Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction
Summary The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective....
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| Veröffentlicht in: | Journal of applied econometrics (Chichester, England) England), 2021-08, Vol.36 (5), p.614-627 |
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| Hauptverfasser: | , |
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| Sprache: | eng |
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| container_end_page | 627 |
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| container_issue | 5 |
| container_start_page | 614 |
| container_title | Journal of applied econometrics (Chichester, England) |
| container_volume | 36 |
| creator | Li, Mengheng Koopman, Siem Jan |
| description | Summary
The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation‐based method is computationally feasible, for both univariate and multivariate models. We assess the performance of the method in a Monte Carlo study. In an empirical study, we analyse U.S. headline inflation using different univariate and multivariate model specifications. |
| doi_str_mv | 10.1002/jae.2831 |
| format | Article |
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The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation‐based method is computationally feasible, for both univariate and multivariate models. We assess the performance of the method in a Monte Carlo study. In an empirical study, we analyse U.S. headline inflation using different univariate and multivariate model specifications.</description><identifier>ISSN: 0883-7252</identifier><identifier>EISSN: 1099-1255</identifier><identifier>DOI: 10.1002/jae.2831</identifier><language>eng</language><publisher>Chichester: Wiley Periodicals Inc</publisher><subject>Bayesian analysis ; Econometrics ; Empirical analysis ; Extraction ; Inflation ; Maximum likelihood estimation ; Maximum likelihood method ; Monte Carlo simulation ; Multivariate analysis ; Parameter estimation ; Simulation ; Time series ; Volatility</subject><ispartof>Journal of applied econometrics (Chichester, England), 2021-08, Vol.36 (5), p.614-627</ispartof><rights>2021 The Authors. Journal of Applied Econometrics Published by John Wiley & Sons, Ltd.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3601-a3a445340c30f2c7e6393a718fb885ac0518f289a5a6640ebcf8a5aeef1b7fc03</citedby><cites>FETCH-LOGICAL-c3601-a3a445340c30f2c7e6393a718fb885ac0518f289a5a6640ebcf8a5aeef1b7fc03</cites><orcidid>0000-0002-4440-9524 ; 0000-0002-8807-7277</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjae.2831$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjae.2831$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,1414,27911,27912,45561,45562</link.rule.ids></links><search><creatorcontrib>Li, Mengheng</creatorcontrib><creatorcontrib>Koopman, Siem Jan</creatorcontrib><title>Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction</title><title>Journal of applied econometrics (Chichester, England)</title><description>Summary
The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation‐based method is computationally feasible, for both univariate and multivariate models. We assess the performance of the method in a Monte Carlo study. In an empirical study, we analyse U.S. headline inflation using different univariate and multivariate model specifications.</description><subject>Bayesian analysis</subject><subject>Econometrics</subject><subject>Empirical analysis</subject><subject>Extraction</subject><subject>Inflation</subject><subject>Maximum likelihood estimation</subject><subject>Maximum likelihood method</subject><subject>Monte Carlo simulation</subject><subject>Multivariate analysis</subject><subject>Parameter estimation</subject><subject>Simulation</subject><subject>Time series</subject><subject>Volatility</subject><issn>0883-7252</issn><issn>1099-1255</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp1kE1OwzAQhS0EEqUgcQRLbNikjO04cdhVVcuPKrGAri3HdairNC522tIdR-CMnASnZctq3jx9M3p6CF0TGBAAerdUZkAFIyeoR6AoEkI5P0U9EIIlOeX0HF2EsASADCDvITNrXBmM35o51m61do1p2oB3tl3g0Dq9UKG1Gm9drVpb23Z_j1_tatNtrvn5-i5ViJcmQquDhVUzx8G-N6rG5rP1SnfuJTqrVB3M1d_so9lk_DZ6TKYvD0-j4TTRLAOSKKbSlLMUNIOK6txkrGAqJ6IqheBKA4-SikJxlWUpmFJXImpjKlLmlQbWRzfHv2vvPjYxlVy6jY9ZgqQ8o4QLVohI3R4p7V0I3lRy7WN8v5cEZFeijCXKrsSIJkd0Z2uz_5eTz8Pxgf8FTOl17Q</recordid><startdate>202108</startdate><enddate>202108</enddate><creator>Li, Mengheng</creator><creator>Koopman, Siem Jan</creator><general>Wiley Periodicals Inc</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope><orcidid>https://orcid.org/0000-0002-4440-9524</orcidid><orcidid>https://orcid.org/0000-0002-8807-7277</orcidid></search><sort><creationdate>202108</creationdate><title>Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction</title><author>Li, Mengheng ; Koopman, Siem Jan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3601-a3a445340c30f2c7e6393a718fb885ac0518f289a5a6640ebcf8a5aeef1b7fc03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bayesian analysis</topic><topic>Econometrics</topic><topic>Empirical analysis</topic><topic>Extraction</topic><topic>Inflation</topic><topic>Maximum likelihood estimation</topic><topic>Maximum likelihood method</topic><topic>Monte Carlo simulation</topic><topic>Multivariate analysis</topic><topic>Parameter estimation</topic><topic>Simulation</topic><topic>Time series</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Mengheng</creatorcontrib><creatorcontrib>Koopman, Siem Jan</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Journal of applied econometrics (Chichester, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Mengheng</au><au>Koopman, Siem Jan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction</atitle><jtitle>Journal of applied econometrics (Chichester, England)</jtitle><date>2021-08</date><risdate>2021</risdate><volume>36</volume><issue>5</issue><spage>614</spage><epage>627</epage><pages>614-627</pages><issn>0883-7252</issn><eissn>1099-1255</eissn><abstract>Summary
The unobserved components time series model with stochastic volatility has gained much interest in econometrics, especially for the purpose of modelling and forecasting inflation. We present a feasible simulated maximum likelihood method for parameter estimation from a classical perspective. The method can also be used for evaluating the marginal likelihood function in a Bayesian analysis. We show that our simulation‐based method is computationally feasible, for both univariate and multivariate models. We assess the performance of the method in a Monte Carlo study. In an empirical study, we analyse U.S. headline inflation using different univariate and multivariate model specifications.</abstract><cop>Chichester</cop><pub>Wiley Periodicals Inc</pub><doi>10.1002/jae.2831</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-4440-9524</orcidid><orcidid>https://orcid.org/0000-0002-8807-7277</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Journal of applied econometrics (Chichester, England), 2021-08, Vol.36 (5), p.614-627 |
| issn | 0883-7252 1099-1255 |
| language | eng |
| recordid | cdi_proquest_journals_2562158398 |
| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Bayesian analysis Econometrics Empirical analysis Extraction Inflation Maximum likelihood estimation Maximum likelihood method Monte Carlo simulation Multivariate analysis Parameter estimation Simulation Time series Volatility |
| title | Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction |
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