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Recursive Models of Dynamic Linear Economies


  • Lars Peter Hansen

    (University of Chicago)

  • Thomas J. Sargent

    (New York University
    Stanford University)


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

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    Blog mentions

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    1. Connecting the Academic and Policy Worlds: Interview with James Bullard
      by David Andolfatto in MacroMania on 2013-11-26 03:40:00


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    Cited by:

    1. Backus, David & Zin, Stanley E. & Chernov, Mikhail & Zviadadze, Irina, 2013. "Monetary policy risk: Rules vs. discretion," CEPR Discussion Papers 9611, C.E.P.R. Discussion Papers.
    2. Edilio Valentini & Paolo Vitale, 2022. "A Dynamic Oligopoly with Price Stickiness and Risk-Averse Agents," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(3), pages 697-718, November.
    3. Min Dai & Steven Kou & H. Mete Soner & Chen Yang, 2023. "Leveraged Exchange-Traded Funds with Market Closure and Frictions," Management Science, INFORMS, vol. 69(4), pages 2517-2535, April.
    4. Al-Sadoon, Majid M., 2014. "Geometric and long run aspects of Granger causality," Journal of Econometrics, Elsevier, vol. 178(P3), pages 558-568.
    5. Lilia Maliar & Serguei Maliar & John B. Taylor & Inna Tsener, 2020. "A tractable framework for analyzing a class of nonstationary Markov models," Quantitative Economics, Econometric Society, vol. 11(4), pages 1289-1323, November.
    6. Giannoni, Marc P. & Woodford, Michael, 2017. "Optimal target criteria for stabilization policy," Journal of Economic Theory, Elsevier, vol. 168(C), pages 55-106.
    7. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014,
    8. 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.
    9. Serguei Maliar & John Taylor & Lilia Maliar, 2016. "The Impact of Alternative Transitions to Normalized Monetary Policy," 2016 Meeting Papers 794, Society for Economic Dynamics.
    10. 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.
    11. Johannes Huber, 2022. "An Augmented Steady-State Kalman Filter to Evaluate the Likelihood of Linear and Time-Invariant State-Space Models," Discussion Paper Series 343, Universitaet Augsburg, Institute for Economics.
    12. 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.
    13. 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.
    14. 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.
    15. Francisco J. Ruge-Murcia & Alexander L. Wolman, 2022. "Relative Price Shocks and Inflation," Working Paper 22-07, Federal Reserve Bank of Richmond.
    16. 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.
    17. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    18. Vitale, Paolo, 2018. "Optimal monetary policy for a pessimistic central bank," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 39-59.
    19. Harrison, Richard & Waldron, Matt, 2021. "Optimal policy with occasionally binding constraints: piecewise linear solution methods," Bank of England working papers 911, Bank of England.
    20. 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.
    21. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    22. David Backus & Mikhail Chernov & Stanley Zin, 2013. "Identifying Taylor Rules in Macro-finance Models," Working Papers 13-12, New York University, Leonard N. Stern School of Business, Department of Economics.
    23. 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.
    24. Steven Kou & Xianhua Peng & Xingbo Xu, 2016. "EM Algorithm and Stochastic Control in Economics," Papers 1611.01767,


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