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Structural estimation of continuous choice models: Evaluating the EGM and MPEC

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  • Jørgensen, Thomas H.

Abstract

In this paper, I evaluate the performance of two recently proposed approaches to solving and estimating structural models: The Endogenous Grid Method (EGM) and Mathematical Programming with Equilibrium Constraints (MPEC). Monte Carlo simulations confirm that both the EGM and MPEC have advantages relative to standard methods. The EGM proved particularly robust, fast and straightforward to implement. Approaches trying to avoid solving the model numerically, therefore, seem to be dominated by these approaches.

Suggested Citation

  • Jørgensen, Thomas H., 2013. "Structural estimation of continuous choice models: Evaluating the EGM and MPEC," Economics Letters, Elsevier, vol. 119(3), pages 287-290.
  • Handle: RePEc:eee:ecolet:v:119:y:2013:i:3:p:287-290
    DOI: 10.1016/j.econlet.2013.02.027
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    References listed on IDEAS

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    1. Giulio Fella, 2011. "A Generalized Endogenous Grid Method for Non-concave Problems," Working Papers 677, Queen Mary University of London, School of Economics and Finance.
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    5. Sule Alan & Martin Browning, 2003. "Estimating Intertemporal Allocation Parameters using Simulated Residual Estimation," CAM Working Papers 2003-03, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    6. Carroll, Christopher D., 2006. "The method of endogenous gridpoints for solving dynamic stochastic optimization problems," Economics Letters, Elsevier, vol. 91(3), pages 312-320, June.
    7. Sule Alan & Orazio Attanasio & Martin Browning, 2009. "Estimating Euler equations with noisy data: two exact GMM estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 309-324, March.
    8. Sule Alan & Martin Browning, 2010. "Estimating Intertemporal Allocation Parameters using Synthetic Residual Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(4), pages 1231-1261.
    9. Giulio Fella, 2011. "A generalized endogenous grid method for non-concave problems," 2011 Meeting Papers 1232, Society for Economic Dynamics.
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    Citations

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    Cited by:

    1. Fedor Iskhakov & Jinhyuk Lee & John Rust & Bertel Schjerning & Kyoungwon Seo, 2015. "Constrained Optimization Approaches to Estimation of Structural Models: Comment," Discussion Papers 15-05, University of Copenhagen. Department of Economics.
    2. Iskhakov, Fedor, 2015. "Multidimensional endogenous gridpoint method: Solving triangular dynamic stochastic optimization problems without root-finding operations," Economics Letters, Elsevier, vol. 135(C), pages 72-76.
    3. Borowczyk-Martins, Daniel & Bradley, Jake & Tarasonis, Linas, 2018. "Racial discrimination in the U.S. labor market: Employment and wage differentials by skill," Labour Economics, Elsevier, vol. 50(C), pages 45-66.
    4. Thomas H. Jørgensen, 2023. "Sensitivity to Calibrated Parameters," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 474-481, March.
    5. Alexander Ludwig & Matthias Schön, 2018. "Endogenous Grids in Higher Dimensions: Delaunay Interpolation and Hybrid Methods," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 463-492, March.
    6. Thomas H. Jørgensen, 2017. "Life-Cycle Consumption and Children: Evidence from a Structural Estimation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 717-746, October.
    7. Fedor Iskhakov & Thomas Høgholm Jørgensen & John Rust & Bertel Schjerning, 2015. "Estimating Discrete-Continuous Choice Models: The Endogenous Grid Method with Taste Shocks," Discussion Papers 15-19, University of Copenhagen. Department of Economics.

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    More about this item

    Keywords

    Structural estimation; Continuous choice; Endogenous Grid Method (EGM); Mathematical Programming with Equilibrium Constraints (MPEC);
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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