{"id":"https://openalex.org/W1515676410","doi":"https://doi.org/10.1007/978-3-642-57489-4_24","title":"Bayesian Automatic Parameter Estimation of Threshold Autoregressive (TAR) Models using Markov Chain Monte Carlo (MCMC)","display_name":"Bayesian Automatic Parameter Estimation of Threshold Autoregressive (TAR) Models using Markov Chain Monte Carlo (MCMC)","publication_year":2002,"publication_date":"2002-01-01","ids":{"openalex":"https://openalex.org/W1515676410","doi":"https://doi.org/10.1007/978-3-642-57489-4_24","mag":"1515676410"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-642-57489-4_24","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-642-57489-4_24","pdf_url":null,"source":{"id":"https://openalex.org/S4306506691","display_name":"COMPSTAT","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Compstat","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064066726","display_name":"Esmail Amiri","orcid":null},"institutions":[{"id":"https://openalex.org/I69425150","display_name":"Imam Khomeini International University","ror":"https://ror.org/02jeykk09","country_code":"IR","type":"education","lineage":["https://openalex.org/I69425150"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Esmail Amiri","raw_affiliation_strings":["Department of Mathematics & Statistics, Imam Khomeini International University, P.o.Box 288, Ghazvin, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics & Statistics, Imam Khomeini International University, P.o.Box 288, Ghazvin, Iran","institution_ids":["https://openalex.org/I69425150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5064066726"],"corresponding_institution_ids":["https://openalex.org/I69425150"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14948454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"189","last_page":"194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9828000068664551,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9828000068664551,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/2000","display_name":"General Economics, Econometrics and Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/setar","display_name":"SETAR","score":0.8897329568862915},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.836315393447876},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.7517720460891724},{"id":"https://openalex.org/keywords/star-model","display_name":"STAR model","score":0.7184069156646729},{"id":"https://openalex.org/keywords/threshold-model","display_name":"Threshold model","score":0.5596588253974915},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5549008250236511},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5326361656188965},{"id":"https://openalex.org/keywords/state-space-representation","display_name":"State-space representation","score":0.4123046100139618},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38237136602401733},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3781524896621704},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3046932816505432},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2959960401058197},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.2589426040649414}],"concepts":[{"id":"https://openalex.org/C30795276","wikidata":"https://www.wikidata.org/wiki/Q7389877","display_name":"SETAR","level":5,"score":0.8897329568862915},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.836315393447876},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7517720460891724},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.7184069156646729},{"id":"https://openalex.org/C202632270","wikidata":"https://www.wikidata.org/wiki/Q7798106","display_name":"Threshold model","level":2,"score":0.5596588253974915},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5549008250236511},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5326361656188965},{"id":"https://openalex.org/C52918065","wikidata":"https://www.wikidata.org/wiki/Q230945","display_name":"State-space representation","level":2,"score":0.4123046100139618},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38237136602401733},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3781524896621704},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3046932816505432},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2959960401058197},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.2589426040649414}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-642-57489-4_24","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-642-57489-4_24","pdf_url":null,"source":{"id":"https://openalex.org/S4306506691","display_name":"COMPSTAT","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Compstat","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W110508052","https://openalex.org/W1544729927","https://openalex.org/W1590093017","https://openalex.org/W1606326889","https://openalex.org/W1922851081","https://openalex.org/W1930187923","https://openalex.org/W1980264500","https://openalex.org/W1985037657","https://openalex.org/W1992397662","https://openalex.org/W2017899835","https://openalex.org/W2072208595","https://openalex.org/W2087618469","https://openalex.org/W2121448470","https://openalex.org/W2152828142","https://openalex.org/W4388215482","https://openalex.org/W6640294858"],"related_works":["https://openalex.org/W4320078083","https://openalex.org/W2019155478","https://openalex.org/W1988789713","https://openalex.org/W1611117054","https://openalex.org/W1515676410","https://openalex.org/W2183194195","https://openalex.org/W23105737","https://openalex.org/W3124189490","https://openalex.org/W2223569195","https://openalex.org/W1992397662"],"abstract_inverted_index":{"In":[0,41],"nonlinear":[1],"time":[2],"series":[3],"analysis":[4],"a":[5,16,24,49,66,70,123],"Threshold":[6,58],"Autoregressive":[7,59],"model":[8,22,27,68],"(TAR)":[9],"is":[10,23,33,55,72,82,97,131],"considered":[11],"as":[12],"an":[13],"approximation":[14],"to":[15,48,69,73,83,98],"Nonlinear":[17],"autoregressive":[18],"process(NLAR).":[19],"A":[20],"TAR":[21,52],"piece-wise":[25],"linear":[26,34],"over":[28],"the":[29,36,39,75,85,87,90,101,105,107,109,137],"state":[30],"space.":[31],"It":[32],"in":[35,64],"space":[37],"of":[38,51,77,92,103,113,119,136,143],"thresholds.":[40],"this":[42,117],"article":[43],"we":[44],"confine":[45],"our":[46],"studies":[47],"class":[50,118],"models":[53,121],"which":[54],"called":[56],"Self-Exciting":[57],"(SETAR).":[60],"The":[61,79],"first":[62],"step":[63,81],"fitting":[65],"SETAR":[67],"data-set":[71],"decide":[74],"number":[76,102],"regimes.":[78],"second":[80],"identify":[84],"delay,":[86,106],"threshold(thresholds)":[88],"and":[89,111],"order":[91,110],"each":[93,114],"regime.":[94],"Our":[95],"approach":[96,125],"estimate":[99],"automatically,":[100],"regimes,":[104],"threshold(thresholds),":[108],"parameters":[112],"regime":[115],"for":[116],"threshold":[120],"via":[122],"Bayesian":[124],"using":[126],"MCMC":[127],"methods.":[128],"Model":[129],"selection":[130],"done":[132],"through":[133],"calculating":[134],"mean":[135,139],"root":[138],"square":[140],"error":[141],"(MRMSE)":[142],"forecasts.":[144]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}