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On the application of automatic differentiation to the likelihood function for dynamic general equilibrium models

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Abstract

A key application of automatic differentiation (AD) is to facilitate numerical optimization problems. Such problems are at the core of many estimation techniques, including maximum likelihood. As one of the first applications of AD in the field of economics, we used Tapenade to construct derivatives for the likelihood function of any linear or linearized general equilibrium model solved under the assumption of rational expectations. We view our main contribution as providing an important check on finite-difference (FD) numerical derivatives. We also construct Monte Carlo experiments to compare maximum-likelihood estimates obtained with and without the aid of automatic derivatives. We find that the convergence rate of our optimization algorithm can increase substantially when we use AD derivatives.

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  • Houtan Bastani & Luca Guerrieri, 2008. "On the application of automatic differentiation to the likelihood function for dynamic general equilibrium models," International Finance Discussion Papers 920, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:920
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    1. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
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    3. Anderson, Gary, 1987. "A procedure for differentiating perfect-foresight-model reduced-from coefficients," Journal of Economic Dynamics and Control, Elsevier, vol. 11(4), pages 465-481, December.
    4. Erceg, Christopher J. & Henderson, Dale W. & Levin, Andrew T., 2000. "Optimal monetary policy with staggered wage and price contracts," Journal of Monetary Economics, Elsevier, vol. 46(2), pages 281-313, October.
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    Cited by:

    1. 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.
    2. Tore Selland Kleppe & Jun Yu & Hans J. skaug, 2011. "Simulated Maximum Likelihood Estimation for Latent Diffusion Models," Working Papers 10-2011, Singapore Management University, School of Economics.
    3. Adolfson, Malin & Lindé, Jesper, 2011. "Parameter Identification in a Estimated New Keynesian Open Economy Model," Working Paper Series 251, Sveriges Riksbank (Central Bank of Sweden).
    4. Skaug, Hans J. & Yu, Jun, 2014. "A flexible and automated likelihood based framework for inference in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 642-654.
    5. Cristian Homescu, 2011. "Adjoints and Automatic (Algorithmic) Differentiation in Computational Finance," Papers 1107.1831, arXiv.org.

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