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Computational Methods for the Study of Dynamic Economies

Editor

Listed:
  • Marimon, Ramon
    (European University Institute, Florence)

  • Scott, Andrew
    (London Business School)

Abstract

Macroeconomics increasingly uses stochastic dynamic general equilibrium models to understand theoretical and policy issues. Unless very strong assumptions are made, understanding the properties of particular models requires solving the model using a computer. This volume brings together leading contributors in the field who explain in detail how to implement the computational techniques needed to solve dynamic economics models. A broad spread of techniques are covered, and their application in a wide range of subjects discussed. The book provides the basics of a toolkit which researchers and graduate students can use to solve and analyse their own theoretical models. Contributors to this volume - Ramon Marimon and Andrew Scott Javier Diaz-Gimenez Harald Uhlig Alfonso Novales, Emilio Dominguez, Javier Perez and Jesus Ruiz Craig Burnside Ellen McGratten Albert Marcet and Guido Lorenzoni Graham V. Candler Thomas J. Sargent and Francois R. Velde Douglas H. Joines, Ayse Imrohoroglu and Selo Imrohoroglu Jose Victor Rios-Rull

Suggested Citation

  • Marimon, Ramon & Scott, Andrew (ed.), 2001. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780199248278.
    • Handle: RePEc:oxp:obooks:9780199248278
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    Cited by:

    1. Frank Hespeler, 2008. "Solution Algorithm to a Class of Monetary Rational Equilibrium Macromodels with Optimal Monetary Policy Design," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 207-223, April.
    2. Yann Algan & Edouard Challe & Xavier Ragot, 2011. "Incomplete markets and the output–inflation tradeoff," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 46(1), pages 55-84, January.
    3. Mikhail Ossama, 2005. "Economic Freedom and the Business Cycle: The Egyptian Experience," Review of Middle East Economics and Finance, De Gruyter, vol. 3(1), pages 1-19, April.
    4. Miroljub Labus & Milica Labus, 2019. "Monetary Transmission Channels in DSGE Models: Decomposition of Impulse Response Functions Approach," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 27-50, January.
    5. Robert Kirkby Author-Email: robertkirkby@gmail.com|, 2017. "Convergence of Discretized Value Function Iteration," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 117-153, January.
    6. Jenö Pál & John Stachurski, 2011. "Fitted Value Function Iteration With Probability One Contractions," ANU Working Papers in Economics and Econometrics 2011-560, Australian National University, College of Business and Economics, School of Economics.
    7. Maurizio Bovi & Roy Cerqueti, 2016. "Forecasting macroeconomic fundamentals in economic crises," Annals of Operations Research, Springer, vol. 247(2), pages 451-469, December.
    8. John Stachurski, 2009. "Economic Dynamics: Theory and Computation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262012774, April.
    9. Robert Kirkby, 2016. "Value Function Iteration Toolkit: In Matlab, on the GPU," EcoMod2016 9122, EcoMod.
    10. Robert Kirkby, 2017. "A Toolkit for Value Function Iteration," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 1-15, January.
    11. Keyvan Eslami & Tom Phelan, 2023. "The Art of Temporal Approximation An Investigation into Numerical Solutions to Discrete and Continuous-Time Problems in Economics," Working Papers 23-10, Federal Reserve Bank of Cleveland.

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