Defence date: 27 March 2013<br>Examining Board: Professor Helmut Lütkepohl, DIW Berlin and Freie Universität (External Supervisor); Professor Fabio Canova, European University Institute; Professor Helmut Herwartz, Georg-August-Universität Göttingen; Professor Markku Lanne, University of Helsinki<br>Structural vector autoregressions are of great importance in applied macroeconometric work. The main di culty associated with structural analysis is to identify unique shocks of interest. In a conventional approach this is done via zero or sign restrictions. Heteroskedasticity is proposed for use in

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7

Seymen, Atılım [Verfasser]. "Business cycle analysis with structural vector autoregressions : four applications / by Atılım Seymen." 2009. http://d-nb.info/1000197395/34.

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8

Tai, Ru Yuh, and 戴如育. "NONLINEAR STRUCTURAL OPTIMIZATION ON THE VECTOR AND PARALLEL SUPERCOMPUTER." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/21297933897859596164.

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碩士<br>國立中山大學<br>機械工程研究所<br>82<br>The purpose of this study is to investigate the optimum design of geometrically nonlinear structure by means of multilevel optimization method with sensitivity analysis. In this study the tangent stiffness method is used to solve the nonlinear problem. The Jacobian conjugate gradient, an iterative solution algorithm, is employed to promote the performance of finite element analysis for structure. The proposed solution procedures are programmed in FORTRAN for

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9

Nodari, Gabriela Thais. "Uncertainty, Fiscal, and Financial Shocks in a Nonlinear World: Empirical Investigations." Doctoral thesis, 2015. http://hdl.handle.net/11562/909410.

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Questa tesi studia gli effetti macroeconomici degli shock di incertezza, shock fiscali e shock finanziari sul ciclo economico USA. La tesi si divide in quattro capitoli indipendenti, ognuno dei quali utilizza sistemi vettoriali autoregressivi, lineari e non-lineari, e local projections, al fine di estendere la ricerca empirica e valutare l’importanza dei diversi canali di trasmissione degli shock, così come proposti da modelli teorici. I risultati ottenuti dimostrano come tali shock hanno effetti non-lineari lungo il ciclo economico, e cioè la reazione delle variabili macroeconomiche è statis

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Σαλαμαλίκη, Παρασκευή. "Μελέτες στην εφαρμοσμένη μακροοικονομετρία : Αιτιότητα κατά Granger σε πολλαπλούς ορίζοντες και μη-γραμμικές τάσεις σε μακροοικονομικές χρονολογικές σειρές". Thesis, 2013. http://hdl.handle.net/10889/6542.

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Η παρούσα διατριβή ασχολείται με δύο ιδιαιτέρως σημαντικά και διαχρονικά επίκαιρα ζητήματα στην ανάλυση χρονολογικών σειρών, τα οποία εντάσσονται, υπό ευρεία έννοια, στο πεδίο της Μακροοικονομετρίας. Ειδικότερα, μελετώνται θέματα και μεθοδολογίες ή τεχνικές ιδιαίτερα χρήσιμες για εκείνους τους ερευνητές, οι οποίοι επικεντρώνονται στην ανάλυση της συμπεριφοράς των συναθροιστικών (aggregate) μεγεθών της οικονομίας, βασιζόμενοι στη χρήση δεδομένων χρονοσειρών ή πιο απλά χρονοσειρές (time series). Το πρώτο ζήτημα αφορά στη μελέτη της δυναμικής αλληλεξάρτησης ανάμεσα σε μακροοικονομικές μεταβλ

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Libros sobre el tema "Nonlinear Structural Vector AutoRegressions"

1

Rubio-Ramírez, Juan Francisco. Markov-Switching structural vector autoregressions: Theory and application. Federal Reserve Bank of Atlanta, 2005.

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2

Healy, Brian E. Applications of parallel and vector algorithms in nonlinear structural dynamics using the finite element method. Dept. of Civil Engineering, University of Illinois at Urbana-Champaign, 1992.

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3

F, Knight Norman, and United States. National Aeronautics and Space Administration., eds. Nonlinear structural response using adaptive dynamic relaxation on a massively-parallel-processing system. National Aeronautics and Space Administration, 1994.

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4

United States. National Aeronautics and Space Administration., ed. Parallel-vector computation for structural analysis and nonlinear unconstrained optimization problems: Final report for the period ended June 15, 1990. Old Dominion University Research Foundation, Dept. of Civil Engineering, College of Engineering & Technology, Old Dominion University, 1990.

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5

United States. National Aeronautics and Space Administration., ed. Parallel-vector computation for structural analysis and nonlinear unconstrained optimization problems: Final report for the period ended June 15, 1990. Old Dominion University Research Foundation, Dept. of Civil Engineering, College of Engineering & Technology, Old Dominion University, 1990.

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Parallel-vector computation for structural analysis and nonlinear unconstrained optimization problems: Final report for the period ended June 15, 1990. Old Dominion University Research Foundation, Dept. of Civil Engineering, College of Engineering & Technology, Old Dominion University, 1990.

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Capítulos de libros sobre el tema "Nonlinear Structural Vector AutoRegressions"

1

Fernández-Villaverde, Jesús, and Juan F. Rubio-Ramírez. "Structural Vector Autoregressions." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2633.

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Fernández-Villaverde, Jesús, and Juan F. Rubio-Ramírez. "Structural Vector Autoregressions." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2633-1.

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Fernández-Villaverde, Jesús, and Juan F. Rubio-Ramírez. "Structural vector autoregressions." In Macroeconometrics and Time Series Analysis. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_33.

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4

Brosowski, Bruno. "A Recursive Procedure for the Solution of Linear and Nonlinear Vector Optimization Problems." In Discretization Methods and Structural Optimization — Procedures and Applications. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83707-4_13.

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5

Feng, Y., Q. Wang, D. Wu, and W. Gao. "Machine Learning-Aided Nonlinear Dynamic Analysis of Engineering Structures." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3330-3_36.

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AbstractA machine learning (ML) technique was used to assist in the dynamic analysis of mixed geometric and material nonlinearities of real-life engineering structures. Various types of inputs of system properties were considered in the 3D dynamic geometric elastoplastic analysis, giving a series of realistic nonlinear descriptions of complex, large deformation structural behaviors. To resolve the numerical challenges of solving the mixed nonlinear problems, a newly established ML technique using a new cluster-based extended support vector regression (X-SVR) algorithm was applied. With this technique, a surrogate model can be built at each time step in the Newmark time integration process, which can then be used to predict the deflection, force and stress of the relevant structural performance at different loading time stages. To demonstrate the accuracy and efficiency of the proposed framework, practical engineering applications with linear and nonlinear properties are fully demonstrated, and the nonlinear behavior of the structure under predicted working conditions in the future was predicted and verified in numerical studies.

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6

Zhang, Wenbo, Xiaoyue Guo, Yunhe Zhang, Yunpeng Zhu, Bo Zhang, and Zhike Peng. "Structural Damage Detection of Cracked Beams Based on Nonlinear Output Frequency Response Functions and Support Vector Machine." In Mechanisms and Machine Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73407-6_30.

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Ran, Zhuying, and Wang Han. "Prefabricated Building Model Construction Using Artificial Intelligence Algorithms." In Novel Technology and Whole-Process Management in Prefabricated Building. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5108-2_15.

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AbstractArtificial intelligence has become a hot research topic in the field of technology worldwide today. This article will discuss a hash and genetic algorithm based model suitable for prefabricated buildings. This article first introduces the application of artificial intelligence algorithms in solving nonlinear programming problems. Then this article proposes to improve the time loss caused by vector distortion caused by similar neighborhood selection in traditional methods, and preprocess the results to improve decision-making accuracy and other characteristics. Finally, this article verifies through experiments that the model is more effective and operable than traditional algorithms under the optimization of artificial intelligence algorithms. The verification results are as follows: In terms of running speed, the performance of artificial intelligence algorithms is 43 m/s, while the performance of traditional algorithms is 24 m/s; In terms of operational efficiency, the performance result of artificial intelligence algorithms is 95%, while the performance effect of traditional algorithms is 74%; In terms of visualization level, artificial intelligence algorithms have higher performance results, while traditional algorithms have lower performance effects. In terms of reliability, the performance result of artificial intelligence algorithms is 0.53, while the performance score of traditional algorithms is 0.43; In terms of robustness, the performance of artificial intelligence algorithms is 0.74, while the performance result of traditional algorithms is 0.67. The accuracy of artificial intelligence algorithms is 84%, while the accuracy of traditional algorithms is 65%. These test results indicate that using artificial intelligence algorithms can assist designers and engineers in optimizing design, automatically generating models, and conducting structural analysis and durability verification. This method helps to reduce errors and waste in the construction process, improve building quality and construction speed.

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Stock, J. H., and M. W. Watson. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics." In Handbook of Macroeconomics. Elsevier, 2016. http://dx.doi.org/10.1016/bs.hesmac.2016.04.002.

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"11.6. Vector Autoregressions and Structural Econometric Models." In Time Series Analysis. Princeton University Press, 1994. http://dx.doi.org/10.1515/9780691218632-097.

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Lütkepohl, Helmut. "Identifying Structural Vector Autoregressions Via Changes in Volatility." In VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims. Emerald Group Publishing Limited, 2013. http://dx.doi.org/10.1108/s0731-9053(2013)0000031005.

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Actas de conferencias sobre el tema "Nonlinear Structural Vector AutoRegressions"

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Luo, Lanxin, Limin Sun, Yixian Li, and Yong Xia. "Hybrid Physics Data-driven Modeling for Structural Nonlinear Boundary Condition Identification." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0729.

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<p>The identification of nonlinear boundary conditions in operational structures is essential. Current physics-based methods only identify nonlinear parameters from known patterns, while data-driven approaches solely focus on load-response relationships at the structural level. To this end, this study introduces a hybrid physics-data-driven strategy for identifying nonlinear boundary conditions. Nonlinear behavior is modeled by multilayer perceptrons (MLPs), while linear structural elements are modeled by the finite element method. These components are assembled to form the global stiffn

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2

Xiao, Li, and Wenzhong Qu. "Nonlinear structural damage detection using support vector machines." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Tribikram Kundu. SPIE, 2012. http://dx.doi.org/10.1117/12.914688.

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Zhang, Jian, and Tadanobu Sato. "Linear and nonlinear structural identifications using the support vector regression." In Smart Structures and Materials, edited by Masayoshi Tomizuka, Chung-Bang Yun, and Victor Giurgiutiu. SPIE, 2006. http://dx.doi.org/10.1117/12.658419.

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BADDOURAH, MAJDI, and DUC NGUYEN. "GEOMETRIC ALLY NONLINEAR DESIGN SENSITIVlTY ANALYSIS ON PARALLEL-VECTOR HIGH-PERFORMANCE COMPUTERS." In 34th Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-1528.

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QIN, JIANGNING, CHUH MEI, and CARL GRAY, JR. "A vector unsymmetric eigenequation solver for nonlinear flutter analysis on high-performance computers." In 32nd Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-1169.

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Shen, Yanning, Brian Baingana, and Georgios B. Giannakis. "Topology inference of directed graphs using nonlinear structural vector autoregressive models." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953411.

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Li, Yilun, Shuangxi Guo, Yue Kong, Min Li, and Weimin Chen. "Non-Linearly Restoring Performance and its Hysteresis Behavior of Dynamic Catenary." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95651.

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Abstract Catenary is increasingly used as mooring-line and riser system as the water depth gets larger due to its lower cost and easier installment. Its dynamic response and restoring performance become more complicated, as the length of the mooring-line become larger, and the structural and fluid dynamics the mooring-line become consequently more obvious. Compared to the quasi-static method where the static restoring force is mainly involved, the dynamic behaviors and its hysteresis of the catenary mooring-line are considered here so as to comprehensively examine the non-linearly restoring pe

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Kumar, Nishant, and Thomas D. Burton. "On Combined Use of POD Modes and Ritz Vectors for Model Reduction in Nonlinear Structural Dynamics." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87416.

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An approach to develop Proper Orthogonal Decomposition (POD) based reduced order models for systems with local nonlinearities is presented in this paper. This technique is applied to multi-degree of freedom systems of coupled oscillators with isolated nonlinear elements. Typically, reduced order models are obtained using POD modes exclusively. In this work, we explore the suitability of using a combination of POD modes and other physically based “Ritz vectors” to produce the reduced model. The objectives are 1). to improve the accuracy of the reduced order differential equation model and 2). t

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Guo, Shuangxi, Yilun Li, Min Li, Weimin Chen, and Yue Kong. "Dynamic Response Analysis on Flexible Riser With Different Configurations in Deep-Water Based on FEM Simulation." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77838.

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For case of oil/gas exploitation and mining in deep water, the length of riser is pretty large and, consequently, it brings huge challenges in both offshore installation and production operations and results in significant cost elevation due to the factors such as extreme tension loads induced from riser suspended self-weight and large structural flexibility. Therefore, there are several alternative riser configurations, e.g. lazy wave, hybrid tower and lazy-wave riser beside free hanging catenary, which have been proposed. In this paper, the dynamic characteristics and responses of several ri

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Luo, Weilin, Bin Fu, Carlos Guedes Soares, and Zaojian Zou. "Robust Control for Ship Course-Keeping Based on Support Vector Machines: Particle Swarm Optimization and L2-Gain." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-11076.

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To achieve a robust autopilot for ship course-keeping, a controller based on support vector machines (SVM) and L2-gain is proposed. A cascaded plant consists of a nonlinear ship response model and the rudder actuator dynamics. Uncertainties including modeling errors and external disturbances are taken into account in the response model. SVM is applied to identify the complicated nonlinear dynamics including the modeling errors, while L2-gain design is used to suppress the uncertain disturbances. To obtain the optimized structural parameters in SVM, particle swarm optimization (PSO) method is i

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Informes sobre el tema "Nonlinear Structural Vector AutoRegressions"

1

Ludvigson, Sydney, Sai Ma, and Serena Ng. Shock Restricted Structural Vector-Autoregressions. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23225.

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2

Baumeister, Christiane, and James Hamilton. Advances in Structural Vector Autoregressions with Imperfect Identifying Information. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27014.

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3

Baumeister, Christiane, and James Hamilton. Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20741.

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4

Baumeister, Christiane, and James Hamilton. Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w26606.

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5

Read, Matthew. Estimating the Effects of Monetary Policy in Australia Using Sign-restricted Structural Vector Autoregressions. Reserve Bank of Australia, 2023. http://dx.doi.org/10.47688/rdp2022-09.

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Resumen

Existing estimates of the macroeconomic effects of Australian monetary policy tend to be based on strong, potentially contentious, assumptions. I estimate these effects under weaker assumptions. Specifically, I estimate a structural vector autoregression identified using a variety of sign restrictions, including restrictions on impulse responses to a monetary policy shock, the monetary policy reaction function, and the relationship between the monetary policy shock and a proxy for this shock. I use an approach to Bayesian inference that accounts for the problem of posterior sensitivity to the

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6

Baumeister, Christiane J. S., and James Hamilton. Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w24167.

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7

Baumeister, Christiane, and James Hamilton. Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w24597.

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8

Giacomini, Raffaella, Toru Kitagawa, and Matthew Read. Identification and Inference under Narrative Restrictions. Reserve Bank of Australia, 2023. http://dx.doi.org/10.47688/rdp2023-07.

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We consider structural vector autoregressions subject to narrative restrictions, which are inequalities involving structural shocks in specific time periods (e.g. shock signs in given quarters). Narrative restrictions are used widely in the empirical literature. However, under these restrictions, there are no formal results on identification or the properties of frequentist approaches to inference, and existing Bayesian methods can be sensitive to prior choice. We provide formal results on identification, propose a computationally tractable robust Bayesian method that eliminates prior sensitiv

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9

Read, Matthew, and Dan Zhu. Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions. Reserve Bank of Australia, 2025. https://doi.org/10.47688/rdp2025-03.

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We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on 'soft' sign restrictions. This step draws from a target density that smoothly penalises parameter values violating the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws from the desired distribution conditional on the 'hard' sign restrictions. Relative to standard accept-reject sampling, the method substantia

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10

Granado, Camilo, and Daniel Parra-Amado. Estimating the Output Gap After COVID: How to Address Unprecedented Macroeconomic Variations. Banco de la República, 2023. http://dx.doi.org/10.32468/be.1249.

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This study examines whether and how important it is to adjust output gap frameworks during the COVID-19 pandemic and similar unprecedentedly large-scale episodes. Our proposed modelling framework comprises a Bayesian Structural Vector Autoregressions with an identification setup based on a permanent-transitory decomposition that exploits the long-run relationship of consumption with output and whose residuals are scaled up around the COVID-19 period. Our results indicate that (i) a single structural error is usually sufficient to explain the permanent component of the gross domestic product (G

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