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Journal articles on the topic 'Reduced form Models'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Koivusalo, Alexander, and Rudi Schäfer. "Calibration of structural and reduced-form recovery models." Journal of Credit Risk 8, no. 4 (December 2012): 31–51. http://dx.doi.org/10.21314/jcr.2012.151.

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2

Berndt, Antje. "A Credit Spread Puzzle for Reduced-Form Models." Review of Asset Pricing Studies 5, no. 1 (March 24, 2015): 48–91. http://dx.doi.org/10.1093/rapstu/rav002.

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3

Rode, David C., and Paul S. Fischbeck. "Reduced-form models for power market risk analysis." Applied Energy 228 (October 2018): 1640–55. http://dx.doi.org/10.1016/j.apenergy.2018.07.044.

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4

Ravenna, Federico. "Vector autoregressions and reduced form representations of DSGE models." Journal of Monetary Economics 54, no. 7 (October 2007): 2048–64. http://dx.doi.org/10.1016/j.jmoneco.2006.09.002.

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5

Sorge, Marco Maria. "Indeterminacy and fundamental reduced form representations of DSGE models." Metroeconomica 69, no. 2 (January 22, 2018): 509–24. http://dx.doi.org/10.1111/meca.12198.

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6

Maasoumi, Esfandiar. "Reduced form estimation and prediction from uncertain structural models." Journal of Econometrics 31, no. 1 (February 1986): 3–29. http://dx.doi.org/10.1016/0304-4076(86)90053-9.

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7

Pellegrini, Santiago, Esther Ruiz, and Antoni Espasa. "Conditionally heteroscedastic unobserved component models and their reduced form." Economics Letters 107, no. 2 (May 2010): 88–90. http://dx.doi.org/10.1016/j.econlet.2009.12.034.

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8

Schneeweiss, Hans. "The reduced form of recursive models: Small sample properties." Linear Algebra and its Applications 237-238 (April 1996): 277–300. http://dx.doi.org/10.1016/0024-3795(95)00527-7.

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9

Boughton, James M. "Tests of the performance of reduced-form exchange rate models." Journal of International Economics 23, no. 1-2 (August 1987): 41–56. http://dx.doi.org/10.1016/s0022-1996(87)80004-1.

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10

Kiviet, Jan F., Garry D. A. Phillips, and Bernhard Schipp. "Alternative bias approximations in first-order dynamic reduced form models." Journal of Economic Dynamics and Control 23, no. 7 (June 1999): 909–28. http://dx.doi.org/10.1016/s0165-1889(98)00055-4.

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11

Hitzemann, Steffen, and Marliese Uhrig-Homburg. "Empirical performance of reduced-form models for emission permit prices." Review of Derivatives Research 22, no. 3 (January 11, 2019): 389–418. http://dx.doi.org/10.1007/s11147-018-09152-7.

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12

Perdang, J. "Reduced normal form as minimal models for nonlinear radial pulsations." Astrophysics and Space Science 220, no. 1 (1994): 1–37. http://dx.doi.org/10.1007/bf00628073.

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13

Phelps, Bruce D. "Reduced Form vs. Structural Models of Credit Risk: A Case Study of Three Models." CFA Digest 36, no. 2 (May 2006): 36–38. http://dx.doi.org/10.2469/dig.v36.n2.4103.

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14

Calzolari, Giorgio, and Frederic P. Sterbenz. "Control Variates to Estimate the Reduced Form Variance in Econometric Models." Econometrica 54, no. 6 (November 1986): 1483. http://dx.doi.org/10.2307/1914310.

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15

Guo, Liang. "Invited Commentary—Removing the Boundary Between Structural and Reduced-Form Models." Marketing Science 25, no. 6 (November 2006): 629–32. http://dx.doi.org/10.1287/mksc.1060.0194.

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16

Olmstead, Sheila M. "Reduced-Form Versus Structural Models of Water Demand Under Nonlinear Prices." Journal of Business & Economic Statistics 27, no. 1 (January 2009): 84–94. http://dx.doi.org/10.1198/jbes.2009.0007.

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17

Gelbach, Jonah B. "The Reduced Form of Litigation Models and the Plaintiff’s Win Rate." Journal of Law and Economics 61, no. 1 (February 2018): 125–57. http://dx.doi.org/10.1086/699151.

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18

Kau, James B., Donald C. Keenan, and Alexey A. Smurov. "Reduced Form Mortgage Pricing as an Alternative to Option-Pricing Models." Journal of Real Estate Finance and Economics 33, no. 3 (November 2006): 183–96. http://dx.doi.org/10.1007/s11146-006-9981-7.

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19

Wong, Hoi Ying, and Tsz Lim Wong. "Reduced-form Models with Regime Switching: An Empirical Analysis for Corporate Bonds." Asia-Pacific Financial Markets 14, no. 3 (September 2007): 229–53. http://dx.doi.org/10.1007/s10690-007-9061-x.

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20

Lassila, Toni, and Gianluigi Rozza. "Parametric free-form shape design with PDE models and reduced basis method." Computer Methods in Applied Mechanics and Engineering 199, no. 23-24 (April 2010): 1583–92. http://dx.doi.org/10.1016/j.cma.2010.01.007.

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21

Chen, Cho-Jieh, and Harry Panjer. "Unifying discrete structural models and reduced-form models in credit risk using a jump-diffusion process." Insurance: Mathematics and Economics 33, no. 2 (October 2003): 357–80. http://dx.doi.org/10.1016/j.insmatheco.2003.08.005.

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22

BANERJEE, TAMAL, MRINAL K. GHOSH, and SRIKANTH K. IYER. "PRICING CREDIT DERIVATIVES IN A MARKOV-MODULATED REDUCED-FORM MODEL." International Journal of Theoretical and Applied Finance 16, no. 04 (June 2013): 1350018. http://dx.doi.org/10.1142/s0219024913500180.

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Numerous incidents in the financial world have exposed the need for the design and analysis of models for correlated default timings. Some models have been studied in this regard which can capture the feedback in case of a major credit event. We extend the research in the same direction by proposing a new family of models having the feedback phenomena and capturing the effects of regime switching economy on the market. The regime switching economy is modeled by a continuous time Markov chain. The Markov chain may also be interpreted to represent the credit rating of the firm whose bond we seek to price. We model the default intensity in a pool of firms using the Markov chain and a risk factor process. We price some single-name and multi-name credit derivatives in terms of certain transforms of the default and loss processes. These transforms can be calculated explicitly in case the default intensity is modeled as a linear function of a conditionally affine jump diffusion process. In such a case, under suitable technical conditions, the price of credit derivatives are obtained as solutions to a system of ODEs with weak coupling, subject to appropriate terminal conditions. Solving the system of ODEs numerically, we analyze the credit derivative spreads and compare their behavior with the nonswitching counterparts. We show that our model can easily incorporate the effects of business cycle. We demonstrate the impact on spreads of the inclusion of rare states that attempt to capture a tight liquidity situation. These states are characterized by low floating interest rate, high default intensity rate, and high volatility. We also model the effects of firm restructuring on the credit spread, in case of a default.

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23

Wei, Dan, Noah Dormady, and Adam Rose. "Development of Reduced-Form Models to Evaluate Macroeconomic Impacts of Greenhouse Gas Mitigation." Journal of Sustainable Energy Engineering 2, no. 4 (April 7, 2015): 377–97. http://dx.doi.org/10.7569/jsee.2015.629503.

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24

Cathcart, Lara, and Lina El-Jahel. "Pricing defaultable bonds: a middle-way approach between structural and reduced-form models." Quantitative Finance 6, no. 3 (June 2006): 243–53. http://dx.doi.org/10.1080/14697680600670754.

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25

Maasoumi, Esfandiar, and Jin-Ho Jeong. "A comparison of GRF and other reduced-form estimators in simultaneous equations models." Journal of Econometrics 37, no. 1 (January 1988): 115–34. http://dx.doi.org/10.1016/0304-4076(88)90078-4.

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26

Gündüz, Yalin, and Marliese Uhrig-Homburg. "Does modeling framework matter? A comparative study of structural and reduced-form models." Review of Derivatives Research 17, no. 1 (April 17, 2013): 39–78. http://dx.doi.org/10.1007/s11147-013-9090-8.

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27

Andreoli, Alessandro, Luca Vincenzo Ballestra, and Graziella Pacelli. "Pricing Credit Default Swaps Under Multifactor Reduced-Form Models: A Differential Quadrature Approach." Computational Economics 51, no. 3 (August 13, 2016): 379–406. http://dx.doi.org/10.1007/s10614-016-9608-x.

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28

Song, Lianlian, Yang Shi, Geoffrey Kwok Fai Tso, and Hing Po Lo. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models." International Journal of Forecasting 37, no. 1 (January 2021): 302–21. http://dx.doi.org/10.1016/j.ijforecast.2020.06.002.

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29

Li, Hong. "Estimation and testing of Euler equation models with time-varying reduced-form coefficients." Journal of Econometrics 142, no. 1 (January 2008): 425–48. http://dx.doi.org/10.1016/j.jeconom.2007.07.002.

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30

Hankey, Steve, and Greg Lindsey. "Facility-Demand Models of Peak Period Pedestrian and Bicycle Traffic: Comparison of Fully Specified and Reduced-Form Models." Transportation Research Record: Journal of the Transportation Research Board 2586, no. 1 (January 2016): 48–58. http://dx.doi.org/10.3141/2586-06.

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31

Skiera, Bernd. "Differences in the Ability of Structural and Reduced-Form Models to Improve Pricing Decisions." Marketing ZFP 32, JRM 1 (2010): 91–100. http://dx.doi.org/10.15358/0344-1369-2010-jrm-1-91.

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32

Nabavi, Seyedbehzad, and Aranya Chakrabortty. "Structured Identification of Reduced-Order Models of Power Systems in a Differential-Algebraic Form." IEEE Transactions on Power Systems 32, no. 1 (January 2017): 198–207. http://dx.doi.org/10.1109/tpwrs.2016.2554154.

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33

Ahmed, Shady E., Omer San, Kursat Kara, Rami Younis, and Adil Rasheed. "Multifidelity computing for coupling full and reduced order models." PLOS ONE 16, no. 2 (February 11, 2021): e0246092. http://dx.doi.org/10.1371/journal.pone.0246092.

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Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes.

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34

Jazdzewski, Brian A., Victor G. Young Jr., and William B. Tolman. "A three-coordinate copper(i)-phenoxide complex that models the reduced form of galactose oxidase." Chemical Communications, no. 22 (1998): 2521–22. http://dx.doi.org/10.1039/a806749j.

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35

Giacomini, Raffaella, and Barbara Rossi. "Forecasting in Nonstationary Environments: What Works and What Doesn’t in Reduced-Form and Structural Models." Annual Review of Economics 7, no. 1 (August 2015): 207–29. http://dx.doi.org/10.1146/annurev-economics-080614-115338.

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36

Yu, Xinyuan, Qirui Zhong, Wenxiao Zhang, Wenjun Meng, Xiao Yun, Haoran Xu, Huizhong Shen, et al. "Direct and Inverse Reduced-Form Models for Reciprocal Calculation of BC Emissions and Atmospheric Concentrations." Environmental Science & Technology 55, no. 15 (July 23, 2021): 10300–10309. http://dx.doi.org/10.1021/acs.est.1c02174.

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37

Blundell, Richard. "What Have We Learned from Structural Models?" American Economic Review 107, no. 5 (May 1, 2017): 287–92. http://dx.doi.org/10.1257/aer.p20171116.

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A structural economic model is one where the structure of decision making is incorporated in the model specification. Structural models aim to identify three distinct, but related, objects: (i) structural “deep” parameters; (ii) underlying mechanisms; (iii) policy counterfactuals. The ability to provide counterfactual predictions sets structural models apart from reduced-form models. The focus is on studies that allow a better understanding of the mechanisms underlying observed behavior and that provide reliable insights about policy counterfactuals. Emphasis is given to models that minimize assumptions on the structural function and on unobserved heterogeneity and approaches that align structural and “reduced form” moments.

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38

Abbott, J. K., and J. E. Wilen. "Voluntary Cooperation in the Commons? Evaluating the Sea State Program with Reduced Form and Structural Models." Land Economics 86, no. 1 (December 31, 2009): 131–54. http://dx.doi.org/10.3368/le.86.1.131.

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39

Trinh, Philippe H. "On reduced models for gravity waves generated by moving bodies." Journal of Fluid Mechanics 813 (January 26, 2017): 824–59. http://dx.doi.org/10.1017/jfm.2016.818.

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In 1983, Tulin published a report proposing a framework for reducing the equations for gravity waves generated by moving bodies into a single nonlinear differential equation solvable in closed form (Proceedings of the 14th Symposium on Naval Hydrodynamics, 1983, pp. 19–51). Several new and puzzling issues were highlighted by Tulin, notably the existence of weak and strong wave-making regimes, and the paradoxical fact that the theory seemed to be applicable to flows at low speeds, ‘but not too low speeds’. These important issues were left unanswered, and despite the novelty of the ideas, Tulin’s report fell into relative obscurity. Now, 30 years later, we will revive Tulin’s observations, and explain how an asymptotically consistent framework allows us to address these concerns. Most notably, we demonstrate, using the asymptotic method of steepest descents, how the production of free-surface waves can be related to the arrangement of integration contours connected to the shape of the moving body. This approach provides a new and powerful methodology for the study of geometrically nonlinear wave–body interactions.

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40

Haider, Shafiq, Abdul Ghafoor, Muhammad Imran, and Fahad Mumtaz Malik. "Time-limited Gramians-based model order reduction for second-order form systems." Transactions of the Institute of Measurement and Control 41, no. 8 (October 15, 2018): 2310–18. http://dx.doi.org/10.1177/0142331218798893.

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A new scheme for model order reduction of large-scale second-order systems in time-limited intervals is presented. Time-limited Gramians that are solutions of continuous-time algebraic Lyapunov equations for second-order form systems are introduced. Time-limited second-order balanced truncation procedures with provision of balancing position and velocity Gramians are formulated. Stability conditions for reduced-order models are stated and algorithms that preserve stability in reduced-order models are discussed. Numerical examples are presented to validate the superiority of the proposed scheme compared with the infinite-time Gramians technique for time-limited applications.

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41

Cimpeanu, Radu, Susana N. Gomes, and Demetrios T. Papageorgiou. "Active control of liquid film flows: beyond reduced-order models." Nonlinear Dynamics 104, no. 1 (February 19, 2021): 267–87. http://dx.doi.org/10.1007/s11071-021-06287-5.

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AbstractThe ability to robustly and efficiently control the dynamics of nonlinear systems lies at the heart of many current technological challenges, ranging from drug delivery systems to ensuring flight safety. Most such scenarios are too complex to tackle directly, and reduced-order modelling is used in order to create viable representations of the target systems. The simplified setting allows for the development of rigorous control theoretical approaches, but the propagation of their effects back up the hierarchy and into real-world systems remains a significant challenge. Using the canonical set-up of a liquid film falling down an inclined plane under the action of active feedback controls in the form of blowing and suction, we develop a multi-level modelling framework containing both analytical models and direct numerical simulations acting as an in silico experimental platform. Constructing strategies at the inexpensive lower levels in the hierarchy, we find that offline control transfer is not viable; however, analytically informed feedback strategies show excellent potential, even far beyond the anticipated range of applicability of the models. The detailed effects of the controls in terms of stability and treatment of nonlinearity are examined in detail in order to gain understanding of the information transfer inside the flows, which can aid transition towards other control-rich frameworks and applications.

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42

TOSO, Raffaella, Francesco BERNARDI, Theresa TIDD, Mirko PINOTTI, Rodney M. CAMIRE, Giovanna MARCHETTI, Katherine A. HIGH, and Eleanor S. POLLAK. "Factor VII mutant V154G models a zymogen-like form of Factor VIIa." Biochemical Journal 369, no. 3 (February 1, 2003): 563–71. http://dx.doi.org/10.1042/bj20020888.

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Proteolytic cleavage of the peptide bond between Arg152 and Ile153 converts the procoagulant protein Factor VII (FVII) to an activated two-chain form (FVIIa). The formation of a salt bridge between Ile153 and Asp343 drives the conversion of FVIIa from being zymogen-like to the active form. In the present paper, we describe the novel FVII mutant V154G (Val154→Gly mutation; residue 17 in the chymotrypsin numbering system), found in three FVII-deficient patients, which models a zymogen-like form of FVIIa. Recombinant V154G FVIIa, although normally cleaved, shows markedly reduced activity towards peptidyl substrate and undetectable activity towards macromolecular substrates. Susceptibility of Ile153 to chemical modification, in either the presence or the absence of tissue factor (TF), suggests that the reduced V154G FVIIa activity is caused by impaired salt-bridge formation, thus resulting in a zymogen-like FVIIa form. The TF-mediated protection from chemical modification of V154A indicated that Gly154 is responsible for this peculiar feature, and suggests that this region, proximal to the heavy chain N-terminus, is directly involved in the conversion of FVII into FVIIa. V154G FVII was exploited to study the FVII—TF interaction, together with three additional FVII variants that were expressed to serve as models for different FVII forms. The comparison of binding affinities of full-length TF after relipidation in l-α-phosphatidylcholine for the zymogen FVII (Arg152→Gln, Kd = 1.04±0.27nM), inactive FVIIa (Ser344→Ala, Kd = 0.27±0.06nM) and a zymogen-like FVIIa (V154G, Kd = 1.15±0.16nM) supports the hypothesis that preferential binding of TF to active FVIIa is insufficient to drive the 105-fold enhancement of FVIIa activity. In addition, the inability of V154G FVIIa to accommodate an inhibitor in the active site, indicating an improperly shaped specificity pocket, would explain the low activity of the zymogen-like form of FVIIa, which is predominant in the absence of TF.

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43

Lee, Sang－Yun, Kil－Seok Han, and Chung－Hoi Heo. "A Study on the Relations between Financial Variables and Implied Default Probability on the Reduced Form Models." Korea Association of Business Education 32, no. 1 (February 24, 2017): 141–62. http://dx.doi.org/10.23839/kabe.2017.32.1.141.

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44

장욱. "Estimation of Implied Loss Given Default Combining Structural and Reduced-form Models and Analysis of Its Determinants." Korean Journal of Financial Engineering 7, no. 2 (June 2008): 49–73. http://dx.doi.org/10.35527/kfedoi.2008.7.2.003.

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45

Vizzaccaro, Alessandra, Yichang Shen, Loïc Salles, Jiří Blahoš, and Cyril Touzé. "Direct computation of nonlinear mapping via normal form for reduced-order models of finite element nonlinear structures." Computer Methods in Applied Mechanics and Engineering 384 (October 2021): 113957. http://dx.doi.org/10.1016/j.cma.2021.113957.

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46

Chen, Songnian, Cheng Hsiao, and Liqun Wang. "MEASUREMENT ERRORS AND CENSORED STRUCTURAL LATENT VARIABLES MODELS." Econometric Theory 28, no. 3 (November 25, 2011): 696–703. http://dx.doi.org/10.1017/s0266466611000715.

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We consider censored structural latent variables models where some exogenous variables are subject to additive measurement errors. We demonstrate that overidentification conditions can be exploited to provide natural instruments for the variables measured with errors, and we propose a two-stage estimation procedure. The first stage involves substituting available instruments in lieu of the variables that are measured with errors and estimating the resulting reduced form parameters using consistent censored regression methods. The second stage obtains structural form parameters using the conventional linear simultaneous equations model estimators.

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47

Hermann, Gerrit, Petra Heffeter, Kushtrim Kryeziu, Walter Berger, Stephan Hann, and Gunda Koellensperger. "The study of reduced versus oxidized glutathione in cancer cell models employing isotopically labelled standards." Anal. Methods 6, no. 9 (2014): 3086–94. http://dx.doi.org/10.1039/c4ay00048j.

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LC-MS/MS assays for accurate quantification of underivatized glutathione (GSH) and its oxidized form glutathione disulfide (GSSG), in cancer cell models, based on isotope dilution and its application in pre-clinical metallo-drug research.

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48

Hein, Rafał, and Cezary Orlikowski. "Hybrid Reduced Model of Rotor." Archive of Mechanical Engineering 60, no. 3 (September 1, 2013): 319–33. http://dx.doi.org/10.2478/meceng-2013-0021.

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Abstract In the paper, the authors describe the method of reduction of a model of rotor system. The proposed approach makes it possible to obtain a low order model including e.g. non-proportional damping or the gyroscopic effect. This method is illustrated using an example of a rotor system. First, a model of the system is built without gyroscopic and damping effects by using the rigid finite element method. Next, this model is reduced. Finally, two identical, low order, reduced models in two perpendicular planes are coupled together by means of gyroscopic and damping interaction to form one model of the system. Thus a hybrid model is obtained. The advantage of the presented method is that the number of gyroscopic and damping interactions does not affect the model range

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49

Edge, Rochelle M., Michael T. Kiley, and Jean-Philippe Laforte. "A Comparison of Forecast Performance Between Federal Reserve Staff Forecasts, Simple Reduced-Form Models, and a DSGE Model." Finance and Economics Discussion Series 2009, no. 10 (2009): 1–52. http://dx.doi.org/10.17016/feds.2009.10.

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50

Santoni, Gabriella, and Dieter Rehder. "Structural models for the reduced form of vanadate-dependent peroxidases: vanadyl complexes with bidentate chiral Schiff base ligands." Journal of Inorganic Biochemistry 98, no. 5 (May 2004): 758–64. http://dx.doi.org/10.1016/j.jinorgbio.2003.10.020.

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