Bibliografías temáticas / Dynamic stochastic models

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Literatura académica sobre el tema "Dynamic stochastic models"

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Publicado: 1 de marzo de 2025

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Artículos de revistas sobre el tema "Dynamic stochastic models"

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Assaf, A. George, Mike G. Tsionas y Florian Kock. "Dynamic quantile stochastic frontier models". International Journal of Hospitality Management 89 (agosto de 2020): 102588. http://dx.doi.org/10.1016/j.ijhm.2020.102588.

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Dror, Moshe y Warren Powell. "Stochastic and Dynamic Models in Transportation". Operations Research 41, n.º 1 (febrero de 1993): 11–14. http://dx.doi.org/10.1287/opre.41.1.11.

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Reichman, David R. "On Stochastic Models of Dynamic Disorder†". Journal of Physical Chemistry B 110, n.º 38 (septiembre de 2006): 19061–65. http://dx.doi.org/10.1021/jp061992j.

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Yano, Makoto. "Comparative statics in dynamic stochastic models". Journal of Mathematical Economics 18, n.º 2 (enero de 1989): 169–85. http://dx.doi.org/10.1016/0304-4068(89)90020-7.

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Zilcha, I. "Efficiency in Stochastic Dynamic Economic Models". IFAC Proceedings Volumes 22, n.º 5 (junio de 1989): 357–61. http://dx.doi.org/10.1016/s1474-6670(17)53474-6.

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Popkov, Yu S. "Macrosystems Models of Dynamic Stochastic Networks". Automation and Remote Control 64, n.º 12 (diciembre de 2003): 1956–74. http://dx.doi.org/10.1023/b:aurc.0000008434.58605.1b.

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Creal, Drew D. y Ruey S. Tsay. "High dimensional dynamic stochastic copula models". Journal of Econometrics 189, n.º 2 (diciembre de 2015): 335–45. http://dx.doi.org/10.1016/j.jeconom.2015.03.027.

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Fan, Ruzong, Bin Zhu y Yuedong Wang. "Stochastic dynamic models and Chebyshev splines". Canadian Journal of Statistics 42, n.º 4 (3 de noviembre de 2014): 610–34. http://dx.doi.org/10.1002/cjs.11233.

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Tsionas, Efthymios G. "Inference in dynamic stochastic frontier models". Journal of Applied Econometrics 21, n.º 5 (2006): 669–76. http://dx.doi.org/10.1002/jae.862.

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Popkov, Yuri S., Alexey Yu Popkov, Yuri A. Dubnov y Dimitri Solomatine. "Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models". Mathematics 8, n.º 7 (8 de julio de 2020): 1119. http://dx.doi.org/10.3390/math8071119.

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We propose a new forecasting procedure that includes randomized hierarchical dynamic regression models with random parameters, measurement noises and random input. We developed the technology of entropy-randomized machine learning, which includes the estimation of characteristics of a dynamic regression model and its testing by generating ensembles of predicted trajectories through the sampling of the entropy-optimal probability density functions of the model parameters and measurement noises. The density functions are determined at the learning stage by solving the constrained maximization problem of an information entropy functional subject to the empirical balances with real data. The proposed procedure is applied to the randomized forecasting of the daily electrical load in a regional power system. We construct a two-layer dynamic model of the daily electrical load. One of the layers describes the dependence of electrical load on ambient temperature while the other simulates the stochastic quasi-fluctuating temperature dynamics.
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Tesis sobre el tema "Dynamic stochastic models"

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Balijepalli, Narasimha Chandrasekhar. "Stochastic process models for dynamic traffic assignment". Thesis, University of Leeds, 2007. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436385.

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Chu, Qin. "Dynamic and stochastic models for container allocation". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11742.

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Corneli, Marco. "Dynamic stochastic block models, clustering and segmentation in dynamic graphs". Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E012/document.

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Cette thèse porte sur l’analyse de graphes dynamiques, définis en temps discret ou continu. Nous introduisons une nouvelle extension dynamique du modèle a blocs stochastiques (SBM), appelée dSBM, qui utilise des processus de Poisson non homogènes pour modéliser les interactions parmi les paires de nœuds d’un graphe dynamique. Les fonctions d’intensité des processus ne dépendent que des classes des nœuds comme dans SBM. De plus, ces fonctions d’intensité ont des propriétés de régularité sur des intervalles temporels qui sont à estimer, et à l’intérieur desquels les processus de Poisson redeviennent homogènes. Un récent algorithme d’estimation pour SBM, qui repose sur la maximisation d’un critère exact (ICL exacte) est ici adopté pour estimer les paramètres de dSBM et sélectionner simultanément le modèle optimal. Ensuite, un algorithme exact pour la détection de rupture dans les séries temporelles, la méthode «pruned exact linear time» (PELT), est étendu pour faire de la détection de rupture dans des données de graphe dynamique selon le modèle dSBM. Enfin, le modèle dSBM est étendu ultérieurement pour faire de l’analyse de réseau textuel dynamique. Les réseaux sociaux sont un exemple de réseaux textuels: les acteurs s’échangent des documents (posts, tweets, etc.) dont le contenu textuel peut être utilisé pour faire de la classification et détecter la structure temporelle du graphe dynamique. Le modèle que nous introduisons est appelé «dynamic stochastic topic block model» (dSTBM)
This thesis focuses on the statistical analysis of dynamic graphs, both defined in discrete or continuous time. We introduce a new extension of the stochastic block model (SBM) for dynamic graphs. The proposed approach, called dSBM, adopts non homogeneous Poisson processes to model the interaction times between pairs of nodes in dynamic graphs, either in discrete or continuous time. The intensity functions of the processes only depend on the node clusters, in a block modelling perspective. Moreover, all the intensity functions share some regularity properties on hidden time intervals that need to be estimated. A recent estimation algorithm for SBM, based on the greedy maximization of an exact criterion (exact ICL) is adopted for inference and model selection in dSBM. Moreover, an exact algorithm for change point detection in time series, the "pruned exact linear time" (PELT) method is extended to deal with dynamic graph data modelled via dSBM. The approach we propose can be used for change point analysis in graph data. Finally, a further extension of dSBM is developed to analyse dynamic net- works with textual edges (like social networks, for instance). In this context, the graph edges are associated with documents exchanged between the corresponding vertices. The textual content of the documents can provide additional information about the dynamic graph topological structure. The new model we propose is called "dynamic stochastic topic block model" (dSTBM).Graphs are mathematical structures very suitable to model interactions between objects or actors of interest. Several real networks such as communication networks, financial transaction networks, mobile telephone networks and social networks (Facebook, Linkedin, etc.) can be modelled via graphs. When observing a network, the time variable comes into play in two different ways: we can study the time dates at which the interactions occur and/or the interaction time spans. This thesis only focuses on the first time dimension and each interaction is assumed to be instantaneous, for simplicity. Hence, the network evolution is given by the interaction time dates only. In this framework, graphs can be used in two different ways to model networks. Discrete time […] Continuous time […]. In this thesis both these perspectives are adopted, alternatively. We consider new unsupervised methods to cluster the vertices of a graph into groups of homogeneous connection profiles. In this manuscript, the node groups are assumed to be time invariant to avoid possible identifiability issues. Moreover, the approaches that we propose aim to detect structural changes in the way the node clusters interact with each other. The building block of this thesis is the stochastic block model (SBM), a probabilistic approach initially used in social sciences. The standard SBM assumes that the nodes of a graph belong to hidden (disjoint) clusters and that the probability of observing an edge between two nodes only depends on their clusters. Since no further assumption is made on the connection probabilities, SBM is a very flexible model able to detect different network topologies (hubs, stars, communities, etc.)
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Nori, Vijay S. "Algorithms for dynamic and stochastic logistics problems". Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/24513.

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Paltrinieri, Federico. "Modeling temporal networks with dynamic stochastic block models". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18805/.

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Osservando il recente interesse per le reti dinamiche temporali e l'ampio numero di campi di applicazione, questa tesi ha due principali propositi: primo, di analizzare alcuni modelli teorici di reti temporali, specialmente lo stochastic blockmodel dinamico, al fine di descrivere la dinamica di sistemi reali e fare previsioni. Il secondo proposito della tesi è quello di creare due nuovi modelli teorici, basati sulla teoria dei processi autoregressivi, dai quali inferire nuovi parametri dalle reti temporali, come la matrice di evoluzione di stato e una migliore stima della varianza del rumore del processo di evoluzione temporale. Infine, tutti i modelli sono testati su un data set interbancario: questi rivelano la presenza di un evento atteso che divide la rete temporale in due periodi distinti con differenti configurazioni e parametri.
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Chung, Kun-Jen. "Some topics in risk-sensitive stochastic dynamic models". Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/28644.

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Loddo, Antonello. "Bayesian analysis of multivariate stochastic volatility and dynamic models". Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4359.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (April 26, 2007) Vita. Includes bibliographical references.
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Foliente, Greg C. "Stochastic dynamic response of wood structural systems". Diss., This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-05042006-164535/.

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Ahn, Kwangwon. "Dynamic stochastic general equilibrium models with money, default and collateral". Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:78317412-e13d-4495-9665-340e777ab7b2.

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This D.Phil. dissertation investigates the areas in financial stability. The three comprising essays have a common ground: money, default and collateral in the theory of finance. Chapter Two (co-authored with Prof. Dimitrios Tsomocos), which is titled “A Dynamic General Equilibrium Model to Analyse Financial Stability”, aims to refine and improve existing DSGE models in two ways. First, it incorporates hitherto neglected components such as endogenous default, money via cash-in-advance constraints and heterogeneous banking sectors. Thus, in contrast to the New Keynesian approach, here it is liquidity and default that are the driving forces behind our results. Second, in focusing on both monetary policy and fiscal policy, it elucidates how interactions between the two policy arenas affect macroeconomic fluctuations, particularly in regard to financial stability. Through these refinements, we put forward the policy response necessary to achieve a stable financial system using a calibrated DSGE model. Chapter Three, entitled “Monetary Policy in a Time of Natural Disaster”, investigates the appropriate monetary policy response to natural disasters in the DSGE framework. I develop a realistic model for financial turmoil by evaluating the impact of natural disasters on credit markets by including financial frictions such as endogenous default and liquidity constraints. I show that the standard Taylor rule (1993) response in models with money and default is to increase the nominal interest rate after a disaster shock. However, in fact an inflation-targeting policy (i.e. monetary contraction) is not compatible with mitigating financial fragility in the highly indebted economy with near-zero interest rate, and arguably the `Taylor Principle' does not hold in such as economy (e.g. Japan in 2011). Nevertheless, expansionary monetary policy induces a debt overhang even further. Chapter Four, “Collateral, Default and Asset Prices”, uses a DSGE framework to put forward a model of how agents adjust their asset holdings in response to deflationary shocks. By introducing collateral constraints in the default decision, I capture some original features of the early debt-deflation literature, such as distress selling and instability. The estimated model successfully delivers a procyclical feedback loop for the default channel, which consists of foreclosure, high borrowing costs, inefficient capital allocation, and a further decrease in the output level. I investigated recessionary shocks inducing deflation in commodity and/or asset prices for monetary policy experiments. This, therefore, underlines the importance of monetary policy in restoring financial stability during a deflationary period.
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Cherepnev, Alexey [Verfasser]. "Stochastic foundations of dynamic trade and labor market models / Alexey Cherepnev". Mainz : Universitätsbibliothek der Johannes Gutenberg-Universität Mainz, 2015. http://d-nb.info/1225685508/34.

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Libros sobre el tema "Dynamic stochastic models"

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Galindo Gil, Hamilton, Alexis Montecinos Bravo y Marco Antonio Ortiz Sosa. Dynamic Stochastic General Equilibrium Models. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58105-2.

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Gong, Gang. Stochastic dynamic macroeconomics: Theory, numerics, and empirical evidence. New York: Oxford University Press, 2005.

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Chatterjee, Partha. Convergence in a stochastic dynamic Heckscher-Ohlin model. Ottawa: Bank of Canada, 2006.

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Pfann, Gerard A. Dynamic modelling of stochastic demand for manufacturing employment. Berlin: Springer-Verlag, 1990.

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Gong, Gang. Stochastic dynamic macroeconomics: Theory and empirical evidence. New York, NY: Oxford University Press, 2004.

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C, Colander David, ed. Post Walrasian macroeconomics: Beyond the dynamic stochastic general equilibrium model. Cambridge: Cambridge University Press, 2006.

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Merbis, Maarten Dirk. Optimal control for econometric models: An application of stochastic dynamic games. Amsterdam: Free University Press, 1986.

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Ransbotham, Sam. Sequential grid computing: Models and computational experiments. Bangalore: Indian Institute of Management Bangalore, 2009.

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Nijkamp, Peter. Spatial interaction and input-output models: A dynamic stochastic multi-objective framework. Amsterdam: Vrije Universiteit, Faculteit der Economische Wetenschappen en Econometrie, 1987.

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author, Muler Nora, ed. Stochastic optimization in insurance: A dynamic programming approach. New York, NY: Springer, 2014.

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Capítulos de libros sobre el tema "Dynamic stochastic models"

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Boguslavskiy, Josif A. "Estimating the Parameters of Stochastic Models". En Dynamic Systems Models, 125–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-04036-3_7.

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Zhang, Zhe George. "Dynamic Optimization in Stochastic Models". En Fundamentals of Stochastic Models, 449–514. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003150060-10.

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Gómez M., Guillermo L. "Stochastic control theory". En Dynamic Probabilistic Models and Social Structure, 401–19. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2524-6_9.

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Benaroya, Haym. "Random Eigenvalues and Structural Dynamic Models". En Stochastic Structural Dynamics 1, 11–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84531-4_2.

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Chen, Huey-Kuo. "Stochastic/Dynamic User-Optimal Route Choice Model". En Dynamic Travel Choice Models, 229–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59980-4_12.

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Ran, Bin y David Boyce. "Instantaneous Stochastic Dynamic Route Choice Models". En Modeling Dynamic Transportation Networks, 211–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80230-0_10.

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Ran, Bin y David Boyce. "Ideal Stochastic Dynamic Route Choice Models". En Modeling Dynamic Transportation Networks, 181–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80230-0_9.

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Ravishanker, Nalini, Balaji Raman y Refik Soyer. "Modeling Stochastic Volatility". En Dynamic Time Series Models using R-INLA, 197–204. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003134039-10.

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Nijkamp, Peter y Aura Reggiani. "Dynamic and Stochastic Spatial Interaction Models". En Interaction, Evolution and Chaos in Space, 89–117. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77509-3_5.

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Tapiero, Charles S. "Dynamic Optimization". En Applied Stochastic Models and Control for Finance and Insurance, 237–74. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5823-1_6.

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Actas de conferencias sobre el tema "Dynamic stochastic models"

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Zhao, Lang, Xueying Wang, Yizheng Li, Cheng Chen, Yawen Qian, Peng Du, Hongtao Xie, Chen Zhang y Zhiyu Wang. "Stochastic Dynamic Economic Dispatch Models of Ultra High Voltage AC-DC Hybrid Grids Based on Approximate Dynamic Programming". En 2024 4th International Conference on Energy, Power and Electrical Engineering (EPEE), 887–91. IEEE, 2024. https://doi.org/10.1109/epee63731.2024.10875448.

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Robinson, Jace y Derek Doran. "Seasonality in dynamic stochastic block models". En WI '17: International Conference on Web Intelligence 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3106426.3109424.

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Rey, Francesc y Josep Sala-Alvarez. "Stochastic dynamic models in PHY abstraction". En 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810577.

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Ghorbanian, Parham, Subramanian Ramakrishnan y Hashem Ashrafiuon. "EEG Stochastic Nonlinear Oscillator Models for Alzheimer’s Disease". En ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9676.

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In this article, we derive unique stochastic nonlinear coupled oscillator models of EEG signals from an Alzheimer’s Disease (AD) study. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) conditions in a pilot study with AD patients and age-matched healthy control subjects (CTL) are employed. An optimization scheme is then utilized to match the output of the stochastic Duffing - van der Pol double oscillator network with EEG signals recorded during each condition for AD and CTL subjects. The selected decision variable are the model parameters and noise intensity. While, the selected signal characteristics are power spectral densities in major brain frequency bands and Shannon and sample entropies to match the signal information content and complexity. It is shown that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. Moreover, the inclusion of sample entropy in the optimization process significantly enhances the stochastic nonlinear oscillator model performance. The study suggests that EEG signals recorded under different brain states as well as those belonging to a brain disorder such as Alzheimer’s disease can be uniquely represented by stochastic nonlinear oscillators paving the way for identification of new discriminants.
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Chemistruck, Heather y John B. Ferris. "Compact Models of Terrain Surfaces". En ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4037.

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Terrain topology is the principal source of vertical excitation to the vehicle system and must be accurately represented in order to correctly predict the vehicle response. It is desirable to evaluate vehicle models and tire models over a wide range of terrain types, but it is computationally impractical to simulate long distances of every terrain variation. This work seeks to study the terrain surface, rather than the terrain profile, to maximize the information available to the tire model (i.e. wheel path data). A method to decompose the terrain surface as a combination of deterministic and stochastic components is presented. If some, or all, of the components of the terrain surface are considered to be stochastic, then the sequence can be modeled as a stochastic process. These stochastic representations of terrain surfaces can then be implemented in tire and vehicle models to predict chassis loads.
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Alexandre, Dolgui,. "Stochastic Dynamic Pricing Models of Monopoly Systems". En Information Control Problems in Manufacturing, editado por Bakhtadze, Natalia, chair Dolgui, Alexandre y Bakhtadze, Natalia. Elsevier, 2009. http://dx.doi.org/10.3182/20090603-3-ru-2001.00243.

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Sion, R. y J. Tatemura. "Dynamic stochastic models for workflow response optimization". En IEEE International Conference on Web Services (ICWS'05). IEEE, 2005. http://dx.doi.org/10.1109/icws.2005.50.

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Kashib, T. y S. Amanetu. "Dynamic Data Integration in Stochastic Reservoir Models". En Canadian International Petroleum Conference. Petroleum Society of Canada, 2003. http://dx.doi.org/10.2118/2003-091.

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Eliasi, Parisa A. y Sundeep Rangan. "Stochastic dynamic channel models for millimeter cellular systems". En 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2015. http://dx.doi.org/10.1109/camsap.2015.7383773.

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Kneser, R. y V. Steinbiss. "On the dynamic adaptation of stochastic language models". En Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319375.

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Informes sobre el tema "Dynamic stochastic models"

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Fernandez-Villaverde, Jesus, Pablo Guerrón-Quintana y Juan Rubio-Ramírez. Estimating Dynamic Equilibrium Models with Stochastic Volatility. Cambridge, MA: National Bureau of Economic Research, septiembre de 2012. http://dx.doi.org/10.3386/w18399.

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Pitarka, A. Testing Dynamic Earthquake Rupture Models Generated With Stochastic Stress Drop. Office of Scientific and Technical Information (OSTI), noviembre de 2018. http://dx.doi.org/10.2172/1490953.

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Judd, Kenneth, Lilia Maliar y Serguei Maliar. How to Solve Dynamic Stochastic Models Computing Expectations Just Once. Cambridge, MA: National Bureau of Economic Research, septiembre de 2011. http://dx.doi.org/10.3386/w17418.

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Judd, Kenneth, Lilia Maliar y Serguei Maliar. Numerically Stable Stochastic Simulation Approaches for Solving Dynamic Economic Models. Cambridge, MA: National Bureau of Economic Research, agosto de 2009. http://dx.doi.org/10.3386/w15296.

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Ghil, Michael, Mickael D. Chekroun, Dmitri Kondrashov, Michael K. Tippett, Andrew Robertson, Suzana J. Camargo, Mark Cane et al. Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2012. http://dx.doi.org/10.21236/ada572180.

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Gelain, Paolo y Pierlauro Lopez. A DSGE Model Including Trend Information and Regime Switching at the ZLB. Federal Reserve Bank of Cleveland, diciembre de 2023. http://dx.doi.org/10.26509/frbc-wp-202335.

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This paper outlines the dynamic stochastic general equilibrium (DSGE) model developed at the Federal Reserve Bank of Cleveland as part of the suite of models used for forecasting and policy analysis by Cleveland Fed researchers, which we have nicknamed CLEMENTINE (CLeveland Equilibrium ModEl iNcluding Trend INformation and the Effective lower bound). This document adopts a practitioner's guide approach, detailing the construction of the model and offering practical guidance on its use as a policy tool designed to support decision-making through forecasting exercises and policy counterfactuals.
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Chen, Xin, Yanfeng Ouyang, Ebrahim Arian, Haolin Yang y Xingyu Ba. Modeling and Testing Autonomous and Shared Multimodal Mobility Services for Low-Density Rural Areas. Illinois Center for Transportation, agosto de 2022. http://dx.doi.org/10.36501/0197-9191/22-013.

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Recent developments in transformative technologies hold the promise to provide holistic solutions for affordable transportation services to rural areas and thus greatly alleviate existing social inequality through efficient planning and management of complex transportation systems and systemwide interactions among multiple modes. To realize the promise, many challenging research questions need to be addressed, which often leads to computationally intractable, large-scale, dynamic/stochastic, discrete optimization models. This project proposes to address some of the challenges by building a series of holistic and tractable models on the design of mobility services, capacity planning, dynamic matching, and routing, as well as pricing. The proposed project is expected to create a new series of planning and management models that can support strategical and operational decisions for large-scale autonomous and shared mobility systems in rural areas. The planned case study and simulation for the Village of Rantoul, Illinois, will lay the foundation for future field implementation.
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Malin, Benjamin, Dirk Krueger y Felix Kubler. Computing Stochastic Dynamic Economic Models with a Large Number of State Variables: A Description and Application of a Smolyak-Collocation Method. Cambridge, MA: National Bureau of Economic Research, octubre de 2007. http://dx.doi.org/10.3386/t0345.

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Malin, Benjamin, Dirk Krueger y Felix Kubler. Computing Stochastic Dynamic Economic Models with a Large Number of State Variables: A Description and Application of a Smolyak-Collocation Method. Cambridge, MA: National Bureau of Economic Research, octubre de 2007. http://dx.doi.org/10.3386/w13517.

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Fernández-Villaverde, Jesús, Galo Nuño y Jesse Perla. Taming the curse of dimensionality: quantitative economics with deep learning. Madrid: Banco de España, noviembre de 2024. http://dx.doi.org/10.53479/38233.

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We argue that deep learning provides a promising approach to addressing the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges involved in solving dynamic equilibrium models, particularly the feedback loop between individual agents’ decisions and the aggregate consistency conditions required to achieve equilibrium. We then introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude with a review of the applications of neural networks in quantitative economics and provide arguments for cautious optimism.
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