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1

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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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

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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10

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

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11

Carroll, Chris. The method of endogenous gridpoints for solving dynamic stochastic optimization problems. Cambridge, MA: National Bureau of Economic Research, 2005.

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12

Brock, William A. A dynamic structural model for stock return volatility and trading volume. Cambridge, MA: National Bureau of Economic Research, 1995.

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13

Lam, Jean-Paul. Estimating policy-neutral interest rates for Canada using a dynamic stochastic general-equilibrium framework. Ottawa, Ont: Bank of Canada, 2004.

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14

Lam, Jean-Paul. Estimating policy-neutral interest rates for Canada using a dynamic stochastic general-equilibrium framework. Ottawa: Bank of Canada, 2004.

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15

Lam, Jean-Paul. Estimating policy-neutral interest rates for Canada using a dynamic stochastic general-equilibrium framework. Ottawa: Bank of Canada, 2004.

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16

Lam, Jean-Paul. Estimating policy-neutral interest rates for Canada using a dynamic stochastic general-equilibrium framework. Ottawa: Bank of Canada, 2004.

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17

Pakanen, Jouko. Prediction and fault detection of building energy consumption using multi-input, single-output dynamic model. Espoo: Technical Research Centre of Finland, 1992.

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18

Paul G. J. ten Brummelhuis. A stochastic dynamic approach to tidal modelling in estuaries, with an application to the eastern Scheldt. [The Netherlands?: s.n., 1987.

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19

Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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20

Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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21

Malin, Benjamin. 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, 2007.

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22

Malin, Benjamin. Computing stochastic dynamic economic models with a large number of state variables: A description and application of a Smolyak-collocation method. Cambridge, Mass: National Bureau of Economic Research, 2007.

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23

Bergstrom, A. R. Gaussian estimation of mixed order continuous time dynamic models with unobservable stochastic trends from mixed stock and flow data. [Colchester]: University of Essex, Department of Economics, 1995.

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24

S, Jensen Bjarne y Palokangas Tapio, eds. Stochastic economic dynamics. [Copenhagen?]: Copenhagen Business School Press, 2007.

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25

V, Evstigneev I., Medova E. A y Dempster, M. A. H. 1938-, eds. Stochastic models of control and economic dynamics. London: Academic Press, 1987.

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26

Herrmann, Samuel. Stochastic resonance: A mathematical approach in the small noise limit. Providence, Rhode Island: American Mathematical Society, 2014.

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27

Kollintzas, Tryphon. A stochastic dynamic general equilibrium model for Greece. London: Centre for Economic Policy Research, 1996.

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28

Benth, Fred Espen y Paul Krühner. Stochastic Models for Prices Dynamics in Energy and Commodity Markets. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40367-5.

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29

Androkovich, R. A. A stochastic dynamic programming model of bycatch control in fisheries. Portsmouth: University of Portsmouth, Centre for Marine Resource Economics, 1992.

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30

Archibald, T. W. An aggregate stochastic dynamic programming model of multi-reservoir systems. Edinburgh: University of Edinburgh, Management School, 1996.

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31

Archibald, T. W. An aggregate stochastic dynamic programming model of multiple reservoir systems. Edinburgh: Department of Business Studies, University of Edinburgh, 1995.

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32

Froot, Kenneth. Exchange rate dynamics under stochastic regime shifts: A unified approach. London: Centre for Economic Policy Research, 1991.

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33

Froot, Kenneth. Exchange rate dynamics under stochastic regime shifts: A unified approach. Cambridge, MA: National Bureau of Economic Research, 1989.

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34

Wilkinson, Darren James. Stochastic modelling for systems biology. Boca Raton, FL: Chapman & Hall/CRC Press, 2007.

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35

Mikhailov, A. S. From cells to societies: Models of complex coherent action. Berlin: Springer, 2002.

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36

Friedrich, Hermann. Die Antwortspektrenmethode bei stochastisch belasteten mechanischen Systemen. Karl-Marx-Stadt: Akademie der Wissenschaften der DDR, 1986.

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37

Corless, Martin J. AIMD dynamics and distributed resource allocation. Philadelphia: Society for Industrial and Applied Mathematics, 2016.

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38

Juillard, Michel. Dynamic Stochastic General Equilibrium Models. Editado por Shu-Heng Chen, Mak Kaboudan y Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.4.

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Dynamic Stochastic General Equilibrium (DSGE) models have become popular in macroeconomics, but the combination of nonlinear microeconomic behavior of the agents and model-consistent expectations raise intricate computational issues; this chapter reviews solution methods and estimation of DSGE models. Perfect foresight deterministic models can easily be solved with a great degree of accuracy. In practice, medium-sized stochastic models can only be solved by local approximation or the perturbation approach. The Bayesian approach to estimation is privileged. It provides a convenient way to communicate both the prior information available to the econo-metrician and new information revealed by the data. This chapter focuses on methods frequently used in applied work rather than aiming at being exhaustive.
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39

Dynamic programming: Deterministic and stochastic models. Englewood Cliffs, N.J: Prentice-Hall, 1987.

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40

Dynamic Optimization: Deterministic and Stochastic Models. Springer International Publishing AG, 2017.

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41

Wolters, J. Stochastic Dynamic Properties of Linear Econometric Models. Springer, 2012.

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42

Yeung, David W. K. Dynamic Consumer Theory: A Premier Treatise with Stochastic Dynamic Slutsky Equations. Nova Science Publishers, Incorporated, 2014.

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43

Travaglini, Guiseppe y Alessandro Bellocchi. Notes on Consumption Theory: Deterministic and Stochastic Dynamic Models. Springer, 2024.

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44

Semmier, Willi y Gang Gong. Stochastic Dynamic Macroeconomics: Theory and Empirical Evidence. Oxford University Press, 2006.

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45

Gong, Gang y Willi Semmler. Stochastic Dynamic Macroeconomics: Theory and Empirical Evidence. Oxford University Press, Incorporated, 2005.

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46

Sosa, Marco Antonio Ortiz. Dynamic Stochastic General Equilibrium Models : Real Business Cycles Models: Closed and Open Economy. Springer, 2024.

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47

Azcue, Pablo y Nora Muler. Stochastic Optimization in Insurance: A Dynamic Programming Approach. Springer London, Limited, 2014.

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48

Colander, David. Post Walrasian Macroeconomics: Beyond the Dynamic Stochastic General Equilibrium Model. Cambridge University Press, 2006.

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49

Colander, David. Post Walrasian Macroeconomics: Beyond the Dynamic Stochastic General Equilibrium Model. Cambridge University Press, 2006.

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50

Henderson, Daniel A., R. J. Boys, Carole J. Proctor y Darren J. Wilkinson. Linking systems biology models to data: A stochastic kinetic model of p53 oscillations. Editado por Anthony O'Hagan y Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.7.

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This article discusses the use of a stochastic kinetic model to study protein level oscillations in single living cancer cells, using the p53 and Mdm2 proteins as examples. It describes the refinement of a dynamic stochastic process model of the cellular response to DNA damage and compares this model to time course data on the levels of p53 and Mdm2. The article first provides a biological background on p53 and Mdm2 before explaining how the stochastic kinetic model is constructed. It then introduces the stochastic kinetic model and links it to the data and goes on to apply sophisticated MCMC methods to compute posterior distributions. The results demonstrate that it is possible to develop computationally intensive Markov chain Monte Carlo (MCMC) methods for conducting a Bayesian analysis of an intra-cellular stochastic systems biology model using single-cell time course data.
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