Relevant bibliographies by topics / Structural models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Structural models'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 July 2024

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Journal articles on the topic "Structural models"

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Jaguljnjak Lazarević, Antonia, Mario Uroš, and Ana Čengija. "FUNDAMENTAL MODELS OF STRUCTURAL STABILITY." Rudarsko-geološko-naftni zbornik 32, no. 2 (March 2017): 37–46. http://dx.doi.org/10.17794/rgn.2017.2.5.

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Arellano-Valle, R. B., and H. Bolfarine. "Elliptical structural models." Communications in Statistics - Theory and Methods 25, no. 10 (January 1996): 2319–41. http://dx.doi.org/10.1080/03610929608831841.

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Sánchez, Brisa N., Esben Budtz-Jørgensen, Louise M. Ryan, and Howard Hu. "Structural Equation Models." Journal of the American Statistical Association 100, no. 472 (December 2005): 1443–55. http://dx.doi.org/10.1198/016214505000001005.

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De Stavola, Bianca L., and Rhian M. Daniel. "Marginal Structural Models." Epidemiology 23, no. 2 (March 2012): 233–37. http://dx.doi.org/10.1097/ede.0b013e318245847e.

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Amemiya, Takeshi. "Structural duration models." Journal of Statistical Planning and Inference 49, no. 1 (January 1996): 39–52. http://dx.doi.org/10.1016/0378-3758(95)00029-1.

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Sarkisov, Gari N. "Structural models of water." Uspekhi Fizicheskih Nauk 176, no. 8 (2006): 833. http://dx.doi.org/10.3367/ufnr.0176.200608b.0833.

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Vallat, Brinda, Benjamin Webb, John Westbrook, Hongsuda Tangmunarunkit, Serban Voinea, Carl Kesselman, Andrej Sali, and Helen M. Berman. "Archiving Integrative Structural Models." Biophysical Journal 120, no. 3 (February 2021): 266a. http://dx.doi.org/10.1016/j.bpj.2020.11.1702.

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Mueller, Charles W., Kenneth A. Bollen, and J. Scott Long. "Testing Structural Equation Models." Contemporary Sociology 23, no. 1 (January 1994): 160. http://dx.doi.org/10.2307/2074955.

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Clogg, Clifford C., Kenneth A. Bollen, and J. Scott Long. "Testing Structural Equation Models." Social Forces 73, no. 3 (March 1995): 1161. http://dx.doi.org/10.2307/2580595.

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Dillon, William R., Kenneth A. Bollen, and J. Scott Long. "Testing Structural Equation Models." Journal of Marketing Research 33, no. 3 (August 1996): 374. http://dx.doi.org/10.2307/3152134.

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Dissertations / Theses on the topic "Structural models"

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Lievin-Lieven, Nicholas Andrew John. "Validation of structural dynamic models." Thesis, Imperial College London, 1990. http://hdl.handle.net/10044/1/46413.

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Adhikari, Sondipon. "Damping models for structural vibration." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620975.

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Fonseca, Jose Manuel Rios. "Uncertainty in structural dynamic models." Thesis, Swansea University, 2005. https://cronfa.swan.ac.uk/Record/cronfa42563.

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Modelling of uncertainty increases trust in analysis tools by providing predictions with confidence levels, produces more robust designs, and reduces design cycle time/cost by reducing the amount of experimental verification and validation that is required. However, uncertainty-based methods are more complex and computationally expensive than their deterministic counterparts, the characterisation of uncertainties is a non-trivial task, and the industry feels comfortable with the traditional design methods. In this work the three most popular uncertainty propagation methods (Monte Carlo simulation, perturbation, and fuzzy) are extensively benchmarked in structural dynamics applications. The main focus of the benchmark is accuracy, simplicity, and scalability. Some general guidelines for choosing the adequate uncertainty propagation method for an application are given. Since direct measurement is often prohibitively costly or even impossible, a novel method to characterise uncertainty sources from indirect measurements is presented. This method can accurately estimate the probability distribution of uncertain parameters by maximising the likelihood of the measurements. The likelihood is estimated using efficient variations of the Monte Carlo simulation and perturbation methods, which shift the computational burden to the outside of the optimisation loop, achieving a substantial time-saving without compromising accuracy. The approach was verified experimentally in several applications with promising results. A novel probabilistic procedure for robust design is proposed. It is based on reweighting of the Monte Carlo samples to avoid the numerical inefficiencies of resampling for every candidate design. Although not globally convergent, the proposed method is able to quickly estimate with high accuracy the optimum design. The method is applied to a numerical example, and the obtained designs are verified with regular Monte Carlo. The main focus of this work was on structural dynamics, but care was taken to make the approach general enough to allow other kinds of structural and non- structural analyses.
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Creamer, Nelson Glenn. "Identification of linear structural models." Diss., Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/53631.

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With a great amount of research currently being aimed towards dynamic analysis and control of very large, flexible structures, the need for accurate knowledge of the properties of a structure in terms of the mass, damping, and stiffness matrices is of extreme importance. Typical problems associated with existing structural model identification methods are: (i) non-unique solutions may be obtained when utilizing only free-response measurements (unless some parameters are fixed at their nominal values), (ii) convergence may be difficult to achieve if the initial estimate of the parameters is not "close" to the truth, (iii) physically unrealistic coupling in the system matrices may occur as a consequence of the identification process, (iv) large, highly redundant parameter sets may be required to characterize the system, and (v) large measurement sets may be required. To overcome these problems, a novel identification technique is developed in this dissertation to determine the mass, damping, and stiffness matrices of an undamped, lightly damped, or significantly damped structure from a small set of measurements of both free-response data (natural frequencies, damping factors) and forced-response data (frequency response functions). The identification method is first developed for undamped structures. Through use of the spectral decomposition of the frequency response matrix and the orthogonality properties of the mode shapes, a unique identification of the mass and stiffness matrices is obtained. The method is also shown to be easily incorporated into a substructure synthesis package for identifying high-order systems. The method is then extended to include viscous damped structures. A matrix perturbation approach is developed for lightly damped structures, in which the mass and stiffness matrices are identified using the imaginary components of the measured eigenvalues and, as a post-processor, the damping matrix is obtained from the real components of the measured eigenvalues. For significantly damped structures, the mass, dauping, and stiffness matrices are identified simultaneously. A simple, practical method is also developed for identification of the time-varying relaxation modulus associated with a viscoelastic structure. By assuming time-localized elastic behavior, the relaxation modulus is determined from a series of identification tests performed at various times throughout the response history. Many interesting examples are presented throughout the dissertation to illustrate the applicability and potential of the identification method. It is observed from the numerical results that the uniquely identified structure agrees with simulated measurements of both free and forced·response records.
Ph. D.
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Cerqueira, Pedro Henrique Ramos. "Structural equation models applied to quantitative genetics." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-05112015-145419/.

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Causal models have been used in different areas of knowledge in order to comprehend the causal associations between variables. Over the past decades, the amount of studies using these models have been growing a lot, especially those related to biological systems where studying and learning causal relationships among traits are essential for predicting the consequences of interventions in such system. Graph analysis (GA) and structural equation modeling (SEM) are tools used to explore such associations. While GA allows searching causal structures that express qualitatively how variables are causally connected, fitting SEM with a known causal structure allows to infer the magnitude of causal effects. Also SEM can be viewed as multiple regression models in which response variables can be explanatory variables for others. In quantitative genetics studies, SEM aimed to study the direct and indirect genetic effects associated to individuals through information related to them, beyond the observed characteristics, such as the kinship relations. In those studies typically the assumptions of linear relationships among traits are made. However, in some scenarios, nonlinear relationships can be observed, which make unsuitable the mentioned assumptions. To overcome this limitation, this paper proposes to use a mixed effects polynomial structural equation model, second or superior degree, to model those nonlinear relationships. Two studies were developed, a simulation and an application to real data. The first study involved simulation of 50 data sets, with a fully recursive causal structure involving three characteristics in which linear and nonlinear causal relations between them were allowed. The second study involved the analysis of traits related to dairy cows of the Holstein breed. Phenotypic relationships between traits were calving difficulty, gestation length and also the proportion of perionatal death. We compare the model of multiple traits and polynomials structural equations models, under different polynomials degrees in order to assess the benefits of the SEM polynomial of second or higher degree. For some situations the inappropriate assumption of linearity results in poor predictions of the direct, indirect and total of the genetic variances and covariance, either overestimating, underestimating, or even assign opposite signs to covariances. Therefore, we conclude that the inclusion of a polynomial degree increases the SEM expressive power.
Modelos causais têm sido muitos utilizados em estudos em diferentes áreas de conhecimento, a fim de compreender as associações ou relações causais entre variáveis. Durante as últimas décadas, o uso desses modelos têm crescido muito, especialmente estudos relacionados à sistemas biológicos, uma vez que compreender as relações entre características são essenciais para prever quais são as consequências de intervenções em tais sistemas. Análise do grafo (AG) e os modelos de equações estruturais (MEE) são utilizados como ferramentas para explorar essas relações. Enquanto AG nos permite buscar por estruturas causais, que representam qualitativamente como as variáveis são causalmente conectadas, ajustando o MEE com uma estrutura causal conhecida nos permite inferir a magnitude dos efeitos causais. Os MEE também podem ser vistos como modelos de regressão múltipla em que uma variável resposta pode ser vista como explanatória para uma outra característica. Estudos utilizando MEE em genética quantitativa visam estudar os efeitos genéticos diretos e indiretos associados aos indivíduos por meio de informações realcionadas aos indivíduas, além das característcas observadas, como por exemplo o parentesco entre eles. Neste contexto, é tipicamente adotada a suposição que as características observadas são relacionadas linearmente. No entanto, para alguns cenários, relações não lineares são observadas, o que torna as suposições mencionadas inadequadas. Para superar essa limitação, este trabalho propõe o uso de modelos de equações estruturais de efeitos polinomiais mistos, de segundo grau ou seperior, para modelar relações não lineares. Neste trabalho foram desenvolvidos dois estudos, um de simulação e uma aplicação a dados reais. O primeiro estudo envolveu a simulação de 50 conjuntos de dados, com uma estrutura causal completamente recursiva, envolvendo 3 características, em que foram permitidas relações causais lineares e não lineares entre as mesmas. O segundo estudo envolveu a análise de características relacionadas ao gado leiteiro da raça Holandesa, foram utilizadas relações entre os seguintes fenótipos: dificuldade de parto, duração da gestação e a proporção de morte perionatal. Nós comparamos o modelo misto de múltiplas características com os modelos de equações estruturais polinomiais, com diferentes graus polinomiais, a fim de verificar os benefícios do MEE polinomial de segundo grau ou superior. Para algumas situações a suposição inapropriada de linearidade resulta em previsões pobres das variâncias e covariâncias genéticas diretas, indiretas e totais, seja por superestimar, subestimar, ou mesmo atribuir sinais opostos as covariâncias. Portanto, verificamos que a inclusão de um grau de polinômio aumenta o poder de expressão do MEE.
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Grafe, Henning. "Model updating of large structural dynamics models using measured response functions." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325047.

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Valeinis, Janis. "Confidence bands for structural relationship models." Doctoral thesis, [S.l.] : [s.n.], 2007. http://webdoc.sub.gwdg.de/diss/2007/valeinis.

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De, Antonio Liedo David. "Structural models for macroeconomics and forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210142.

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This Thesis is composed by three independent papers that investigate

central debates in empirical macroeconomic modeling.

Chapter 1, entitled “A Model for Real-Time Data Assessment with an Application to GDP Growth Rates”, provides a model for the data

revisions of macroeconomic variables that distinguishes between rational expectation updates and noise corrections. Thus, the model encompasses the two polar views regarding the publication process of statistical agencies: noise versus news. Most of the studies previous studies that analyze data revisions are based

on the classical noise and news regression approach introduced by Mankiew, Runkle and Shapiro (1984). The problem is that the statistical tests available do not formulate both extreme hypotheses as collectively exhaustive, as recognized by Aruoba (2008). That is, it would be possible to reject or accept both of them simultaneously. In turn, the model for the

DPP presented here allows for the simultaneous presence of both noise and news. While the “regression approach” followed by Faust et al. (2005), along the lines of Mankiew et al. (1984), identifies noise in the preliminary

figures, it is not possible for them to quantify it, as done by our model.

The second and third chapters acknowledge the possibility that macroeconomic data is measured with errors, but the approach followed to model the missmeasurement is extremely stylized and does not capture the complexity of the revision process that we describe in the first chapter.

Chapter 2, entitled “Revisiting the Success of the RBC model”, proposes the use of dynamic factor models as an alternative to the VAR based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Our empirical illustration compares the out-of-sample forecasting performance of a simple RBC model augmented with a serially correlated noise component against several specifications belonging to classes of dynamic factor and VAR models. Although the performance of the RBC model is comparable

to that of the reduced form models, a formal test of predictive accuracy reveals that the weak restrictions are more useful at forecasting than the strong behavioral assumptions imposed by the microfoundations in the model economy.

The last chapter, “What are Shocks Capturing in DSGE modeling”, contributes to current debates on the use and interpretation of larger DSGE

models. Recent tendency in academic work and at central banks is to develop and estimate large DSGE models for policy analysis and forecasting. These models typically have many shocks (e.g. Smets and Wouters, 2003 and Adolfson, Laseen, Linde and Villani, 2005). On the other hand, empirical studies point out that few large shocks are sufficient to capture the covariance structure of macro data (Giannone, Reichlin and

Sala, 2005, Uhlig, 2004). In this Chapter, we propose to reconcile both views by considering an alternative DSGE estimation approach which

models explicitly the statistical agency along the lines of Sargent (1989). This enables us to distinguish whether the exogenous shocks in DSGE

modeling are structural or instead serve the purpose of fitting the data in presence of misspecification and measurement problems. When applied to the original Smets and Wouters (2007) model, we find that the explanatory power of the structural shocks decreases at high frequencies. This allows us to back out a smoother measure of the natural output gap than that

resulting from the original specification.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

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Konarski, Roman. "Sensitivity analysis for structural equation models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22893.pdf.

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Gungor, Murat Kahraman. "Structural models for large software systems." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2006. http://proquest.umi.com/login?COPT=REJTPTU0NWQmSU5UPTAmVkVSPTI=&clientId=3739.

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Books on the topic "Structural models"

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Westland, J. Christopher. Structural Equation Models. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12508-0.

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Westland, J. Christopher. Structural Equation Models. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16507-3.

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Fieldhouse, John. Biochemical structural models. [Wymondham, Melton Mowbray, Leicestershire: Witmehá Productions, 1989.

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Fieldhouse, John. Biochemical structural models. 2nd ed. Wymondham, Melton Mowbray, Leicestershire: Witmehá Productions, 1993.

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Barry, Hilson, ed. Basic structural behaviour: Understanding structures from models. London: T. Telford, 1993.

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Godehardt, Erhard. Graphs as Structural Models. Wiesbaden: Vieweg+Teubner Verlag, 1988. http://dx.doi.org/10.1007/978-3-322-96310-9.

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A, Bollen Kenneth, and Long J. Scott, eds. Testing structural equation models. Newbury Park: Sage Publications, 1993.

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University College Dublin. School of Architecture. Structures models. Dublin: University College Dublin, School of Architecture, 1998.

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Fernández-Villaverde, Jesús. How structural are structural parameters? Cambridge, Mass: National Bureau of Economic Research, 2007.

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Stronge, W. J. Dynamic models for structural plasticity. London: Springer Verlag, 1993.

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Book chapters on the topic "Structural models"

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Albuquerque, Paulo, and Bart J. Bronnenberg. "Structural Models." In International Series in Quantitative Marketing, 203–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53469-5_7.

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Galiani, Sebastian, and Juan Pantano. "Structural Models." In Handbook of Labor, Human Resources and Population Economics, 1–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-57365-6_52-1.

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Rots, J. G. "Numerical models in DIANA." In Structural Masonry, 46–71. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003077961-3.

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Hilbert, Sven, and Matthias Stadler. "Structural Equation Models." In Encyclopedia of Personality and Individual Differences, 5253–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1285.

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Gómez, Víctor. "Multivariate Structural Models." In Linear Time Series with MATLAB and OCTAVE, 245–62. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20790-8_7.

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Plassmann, Engelbert. "Structural ECM Models." In Contributions to Economics, 61–79. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-642-57336-1_4.

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Bauldry, Shawn. "Structural Equation Models." In Encyclopedia of Gerontology and Population Aging, 1–3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-69892-2_566-1.

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Rosenblum, Michael. "Marginal Structural Models." In Targeted Learning, 145–60. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_9.

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Raghunathan, Trivellore, Patricia A. Berglund, and Peter W. Solenberger. "Structural Equation Models." In Multiple Imputation in Practice, 110–19. Boca Raton, Florida : CRC Press, [2019] | Authors have developed a software for analyzing mathematical data, IVEware.: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315154275-7.

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Matzkin, Rosa L. "Nonparametric Structural Models." In The New Palgrave Dictionary of Economics, 1–7. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2163-1.

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Conference papers on the topic "Structural models"

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BENAROYA, HAYM, and HOWARD FLEISHER. "Probabilistic aircraft structural dynamics models." In 32nd Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-921.

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SMITH, SUZANNE, and CHRISTOPHER BEATTIE. "Secant-method adjustment for structural models." In 30th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1278.

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Kordt, M., H. Lusebrink, and G. Schullerus. "Nonlinear model reduction of structural dynamic aircraft models." In 41st Structures, Structural Dynamics, and Materials Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-1757.

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Sacks, Michael S. "Tissue-Level Structural Constitutive Models." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1925.

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Abstract A fundamental goal in constitutive modeling is the ability to predict the mechanical behavior of a material under a generalized loading state. To achieve this goal, rigorous experimentation involving all relevant deformations is necessary to obtain both the form and material constants of a strain-energy density function. For both natural biological tissues and tissue-derived soft biomaterials, there exist many physiological, surgical, and medical device applications where rigorous constitutive models are required. Although able to fit the biaxial data well, phenomenological models cannot be used to determine the underlying mechanisms of tissue mechanical behavior. In particular, the respective roles of the fibers and the matrix and how these may change with growth or chemical treatments are unknown. Structurally based constitutive models avoid ambiguities in material characterization and offer insights into the function, structure, and mechanics of tissue components. In the present work a structural constitutive model for the aortic valve is presented as an example of a structural approach. Ongoing issues in practically applying structural models to other tissues are also addressed.
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Enelund, Mikael, and Peter Olsson. "Damping described by fading memory models." In 36th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-1181.

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HOLLKAMP, J., and S. BATILL. "Time series models for nonlinear systems." In 30th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1197.

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BARBERO, E., and S. SONTI. "Micromechanical models for pultruded composite beams." In 32nd Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-1045.

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Hasselman, Timothy, Jon Chrostowski, and Timothy Ross. "Propagation of modeling uncertainty through structural dynamic models." In 35th Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-1316.

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HAJELA, P., and L. BERKE. "Neurobiological Computational Models in Structural Analysis and Design." In 31st Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-1133.

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CRAWLEY, EDWARD, and ERIC ANDERSON. "Detailed models of piezoceramic actuation of beams." In 30th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1388.

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Reports on the topic "Structural models"

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Batman, Joe, Larry Howard, and Bill Schelker. An Introduction to Structural Models. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada268151.

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Fair, Ray. VAR Models as Structural Approximations. Cambridge, MA: National Bureau of Economic Research, January 1988. http://dx.doi.org/10.3386/w2495.

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Galiani, Sebastian, and Juan Pantano. Structural Models: Inception and Frontier. Cambridge, MA: National Bureau of Economic Research, April 2021. http://dx.doi.org/10.3386/w28698.

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Kuether, Robert J., Jonel Ortiz, and Mark Chen. Model Order Reduction of Nonviscously Damped Structural Dynamic Models. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1475503.

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Koşar, Gizem, and Cormac O'Dea. Expectations Data in Structural Microeconomic Models. Cambridge, MA: National Bureau of Economic Research, May 2022. http://dx.doi.org/10.3386/w30094.

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HAMMERAND, DANIEL C., SAMUEL W. KEY, J. T. ODEN, I. BAKUSKA, G. RODIN, C. BAJAJ, S. PRUDHOMME, and K. VEMAGANTI. Structural Simulations Using Multi-Resolution Material Models. Office of Scientific and Technical Information (OSTI), November 2001. http://dx.doi.org/10.2172/789595.

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Chen, Le-Yu. Identification of structural dynamic discrete choice models. Institute for Fiscal Studies, May 2009. http://dx.doi.org/10.1920/wp.cem.2009.0809.

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Weijters, Bert. Analyzing Experimental Data in Structural Equation Models. Instats Inc., 2023. http://dx.doi.org/10.61700/zclk0a8vgkfaa706.

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This two-day workshop, 'Analyzing Experimental Data using Structural Equation Modeling', led by Bert Weijters from Ghent University, provides a comprehensive understanding of SEM and its applications in research, with a focus on using Mplus software for SEM analysis. Ideal for PhD students, professors, and professional researchers in Psychology, Education, Management, and Marketing, the seminar offers practical experience in data analysis and an official Instats certificate of completion, with ECTS Equivalent points for European students.
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Attansio, Orazio, and Debbie Blair. Structural modelling in policymaking. Centre for Excellence and Development Impact and Learning (CEDIL), November 2018. http://dx.doi.org/10.51744/cip9.

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Structural modelling, that is the use of behavioural models to add a framework to the decision problem of an agent, is a useful yet underused tool in evaluation. This paper provides a general introduction to structural modelling, as well as an overview of other commonly used evaluation techniques in Economics and other social sciences. It then goes on to show with three key case studies, how structural models can be used to enrich the findings from randomised control trials. The case studies cover a wide range of policy questions: examining demand for health products in Kenya, incentivising teachers to attend school in India, and evaluating conditional cash transfers for education in Mexico. The case studies show how structural models add to our understanding of the mechanisms behind a given treatment effect, how the findings may change when the policy is rolled out under different circumstances, as well as allowing for the evaluation of different policies that were not originally trialled. The common pitfalls of structural models are discussed, with guidance provided throughout on how to conduct sensitivity analysis and model validation. It is hoped that this paper will persuade other researchers to use structural models, in conjunction with randomised control trials, that will lead to improved evaluation results, a deeper understanding of important problems, and better informed policymaking in the future.
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Romero-Chamorro, José Vicente, and Sara Naranjo-Saldarriaga. Weather Shocks and Inflation Expectations in Semi-Structural Models. Banco de la República Colombia, November 2022. http://dx.doi.org/10.32468/be.1218.

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Colombia is particularly affected by the El Niño Southern Oscillation (ENSO) weather fluctuations. In this context, this study explores how the adverse weather events linked to ENSO affect the inflation expectations in Colombia and how to incorporate these second-round effects into a small open economy New Keynesian model. Using BVARx models we provide evidence that the inflation expectations obtained from surveys and break-even inflation measures are affected by weather supply shocks. Later, using this stylised fact, we modify one of the core forecasting models of the Banco de la República by incorporating the mechanisms in which weather-related shocks affect marginal costs and inflation expectations. We find that ENSO shocks had an important role in both inflation and the dynamics of inflation expectations, and that policymakers should consider this fact.
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