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Indirect Inference- a methodological essay on its role and applications

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In this short paper we review the intellectual history of indirect inference as a methodology in its progress from an informal method for evaluating early models of representative agents to formally testing DSGE models of the economy; and we have considered the issues that can arise in carrying out these tests. We have noted that it is asymptotically equivalent to using FIML-i.e. in large samples; and that in small samples it is superior to FIML both in lowering bias and achieving good power. In application its power needs to be evaluated by Monte Carlo experiment for the particular context. Structural models need to be defined in terms of their scope of application and auxiliary models chosen suitably to test their applicability within this scope. Power can be set too high by using too many auxiliary model features to match; and it can be pushed too low by using too few. Excessively high shocks, such as wars and crises, may also limit a model’s applicability by causing unusual behaviour that cannot be captured by the model. If so, these need to be excluded so that the model is evaluated for the ’normal times’ in which it is applicable.

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  • Minford, Patrick & Xu, Yongdeng, 2024. "Indirect Inference- a methodological essay on its role and applications," Cardiff Economics Working Papers E2024/1, Cardiff University, Cardiff Business School, Economics Section.
    • Handle: RePEc:cdf:wpaper:2024/1
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    1. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    2. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 63-84, Suppl. De.
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    4. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2017. "A Monte Carlo procedure for checking identification in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 202-210.
    5. Patrick Minford & Yue Gai & David Meenagh, 2022. "North and South: A Regional Model of the UK," Open Economies Review, Springer, vol. 33(3), pages 565-616, July.
    6. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    7. Minford, Patrick & Wickens, Michael & Xu, Yongdeng, 2016. "Comparing different data descriptors in Indirect Inference tests on DSGE models," Economics Letters, Elsevier, vol. 145(C), pages 157-161.
    8. George W. Evans & Seppo Honkapohja, 2005. "An Interview with Thomas J. Sargent," CESifo Working Paper Series 1434, CESifo.
    9. Minford, Patrick & Xu, Yongdeng & Dong, Xue, 2023. "Testing competing world trade models against the facts of world trade," Journal of International Money and Finance, Elsevier, vol. 138(C).
    10. David Meenagh & Patrick Minford & Michael Wickens & Yongdeng Xu, 2019. "Testing DSGE Models by Indirect Inference: a Survey of Recent Findings," Open Economies Review, Springer, vol. 30(3), pages 593-620, July.
    11. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2011. "How much nominal rigidity is there in the US economy? Testing a new Keynesian DSGE model using indirect inference," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2078-2104.
    12. Vo Le & David Meenagh & Patrick Minford & Michael Wickens & Yongdeng Xu, 2016. "Testing Macro Models by Indirect Inference: A Survey for Users," Open Economies Review, Springer, vol. 27(1), pages 1-38, February.
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