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Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data


  • Laura Liu

    (Indiana University)

  • Mikkel Plagborg-Møller

    (Princeton University)


We develop a generally applicable full-information inference method for heterogeneous agent models, combining aggregate time series data and repeated cross sections of micro data. To handle unobserved aggregate state variables that affect cross-sectional distributions, we compute a numerically unbiased estimate of the model-implied likelihood function. Employing the likelihood estimate in a Markov Chain Monte Carlo algorithm, we obtain fully efficient and valid Bayesian inference. Evaluation of the micro part of the likelihood lends itself naturally to parallel computing. Numerical illustrations in models with heterogeneous households or firms demonstrate that the proposed full-information method substantially sharpens inference relative to using only macro data, and for some parameters micro data is essential for identification.

Suggested Citation

  • Laura Liu & Mikkel Plagborg-Møller, 2021. "Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data," CAEPR Working Papers 2021-001 Classification- , Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
  • Handle: RePEc:inu:caeprp:2021001

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    References listed on IDEAS

    1. Aubhik Khan & Julia K. Thomas, 2008. "Idiosyncratic Shocks and the Role of Nonconvexities in Plant and Aggregate Investment Dynamics," Econometrica, Econometric Society, vol. 76(2), pages 395-436, March.
    2. Thomas Winberry, 2018. "A method for solving and estimating heterogeneous agent macro models," Quantitative Economics, Econometric Society, vol. 9(3), pages 1123-1151, November.
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    More about this item


    Bayesian inference; data combination; heterogeneous agent models;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models

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