Inference Using Simulated Neural Moments
This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals usi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Econometrics |
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| Online Access: | https://www.mdpi.com/2225-1146/9/4/35 |
| _version_ | 1797505314403123200 |
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| author | Michael Creel |
| author_facet | Michael Creel |
| author_sort | Michael Creel |
| collection | DOAJ |
| description | This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals using these methods may have coverage which is far from the nominal level, a result which has parallels in the literature that studies overidentified GMM estimators. A neural network may be used to reduce the dimension of an initial set of moments to the minimum number that maintains identification, as in Creel (2017). When MSM-MCMC estimation and inference is based on such moments, and using a continuously updating criteria function, confidence intervals have statistically correct coverage in all cases studied. The methods are illustrated by application to several test models, including a small DSGE model, and to a jump-diffusion model for returns of the S&P 500 index. |
| first_indexed | 2024-03-10T04:16:51Z |
| format | Article |
| id | doaj.art-4983214ee695441c8c5fe999ff00341e |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-03-10T04:16:51Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-4983214ee695441c8c5fe999ff00341e2023-11-23T07:58:14ZengMDPI AGEconometrics2225-11462021-09-01943510.3390/econometrics9040035Inference Using Simulated Neural MomentsMichael Creel0Department of Economics and Economic History and MOVE, Universitat Autònoma de Barcelona, 08193 Bellaterra, SpainThis paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals using these methods may have coverage which is far from the nominal level, a result which has parallels in the literature that studies overidentified GMM estimators. A neural network may be used to reduce the dimension of an initial set of moments to the minimum number that maintains identification, as in Creel (2017). When MSM-MCMC estimation and inference is based on such moments, and using a continuously updating criteria function, confidence intervals have statistically correct coverage in all cases studied. The methods are illustrated by application to several test models, including a small DSGE model, and to a jump-diffusion model for returns of the S&P 500 index.https://www.mdpi.com/2225-1146/9/4/35neural networksLaplace-type estimatorsapproximate Bayesian computingsimulated momentsjump diffusion |
| spellingShingle | Michael Creel Inference Using Simulated Neural Moments Econometrics neural networks Laplace-type estimators approximate Bayesian computing simulated moments jump diffusion |
| title | Inference Using Simulated Neural Moments |
| title_full | Inference Using Simulated Neural Moments |
| title_fullStr | Inference Using Simulated Neural Moments |
| title_full_unstemmed | Inference Using Simulated Neural Moments |
| title_short | Inference Using Simulated Neural Moments |
| title_sort | inference using simulated neural moments |
| topic | neural networks Laplace-type estimators approximate Bayesian computing simulated moments jump diffusion |
| url | https://www.mdpi.com/2225-1146/9/4/35 |
| work_keys_str_mv | AT michaelcreel inferenceusingsimulatedneuralmoments |