1b76376aa4a3d3f1a619baaa36882d36 econometrics_a2021v9n4p35.pdf e29744f09d242fb51cceb52d1931dd309ba3fce5 econometrics_a2021v9n4p35.pdf 1d69b1775e32ac1d4580be7d75254e210132489264a41909804114400951a65b econometrics_a2021v9n4p35.pdf Title: Inference Using Simulated Neural Moments Subject: 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. Keywords: neural networks; Laplace-type estimators; approximate Bayesian computing, simulated moments; jump diffusion Author: Michael Creel Creator: LaTeX with hyperref Producer: MiKTeX pdfTeX-1.40.21 CreationDate: Fri Sep 24 15:56:18 2021 CEST ModDate: Fri Sep 24 16:03:49 2021 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 15 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 546100 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- PFWUXO+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 GPYWUR+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 TZHLCO+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 DHQHRL+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 27 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 44 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 48 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 52 0 UCVETT+CMEX10 Type 1 Builtin yes yes no 64 0 RDNYWY+PazoMath-Italic Type 1 Builtin yes yes no 68 0 NMTTXM+PazoMath Type 1 Builtin yes yes no 72 0 GIGFZE+CMR10 Type 1 Builtin yes yes no 76 0 EXCUGD+CMSY10 Type 1 Builtin yes yes no 80 0 QDTWCG+MSBM10 Type 1 Builtin yes yes no 84 0 NNCWEN+URWPalladioL-BoldItal Type 1 Custom yes yes no 103 0 RLYTRW+CMMI10 Type 1 Builtin yes yes no 125 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-10-02 03:45:19 CEST RepresentationInformation: econometrics_a2021v9n4p35.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2023-03-31 11:57:37 CEST Size: 546100 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 333 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Simulated Moments, Indirect Likelihood, and MSM-MCMC Inference Destination: section.2 Item: Title: Neural Moments Destination: section.3 Item: Title: Examples Destination: section.4 Children: Item: Title: Stochastic Volatility Destination: subsection.4.1 Item: Title: ARMA Destination: subsection.4.2 Item: Title: Mixture of Normals Destination: subsection.4.3 Item: Title: DSGE Model Destination: subsection.4.4 Item: Title: Monte Carlo Results Destination: section.5 Item: Title: Application: A Jump-Diffusion Model of S&P 500 Returns Destination: section.6 Item: Title: Conclusions Destination: section.7 Item: Title: References Destination: section.7 Info: Title: Inference Using Simulated Neural Moments Author: Michael Creel Subject: 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. 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