{"id":"https://openalex.org/W2001168511","doi":"https://doi.org/10.1080/03610918.2015.1011335","title":"Bayesian averaging of classical estimates in asymmetric vector autoregressive models","display_name":"Bayesian averaging of classical estimates in asymmetric vector autoregressive models","publication_year":2015,"publication_date":"2015-03-25","ids":{"openalex":"https://openalex.org/W2001168511","doi":"https://doi.org/10.1080/03610918.2015.1011335","mag":"2001168511"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2015.1011335","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2015.1011335","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","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/A5002647631","display_name":"Manuel Leonard F. Albis","orcid":"https://orcid.org/0000-0003-1691-9153"},"institutions":[{"id":"https://openalex.org/I87074743","display_name":"University of the Philippines Diliman","ror":"https://ror.org/03tbh6y23","country_code":"PH","type":"education","lineage":["https://openalex.org/I103911934","https://openalex.org/I87074743"]}],"countries":["PH"],"is_corresponding":true,"raw_author_name":"Manuel Leonard F. Albis","raw_affiliation_strings":["School of Statistics, University of the Philippines, Diliman, Quezon City, Metro Manila, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics, University of the Philippines, Diliman, Quezon City, Metro Manila, Philippines","institution_ids":["https://openalex.org/I87074743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114250319","display_name":"Dennis S. Mapa","orcid":null},"institutions":[{"id":"https://openalex.org/I87074743","display_name":"University of the Philippines Diliman","ror":"https://ror.org/03tbh6y23","country_code":"PH","type":"education","lineage":["https://openalex.org/I103911934","https://openalex.org/I87074743"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Dennis S. Mapa","raw_affiliation_strings":["School of Statistics, University of the Philippines, Diliman, Quezon City, Metro Manila, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Statistics, University of the Philippines, Diliman, Quezon City, Metro Manila, Philippines","institution_ids":["https://openalex.org/I87074743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5002647631"],"corresponding_institution_ids":["https://openalex.org/I87074743"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.09204115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"46","issue":"3","first_page":"1760","last_page":"1770"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T10007","display_name":"Monetary Policy and Economic Impact","score":0.9988999962806702,"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"}},{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9902999997138977,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.688417911529541},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5313575863838196},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5246065855026245},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5079427361488342},{"id":"https://openalex.org/keywords/chen","display_name":"Chen","score":0.4847111701965332},{"id":"https://openalex.org/keywords/vector-autoregression","display_name":"Vector autoregression","score":0.44577598571777344},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4362199902534485},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4216915965080261},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.41183003783226013},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3321765065193176}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.688417911529541},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5313575863838196},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5246065855026245},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5079427361488342},{"id":"https://openalex.org/C2776085556","wikidata":"https://www.wikidata.org/wiki/Q183361","display_name":"Chen","level":2,"score":0.4847111701965332},{"id":"https://openalex.org/C133029050","wikidata":"https://www.wikidata.org/wiki/Q385593","display_name":"Vector autoregression","level":2,"score":0.44577598571777344},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4362199902534485},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4216915965080261},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41183003783226013},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3321765065193176},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2015.1011335","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2015.1011335","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1482953311","https://openalex.org/W1500470240","https://openalex.org/W1968720267","https://openalex.org/W1976093592","https://openalex.org/W1978068553","https://openalex.org/W1998650759","https://openalex.org/W2000842688","https://openalex.org/W2003068901","https://openalex.org/W2019459021","https://openalex.org/W2038909645","https://openalex.org/W2060558872","https://openalex.org/W2080720591","https://openalex.org/W2102680945","https://openalex.org/W2110787586","https://openalex.org/W2114580622","https://openalex.org/W2125078855","https://openalex.org/W2127263137","https://openalex.org/W2142635246","https://openalex.org/W2143428333","https://openalex.org/W2164141257","https://openalex.org/W2168175751","https://openalex.org/W3123723806","https://openalex.org/W3125696988"],"related_works":["https://openalex.org/W2370310922","https://openalex.org/W2386975637","https://openalex.org/W2891436901","https://openalex.org/W2087355708","https://openalex.org/W2388837937","https://openalex.org/W2385339070","https://openalex.org/W2390434941","https://openalex.org/W3196086590","https://openalex.org/W3197822338","https://openalex.org/W3124601637"],"abstract_inverted_index":{"ABSTRACTThe":[0],"estimated":[1,39,292],"vector":[2,35,329],"autoregressive":[3],"(VAR)":[4],"model":[5,9,60,209,287],"is":[6,38,187,192,203,288,307,342],"sensitive":[7],"to":[8,12,33,53,116,130,355,384,408,422],"misspecifications,":[10],"resulting":[11],"biased":[13],"and":[14,77,90,120,129,160,169,173,178,189,225,318,324,397],"inconsistent":[15],"parameter":[16],"estimates.":[17],"This":[18,401],"article":[19],"extends":[20],"the":[21,65,84,94,117,125,131,134,137,141,147,155,190,204,207,240,245,251,255,272,285,291,297,300,312,336,347,351,356,361,363,382,391,409],"Bayesian":[22],"averaging":[23],"of":[24,44,59,64,133,143,146,154,162,254,271,275,284,299,302,420],"classical":[25],"estimates,":[26],"a":[27,34,41,267,303,405,417],"robustness":[28],"procedure":[29,50,86,433],"in":[30,244,311,335,386,412],"cross-section":[31,423],"data,":[32],"time-series":[36,330,413],"that":[37,157,257,277],"using":[40,434],"large":[42,369],"number":[43],"asymmetric":[45],"VAR":[46,99,185,428],"models.":[47],"The":[48,62,183,234,282,315,344,376],"proposed":[49,85],"was":[51,366,378],"applied":[52],"simulated":[54],"data":[55,331,414],"from":[56,165,322,430],"various":[57],"forms":[58],"misspecifications.":[61],"results":[63,89],"simulation":[66],"suggest":[67],"that,":[68],"under":[69],"misspecification":[70],"problems,":[71],"particularly":[72],"if":[73,194,219],"an":[74,308],"important":[75,340],"variable":[76,304,341],"moving":[78],"average":[79],"(MA)":[80],"terms":[81],"were":[82],"omitted,":[83],"gives":[87,296],"robust":[88],"better":[91],"forecasts":[92],"than":[93],"automatically":[95],"selected":[96],"equal":[97],"lag-length":[98],"model.KEYWORDS:":[100],"AVARBACEForecastingRobustness":[101],"proceduresMATHEMATICS":[102],"SUBJECT":[103],"CLASSIFICATION:":[104],"62M10":[105],"Time":[106],"seriesauto-correlationregression91B84":[107],"Economic":[108],"time":[109],"series":[110],"analysis":[111],"AcknowledgmentsThe":[112],"authors":[113],"are":[114,164,236,320],"grateful":[115],"Statistical":[118,138],"Research":[119],"Training":[121],"Center":[122],"(SRTC)":[123],"for":[124,149,346,368,373,390],"thesis":[126],"fellowship":[127],"grant,":[128],"participants":[132],"Colloquium":[135],"on":[136],"Sciences":[139],"at":[140],"School":[142],"Statistics,":[144],"University":[145],"Philippines,":[148],"their":[150],"valuable":[151],"comments.Notes1":[152],"Some":[153],"studies":[156],"used":[158],"BVAR":[159],"variants":[161],"it":[163,295],"Po,":[166],"Chi,":[167],"Shyu,":[168],"Hsiao":[170],"Citation(2002),":[171],"Chen":[172],"Leung":[174],"Citation(2003),":[175,177],"Ramos":[176],"Carriero":[179],"et":[180],"al.":[181],"Citation(2009).2":[182],"he":[184],"operator":[186],"stable":[188],"process":[191],"stationary":[193],"det":[195],"A*":[196],"(z)":[197],"\u2260":[198],"0,":[199],"where":[200,215,338],"If":[201],"this":[202],"case,":[205],"then":[206],"VAR(p)":[208,286],"can":[210,258],"also":[211],"be":[212,259,333],"expressed":[213,260],"as":[214,239,294,404],"\u03a60":[216],"=":[217,221,223,228,263,280,395,399],"IK":[218],"A*0":[220],"M0*":[222],"IK,":[224],"with":[226],"A*j":[227],"0":[229],"or":[230],"j":[231],">":[232],"p":[233],"\u03a6i's":[235],"popularly":[237],"known":[238],"impulse":[241],"response":[242],"function":[243],"literature.":[246],"In":[247,360],"practice,":[248],"researchers":[249],"use":[250],"orthogonalized":[252],"form":[253],"IRF":[256,293],"by":[261,380],"\u03a6oi":[262],"\u03a6iL":[264],"here":[265],"L's":[266],"lower":[268],"triangular":[269],"matrix":[270],"Cholesky":[273],"decomposition":[274],"\u03a3,":[276],"is,":[278],"\u03a3":[279],"LL\u2032":[281],"interpretations":[283],"coursed":[289],"through":[290],"reaction":[298],"value":[301],"when":[305],"there":[306],"abrupt":[309],"change":[310],"other":[313],"variables.3":[314],"names":[316],"KAIC":[317],"KSIC":[319],"adapted":[321],"Ozcicek":[323],"McMillin":[325],"Citation(1999).4":[326],"A":[327],"four-dimensional":[328],"will":[332],"generated":[334],"cases":[337],"one":[339],"omitted.5":[343],"formula":[345],"posterior":[348,364],"probability":[349,365],"involves":[350],"SSE":[352,383,411],"being":[353],"raised":[354],"power":[357],"\u2212":[358],"n/2.":[359],"simulation,":[362],"zero":[367],"sample":[370,392],"size":[371],"T":[372,394,398],"small":[374],"SSE.":[375],"problem":[377],"remedied":[379],"raising":[381],"\u2212(0.1T)/2":[385],"Eq.":[387],"(Equation8(8)":[388],")":[389],"sizes":[393],"300":[396],"1,000.":[400],"stands":[402],"only":[403],"temporary":[406],"remedy":[407],"problem.":[410],"may":[415],"have":[416],"different":[418],"rate":[419],"convergence":[421],"data.":[424],"Table":[425,437],"2.":[426],"Average":[427],"lag-lengths":[429],"automatic":[431],"selection":[432],"AICc.Download":[435],"CSVDisplay":[436]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
