Academic literature on the topic 'Expectational Model'
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Journal articles on the topic "Expectational Model"
Heinemann, Maik. "CONVERGENCE OF ADAPTIVE LEARNING AND EXPECTATIONAL STABILITY: THE CASE OF MULTIPLE RATIONAL-EXPECTATIONS EQUILIBRIA." Macroeconomic Dynamics 4, no. 3 (September 2000): 263–88. http://dx.doi.org/10.1017/s1365100500016011.
Full textKoustas, Zisimos. "Some model-based tests of expectational rationality." Atlantic Economic Journal 17, no. 2 (June 1989): 53–64. http://dx.doi.org/10.1007/bf02304821.
Full textSunder, Shyam. "Management Control, Expectations, Common Knowledge, and Culture." Journal of Management Accounting Research 14, no. 1 (January 1, 2002): 173–87. http://dx.doi.org/10.2308/jmar.2002.14.1.173.
Full textVan Zandt, Timothy, and Martin Lettau. "ROBUSTNESS OF ADAPTIVE EXPECTATIONS AS AN EQUILIBRIUM SELECTION DEVICE." Macroeconomic Dynamics 7, no. 1 (January 7, 2003): 89–118. http://dx.doi.org/10.1017/s1365100502010313.
Full textCho, In-Koo, and Kenneth Kasa. "Gresham's Law of Model Averaging." American Economic Review 107, no. 11 (November 1, 2017): 3589–616. http://dx.doi.org/10.1257/aer.20160665.
Full textXu, Xiaoli, and Yibei Ling. "A study on the expectational model for tumor growth." International Journal of Bio-Medical Computing 22, no. 2 (March 1988): 135–41. http://dx.doi.org/10.1016/0020-7101(88)90049-9.
Full textKurozumi, Takushi, and Willem Van Zandweghe. "TREND INFLATION AND EQUILIBRIUM STABILITY: FIRM-SPECIFIC VERSUS HOMOGENEOUS LABOR." Macroeconomic Dynamics 21, no. 4 (August 8, 2016): 947–81. http://dx.doi.org/10.1017/s1365100515000784.
Full textEvans, George W., and Seppo Honkapohja. "Expectational stability of stationary sunspot equilibria in a forward-looking linear model." Journal of Economic Dynamics and Control 28, no. 1 (October 2003): 171–81. http://dx.doi.org/10.1016/s0165-1889(02)00137-9.
Full textPfajfar, Damjan, and Emiliano Santoro. "CREDIT MARKET DISTORTIONS, ASSET PRICES AND MONETARY POLICY." Macroeconomic Dynamics 18, no. 3 (September 28, 2012): 631–50. http://dx.doi.org/10.1017/s1365100512000557.
Full textArifovic, Jasmina, and James Bullard. "INTRODUCTION TO THE SPECIAL ISSUE: NEW APPROACHES TO LEARNING IN MACROECONOMIC MODELS." Macroeconomic Dynamics 5, no. 02 (April 2001): 143–47. http://dx.doi.org/10.1017/s1365100501019010.
Full textDissertations / Theses on the topic "Expectational Model"
Galbraith, J. W. "Modelling the formation of expectations." Thesis, University of Oxford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381848.
Full textKræmer, John Ph D. Massachusetts Institute of Technology. "An expectation model of referring expressions." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62046.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 297-205).
This thesis introduces EMRE, an expectation-based model of referring expressions. EMRE is proposed as a model of non-syntactic dependencies - in particular, discourse-level semantic dependencies that bridge sentence gaps. These include but are not limited to anaphora (references to noun phrases in previous sentences) and coherence predicates such as causality, temporal ordering and resemblance -- two domains that have typically been treated as entirely distinct aspects of language. EMRE is a computational-level model, and is agnostic about any particular algorithms, cognitive faculties, or neurological substrates that might be applied to the problem of semantic reference. Instead, it describes reference as a computational problem framed in terms of expectation and inference, and describes a solution to the problem based on rational top-down expectations about the likely targets of referring expressions, and on bottom-up feature-based matching that occurs when a referring expression is encountered. EMRE is used to derive novel empirical predictions about how people will construe particular discourse constructions involving NP anaphora and coherence predicates. These predictions are tested in controlled behavioral experiments, in which participants read and answer questions about short texts. The results of these experiments are shown to be consistent with a model of reference as an expectation-based computational structure with different underlying rules than those governing syntactic processing.
by John Kræmer.
Ph.D.
Morman, Karen. "Teacher Expectations of a Literacy Coaching Model." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2415.
Full textZumpe, Martin Kai. "Stabilité macroéconomique, apprentissage et politique monétaire : une approche comparative : modélisation DSGE versus modélisation multi-agents." Thesis, Bordeaux 4, 2012. http://www.theses.fr/2012BOR40022/document.
Full textThis thesis analyses the role of learning in two different modelling frameworks. In the new canonicalmodel with adaptive learning, the most remarkable characteristics of the learning dynamics deal withthe capacity of monetary policy rules to guaranty convergence to the rational expectations equilibrium.The transmission mechanism of the monetary policy is based on the substitution effect associated to theconsumption channel. In the case of an agent-based model which relaxes some restrictive assumptionsof the new canonical model - but is endowed with a similar structure - aggregate variables evolve atsome distance from the rational expectations equilibrium. Monetary policy has a marginal impact onthe agregated variables via the wealth effect of the consumption channel. When agents learn accordingto an evolutionnary social learning process, the economy converges to regions of low economic activity.The introduction of a process where agents learn individually by using their mental models induces lessdepressive learning dynamics. These differences between the two modelling frameworks show that thegeneralisation of the results of the new canonical model is not easy to achieve
Creel, James Silas. "Intention is commitment with expectation." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2313.
Full textQi, Yuan 1974. "Extending expectation propagation for graphical models." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/30215.
Full textIncludes bibliographical references (p. 101-106).
Graphical models have been widely used in many applications, ranging from human behavior recognition to wireless signal detection. However, efficient inference and learning techniques for graphical models are needed to handle complex models, such as hybrid Bayesian networks. This thesis proposes extensions of expectation propagation, a powerful generalization of loopy belief propagation, to develop efficient Bayesian inference and learning algorithms for graphical models. The first two chapters of the thesis present inference algorithms for generative graphical models, and the next two propose learning algorithms for conditional graphical models. First, the thesis proposes a window-based EP smoothing algorithm for online estimation on hybrid dynamic Bayesian networks. For an application in wireless communications, window-based EP smoothing achieves estimation accuracy comparable to sequential Monte Carlo methods, but with less than one-tenth computational cost. Second, it develops a new method that combines tree-structured EP approximations with the junction tree for inference on loopy graphs. This new method saves computation and memory by propagating messages only locally to a subgraph when processing each edge in the entire graph. Using this local propagation scheme, this method is not only more accurate, but also faster than loopy belief propagation and structured variational methods. Third, it proposes predictive automatic relevance determination (ARD) to enhance classification accuracy in the presence of irrelevant features. ARD is a Bayesian technique for feature selection.
(cont.) The thesis discusses the overfitting problem associated with ARD, and proposes a method that optimizes the estimated predictive performance, instead of maximizing the model evidence. For a gene expression classification problem, predictive ARD outperforms previous methods, including traditional ARD as well as support vector machines combined with feature selection techniques. Finally, it presents Bayesian conditional random fields (BCRFs) for classifying interdependent and structured data, such as sequences, images or webs. BCRFs estimate the posterior distribution of model parameters and average prediction over this posterior to avoid overfitting. For the problems of frequently-asked-question labeling and of ink recognition, BCRFs achieve superior prediction accuracy over conditional random fields trained with maximum likelihood and maximum a posteriori criteria.
by Yuan Qi.
Ph.D.
Pollio, G. "Empirical tests of the rational expectations hypothesis." Thesis, City University London, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.351632.
Full textZhang, Xiaohua 1964. "Price expectations in perennial crop supply models." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/291531.
Full textDavis, J. G. "A rational expectations model of the Federal Republic of Germany." Thesis, University of Liverpool, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377979.
Full textElhouar, Mikael. "Essays on interest rate theory." Doctoral thesis, Handelshögskolan i Stockholm, Finansiell Ekonomi (FI), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-451.
Full textBooks on the topic "Expectational Model"
Dolado, Juan José. An expectational model of labour demand in Spanish industry. Madrid: Banco de España, Servicio de Estudios, 1985.
Find full textBranch, William A. Expectational stability in regime-switching rational expectations models. Kansas City [Mo.]: Research Division, Federal Reserve Bank of Kansas City, 2007.
Find full textKollintzas, Tryphon, ed. The Rational Expectations Equilibrium Inventory Model. New York, NY: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4684-6374-3.
Full textWestaway, Peter. Consistent expectations in the Treasury model. London: Treasury, 1986.
Find full textBlake, A. P. The Treasury model under rational expectations. London: University of London. Queen Mary College. Department of Economics, 1986.
Find full textPesaran, Hashem. Limited-dependent rational expectations models with future expectations. Cambridge: Department of Applied Economics, University of Cambridge, 1993.
Find full textFisher, Paul. Rational Expectations in Macroeconomic Models. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-8002-1.
Full textKowal, James A. Behavior models: Specifying users' expectations. Englewood Cliffs, New Jersey: Prentice-Hall, 1992.
Find full textKowal, James A. Behavior models: Specifying user's expectations. Englewood Cliffs, N.J: Prentice Hall, 1992.
Find full textRational expectations in macroeconomic models. Dordrecht: Kluwer Academic Publishers, 1992.
Find full textBook chapters on the topic "Expectational Model"
Jiang, Di, Chen Zhang, and Yuanfeng Song. "Expectation Maximization." In Probabilistic Topic Models, 53–62. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2431-8_4.
Full textOsborne, Martin J., and Ariel Rubinstein. "Equilibrium with prices and expectations." In Models in Microeconomic Theory, 187–202. 2nd ed. Cambridge, UK: Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0362.13.
Full textOsborne, Martin J., and Ariel Rubinstein. "Equilibrium with prices and expectations." In Models in Microeconomic Theory, 187–202. 2nd ed. Cambridge, UK: Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0361.13.
Full textDavis, Mark H. A. "Distributions and expectations." In Markov Models and Optimization, 81–133. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4483-2_3.
Full textPauletto, Giorgio. "Rational Expectations Models." In Advances in Computational Economics, 93–137. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2631-2_5.
Full textClements, Michael P. "Macroeconomic Uncertainty: Surveys Versus Models?" In Macroeconomic Survey Expectations, 123–43. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97223-7_7.
Full textClements, Michael P. "Behavioural Models of Expectations Formation." In Macroeconomic Survey Expectations, 145–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97223-7_8.
Full textFrydman, Roman, and Michael D. Goldberg. "6. Opening Models of Asset Prices and Risk to Nonroutine Change." In Rethinking Expectations, edited by Roman Frydman and Edmund S. Phelps, 207–48. Princeton: Princeton University Press, 2013. http://dx.doi.org/10.1515/9781400846450.207.
Full textMitchell, Terence R. "Expectancy-Value Models in Organizational Psychology." In Expectations and Actions, 293–312. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003150879-16.
Full textHolden, K., D. A. Peel, and J. L. Thompson. "Expectations in Econometric Models." In Expectations: Theory and Evidence, 143–62. London: Macmillan Education UK, 1985. http://dx.doi.org/10.1007/978-1-349-17862-9_6.
Full textConference papers on the topic "Expectational Model"
Lu, Shu Quan, Shiyu Xie, and Takao Ito. "Estimation of the Rigidity and Expectational Model." In 2009 Fifth International Joint Conference on INC, IMS and IDC. IEEE, 2009. http://dx.doi.org/10.1109/ncm.2009.148.
Full textYanagisawa, Hideyoshi, and Natsu Mikami. "Effects of Expectation Uncertainty and Surprise on Quality Perception Factors of Expectation Effect." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34458.
Full textWells, Peter, and J.-P. Skeete. "Circular Business Models as Instruments of Corporate Power." In New Business Models 2023. Maastricht University Press, 2023. http://dx.doi.org/10.26481/mup.2302.36.
Full textWan, Yi, Muhammad Zaheer, Adam White, Martha White, and Richard S. Sutton. "Planning with Expectation Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/506.
Full textAbdulla, Farzanna Yashera, and Jabil Mapjabil. "REVIEW OF THEORIES AND MODEL OF RESEARCH ON LIMINALITY IN TOURISM." In GLOBAL TOURISM CONFERENCE 2021. PENERBIT UMT, 2021. http://dx.doi.org/10.46754/gtc.2021.11.048.
Full textFink, Lauren K. "Computational models of temporal expectations." In Future Directions of Music Cognition. The Ohio State University Libraries, 2021. http://dx.doi.org/10.18061/fdmc.2021.0041.
Full text"SEMI-SUPERVISED LEARNING OF ALTERNATIVELY SPLICED EXONS USING EXPECTATION MAXIMIZATION TYPE APPROACHES." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003791802400245.
Full textMeng, Ming-Qiang, and Yan-Kui Liu. "Fuzzy Expectation-Based Data Envelopment Analysis Model." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370341.
Full textChiang, Yu-Jui, and Hsu-Feng Hsiao. "Expectation Model and Scheduling for Video Streaming." In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2018. http://dx.doi.org/10.1109/iscas.2018.8351571.
Full textChereshova, S. V. "Remuneration of Subjects of Parental Labour in Modern Russia: from Expectations to Reality." In XII Ural Demographic Forum “Paradigms and models of demographic development”. Institute of Economics of the Ural Branch of the Russian Academy of Sciences, 2021. http://dx.doi.org/10.17059/udf-2021-2-19.
Full textReports on the topic "Expectational Model"
Oliveira, Lucas Gabriel Martins de. Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data. Inter-American Development Bank, July 2023. http://dx.doi.org/10.18235/0005004.
Full textMolavi, Pooya, Alireza Tahbaz-Salehi, and Andrea Vedolin. Model Complexity, Expectations, and Asset Prices. Cambridge, MA: National Bureau of Economic Research, January 2021. http://dx.doi.org/10.3386/w28408.
Full textRomero-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.
Full textHajdini, Ina, Edward S. Knotek, John Leer, Mathieu O. Pedemonte, Robert W. Rich, and Raphael S. Schoenle. Low passthrough from inflation expectations to income growth expectations: why people dislike inflation. Federal Reserve Bank of Cleveland, March 2023. http://dx.doi.org/10.26509/frbc-wp-202221r.
Full textHajdini, Ina. Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model. Federal Reserve Bank of Cleveland, March 2023. http://dx.doi.org/10.26509/frbc-wp-202203r.
Full textGaspar, Jess, and Kenneth Judd. Solving Large Scale Rational Expectations Models. Cambridge, MA: National Bureau of Economic Research, February 1997. http://dx.doi.org/10.3386/t0207.
Full textFarmer, Roger E. A., Tao Zha, and Daniel Waggoner. Understanding Markov-Switching Rational Expectations Models. Cambridge, MA: National Bureau of Economic Research, February 2009. http://dx.doi.org/10.3386/w14710.
Full textKoşar, 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.
Full textBaldwin, Richard. The Core-Periphery Model with Forward-Looking Expectations. Cambridge, MA: National Bureau of Economic Research, February 1999. http://dx.doi.org/10.3386/w6921.
Full textBeyer, Andreas, Roger E. Farmer, Jérôme Henry, and Massimiliano Marcellino. Factor Analysis in a Model with Rational Expectations. Cambridge, MA: National Bureau of Economic Research, September 2007. http://dx.doi.org/10.3386/w13404.
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