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Recursive Models of Dynamic Linear Economies

Author

Listed:
  • Lars Peter Hansen

    (University of Chicago)

  • Thomas J. Sargent

    (New York University
    Stanford University)

Abstract

A common set of mathematical tools underlies dynamic optimization, dynamic estimation, and filtering. In Recursive Models of Dynamic Linear Economies, Lars Peter Hansen and Thomas Sargent use these tools to create a class of econometrically tractable models of prices and quantities. They present examples from microeconomics, macroeconomics, and asset pricing. The models are cast in terms of a representative consumer. While Hansen and Sargent demonstrate the analytical benefits acquired when an analysis with a representative consumer is possible, they also characterize the restrictiveness of assumptions under which a representative household justifies a purely aggregative analysis. Based on the 2012 Gorman lectures, the authors unite economic theory with a workable econometrics while going beyond and beneath demand and supply curves for dynamic economies. They construct and apply competitive equilibria for a class of linear-quadratic-Gaussian dynamic economies with complete markets. Their book stresses heterogeneity, aggregation, and how a common structure unites what superficially appear to be diverse applications. An appendix describes MATLAB programs that apply to the book's calculations.

Suggested Citation

  • Lars Peter Hansen & Thomas J. Sargent, 2013. "Recursive Models of Dynamic Linear Economies," Economics Books, Princeton University Press, edition 1, number 10141.
  • Handle: RePEc:pup:pbooks:10141
    as

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    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Connecting the Academic and Policy Worlds: Interview with James Bullard
      by David Andolfatto in MacroMania on 2013-11-26 03:40:00

    Citations

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    Cited by:

    1. David Backus & Mikhail Chernov & Stanley E. Zin & Irina Zviadadze, 2013. "Identifying monetary policy in macro-finance models," NBER Working Papers 19360, National Bureau of Economic Research, Inc.
    2. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2015. "A Tractable Framework for Analyzing a Class of Nonstationary Markov Models," Economics Working Papers 15105, Hoover Institution, Stanford University.
    3. Backus, David & Chernov, Mikhail & Zin, Stanley E., 2013. "Identifying Taylor rules in macro-finance models," CEPR Discussion Papers 9611, C.E.P.R. Discussion Papers.
    4. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.),Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    5. Giannoni, Marc P. & Woodford, Michael, 2017. "Optimal target criteria for stabilization policy," Journal of Economic Theory, Elsevier, vol. 168(C), pages 55-106.
    6. Al-Sadoon, Majid M., 2014. "Geometric and long run aspects of Granger causality," Journal of Econometrics, Elsevier, vol. 178(P3), pages 558-568.
    7. James Mitchell & Donald Robertson & Stephen Wright, 2019. "R2 Bounds for Predictive Models: What Univariate Properties Tell us About Multivariate Predictability," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 681-695, October.
    8. Mitchell, James & Robertson, Donald & Wright, Stephen, 2016. "What univariate models tell us about multivariate macroeconomic models," EMF Research Papers 08, Economic Modelling and Forecasting Group.
    9. Huang, Danyang & Wang, Feifei & Zhu, Xuening & Wang, Hansheng, 2020. "Two-mode network autoregressive model for large-scale networks," Journal of Econometrics, Elsevier, vol. 216(1), pages 203-219.
    10. Wang, Qing & Yu, Xiangrong, 2017. "Family linkages, social interactions, and investment in human capital: A theoretical analysis," Journal of Comparative Economics, Elsevier, vol. 45(2), pages 271-286.
    11. Paul Beaudry & Patrick Feve & Alain Guay & Franck Portier, 2019. "When is Nonfundamentalness in SVARs a Real Problem?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 34, pages 221-243, October.
    12. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
    13. Vitale, Paolo, 2018. "Optimal monetary policy for a pessimistic central bank," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 39-59.
    14. Rostagno, Massimo & Altavilla, Carlo & Carboni, Giacomo & Lemke, Wolfgang & Motto, Roberto & Saint Guilhem, Arthur & Yiangou, Jonathan, 2019. "A tale of two decades: the ECB’s monetary policy at 20," Working Paper Series 2346, European Central Bank.
    15. Steven Kou & Xianhua Peng & Xingbo Xu, 2016. "EM Algorithm and Stochastic Control in Economics," Papers 1611.01767, arXiv.org.
    16. Serguei Maliar & John Taylor & Lilia Maliar, 2016. "The Impact of Alternative Transitions to Normalized Monetary Policy," 2016 Meeting Papers 794, Society for Economic Dynamics.
    17. Paolo Vitale, 2017. "Pessimistic Optimal Choice for Risk-Averse Agents: The Continuous-Time Limit," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 17-65, January.

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