Neural nets for indirect inference
For simulable models, neural networks are used to approximate the limited information posterior mean, which conditions on a vector of statistics, rather than on the full sample. Because the model is simulable, training and testing samples may be generated with sizes large enough to train well a net...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:174326 |
| Acceso en línea: | https://ddd.uab.cat/record/174326 https://dx.doi.org/urn:doi:10.1016/j.ecosta.2016.11.008 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks Indirect inference Approximate Bayesian computing Machine learning DSGE Jump-diffusion |
| Sumario: | For simulable models, neural networks are used to approximate the limited information posterior mean, which conditions on a vector of statistics, rather than on the full sample. 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 the limited information posterior mean with good accuracy. Targeting the limited information posterior mean using neural nets is simpler, faster, and more successful than is targeting the full information posterior mean, which conditions on the observed sample. The output of the trained net can be used directly as an estimator of the model's parameters, or as an input to subsequent classical or Bayesian indirect inference estimation. The methods are illustrated with applications to a small dynamic stochastic general equilibrium model and a continuous time jump-diffusion model for stock index returns. |
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