Indirect inference

Gouriéroux, Christian; Monfort, Alain; Renault, Éric

doi:10.1002/jae.3950080507

Cited by 1,119 publications

(842 citation statements)

References 14 publications

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“…Indirect inference has been widely used in the estimation of structural models (e.g., Smith, 1993, Gregory and Smith, 1991, 1993, Gourieroux et al, 1993, Gourieroux and Monfort, 1995and Canova, 2005. Here we make a further use of indirect inference, to evaluate an already estimated or calibrated structural model.…”

Section: The Methods Of Indirect Inferencementioning

confidence: 99%

What Causes Banking Crises? An Empirical Investigation for the World Economy

Meenagh

Minford

et al. 2013

Open Econ Rev

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We add the Bernanke-Gertler-Gilchrist model to a world model consisting of the US, the Eurozone and the Rest of the World in order to explore the causes of the banking crisis. We test the model against linear-detrended data and reestimate it by indirect inference; the resulting model passes the Wald test only on outputs in the two countries. We then extract the model's implied residuals on un…ltered data to replicate how the model predicts the crisis. Banking shocks worsen the crisis but 'traditional' shocks explain the bulk of the crisis; the non-stationarity of the productivity shocks plays a key role. Crises occur when there is a 'run' of bad shocks; based on this sample Great Recessions occur on average once every quarter century. Financial shocks on their own, even when extreme, do not cause crises -provided the government acts swiftly to counteract such a shock as happened in this sample.

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Section: The Methods Of Indirect Inferencementioning

confidence: 99%

What Causes Banking Crises? An Empirical Investigation for the World Economy

Meenagh

Minford

et al. 2013

Open Econ Rev

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“…where Pakes and Pollard (1989)) sets Z n to sample moments; indirect inference (Gouriéroux et al (1993)) sets it to the parameter estimate of an auxiliary model, and the efficient method of moments (EMM - Gallant and Tauchen (1996)) sets it to the score of an auxiliary model. In principle, moments can be specified to use combinations of these ideas, and I refer to the general idea as an indirect inference estimator.…”

Section: Indirect Inferencementioning

confidence: 99%

“…This paper proposes to use neural nets for the estimation of the parameters of a simulable model, in situations where indirect inference (Gouriéroux et al (1993)) or Approximate Bayesian computing (Beaumont et al (2002)) might be used. Let θ be a draw of a parameter vector, from a prior.…”

Section: Introductionmentioning

confidence: 99%

Neural nets for indirect inference

Creel

2017

Econometrics and Statistics

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This paper shows how neural networks may be used to approximate the limited information posterior mean, E(θ|Zn), where θ is the parameter vector of a simulable model, and Zn is a vector of statistics. Because the model is simulable, training and testing samples may be generated with sizes large enough to train well a net that is large enough, in terms of number of hidden layers and neurons, to learn E(θ|Zn) with good accuracy. The output of the net can be used as an estimator of the parameter, or, following Jiang et al. (2015), as an input to subsequent classical or Bayesian indirect inference estimation. Targeting E(θ|Zn) using neural nets is simpler, faster, and more successful than is targeting the full information posterior mean, E(θ|Yn), where Yn is the sample data. Code to replicate the examples and to use the methods for other models is available at https://github.com/mcreel/NeuralNetsForIndirectInference.jl. This code uses the Mocha.jl package for the Julia language, which allows for easy access to GPU computing, which greatly accelerates training the net.

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“…This approach was suggested by Gouri eroux, Monfort and Renault 1993]. which is also used in Breitung and Lechner 1996] and Bertschek and Lechner 1998].…”

Section: A Minimum Distance Approachmentioning

confidence: 99%