{"id":"https://openalex.org/W7204248317","doi":"https://doi.org/10.30598/barekengvol20iss4pp3241-3258","title":"VECTOR AUTOREGRESSION AND MULTIRESPONSE REGRESSION APPROACHES FOR MODELING GOLD, TIN, AND NICKEL PRICES","display_name":"VECTOR AUTOREGRESSION AND MULTIRESPONSE REGRESSION APPROACHES FOR MODELING GOLD, TIN, AND NICKEL PRICES","publication_year":2026,"publication_date":"2026-08-24","ids":{"openalex":"https://openalex.org/W7204248317","doi":"https://doi.org/10.30598/barekengvol20iss4pp3241-3258"},"language":"en","primary_location":{"id":"doi:10.30598/barekengvol20iss4pp3241-3258","doi":"https://doi.org/10.30598/barekengvol20iss4pp3241-3258","is_oa":true,"landing_page_url":"https://doi.org/10.30598/barekengvol20iss4pp3241-3258","pdf_url":"https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/22268/13152","source":{"id":"https://openalex.org/S4210230638","display_name":"BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN","issn_l":"1978-7227","issn":["1978-7227","2615-3017"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BAREKENG: Jurnal Ilmu Matematika dan Terapan","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/22268/13152","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046306756","display_name":"Suliyanto Suliyanto","orcid":"https://orcid.org/0009-0002-0850-7888","observed_orcids":["https://orcid.org/0009-0002-0850-7888"]},"institutions":[{"id":"https://openalex.org/I205133468","display_name":"Universitas Airlangga","ror":"https://ror.org/04ctejd88","country_code":"ID","type":"education","lineage":["https://openalex.org/I205133468"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Suliyanto Suliyanto","raw_affiliation_strings":["Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia"],"raw_orcid":"https://orcid.org/0009-0002-0850-7888","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia","institution_ids":["https://openalex.org/I205133468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070341940","display_name":"Dita Amelia","orcid":"https://orcid.org/0000-0002-2387-9981","observed_orcids":["https://orcid.org/0000-0002-2387-9981"]},"institutions":[{"id":"https://openalex.org/I205133468","display_name":"Universitas Airlangga","ror":"https://ror.org/04ctejd88","country_code":"ID","type":"education","lineage":["https://openalex.org/I205133468"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Dita Amelia","raw_affiliation_strings":["Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia"],"raw_orcid":"https://orcid.org/0000-0002-2387-9981","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia","institution_ids":["https://openalex.org/I205133468"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116727029","display_name":"Gabriella Agnes Budijono","orcid":"https://orcid.org/0009-0006-7870-2637","observed_orcids":["https://orcid.org/0009-0006-7870-2637"]},"institutions":[{"id":"https://openalex.org/I205133468","display_name":"Universitas Airlangga","ror":"https://ror.org/04ctejd88","country_code":"ID","type":"education","lineage":["https://openalex.org/I205133468"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Gabriella Agnes Budijono","raw_affiliation_strings":["Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia"],"raw_orcid":"https://orcid.org/0009-0006-7870-2637","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia","institution_ids":["https://openalex.org/I205133468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5148301246","display_name":"Rere Fetri Damanik","orcid":"https://orcid.org/0009-0007-8703-6952","observed_orcids":["https://orcid.org/0009-0007-8703-6952"]},"institutions":[{"id":"https://openalex.org/I205133468","display_name":"Universitas Airlangga","ror":"https://ror.org/04ctejd88","country_code":"ID","type":"education","lineage":["https://openalex.org/I205133468"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Rere Fetri Damanik","raw_affiliation_strings":["Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia"],"raw_orcid":"https://orcid.org/0009-0007-8703-6952","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia","institution_ids":["https://openalex.org/I205133468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205133468"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.5691628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":"4","first_page":"3241","last_page":"3258"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9994999766349792,"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/T11898","display_name":"Economics of Agriculture and Food Markets","score":0.00039999998989515007,"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/T10007","display_name":"Monetary Policy and Economic Impact","score":9.999999747378752e-05,"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/gold-prices","display_name":"gold prices","score":0.9509999752044678},{"id":"https://openalex.org/keywords/tin-prices","display_name":"tin prices","score":0.9890000224113464},{"id":"https://openalex.org/keywords/nickel-prices","display_name":"nickel prices","score":1.0},{"id":"https://openalex.org/keywords/commodity-price-dynamics","display_name":"commodity price dynamics","score":0.8169999718666077},{"id":"https://openalex.org/keywords/multivariate-regression","display_name":"multivariate regression","score":0.671999990940094},{"id":"https://openalex.org/keywords/vector-autoregression","display_name":"vector autoregression","score":0.9459999799728394},{"id":"https://openalex.org/keywords/oil-prices","display_name":"oil prices","score":0.9539999961853027},{"id":"https://openalex.org/keywords/us-dollar-exchange-rate","display_name":"US dollar exchange rate","score":0.8859999775886536},{"id":"https://openalex.org/keywords/silver-prices","display_name":"silver prices","score":1.0},{"id":"https://openalex.org/keywords/covid-19","display_name":"COVID-19 pandemic","score":0.6790000200271606},{"id":"https://openalex.org/keywords/commodity-price-volatility","display_name":"commodity price volatility","score":0.6790000200271606},{"id":"https://openalex.org/keywords/price-forecasting","display_name":"price forecasting","score":0.44999998807907104},{"id":"https://openalex.org/keywords/impulse-response","display_name":"impulse response","score":0.5889999866485596},{"id":"https://openalex.org/keywords/variance-decomposition","display_name":"variance decomposition","score":0.9520000219345093}],"concepts":[{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.7461000084877014},{"id":"https://openalex.org/C2776988154","wikidata":"https://www.wikidata.org/wiki/Q66100","display_name":"Exchange rate","level":2,"score":0.6589999794960022},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.6413999795913696},{"id":"https://openalex.org/C133029050","wikidata":"https://www.wikidata.org/wiki/Q385593","display_name":"Vector autoregression","level":2,"score":0.6226999759674072},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5936999917030334},{"id":"https://openalex.org/C145162277","wikidata":"https://www.wikidata.org/wiki/Q1751675","display_name":"Cointegration","level":2,"score":0.5852000117301941},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5194000005722046},{"id":"https://openalex.org/C89715816","wikidata":"https://www.wikidata.org/wiki/Q7915763","display_name":"Variance decomposition of forecast errors","level":2,"score":0.5059000253677368},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C129824826","wikidata":"https://www.wikidata.org/wiki/Q2630107","display_name":"Granger causality","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C2779439359","wikidata":"https://www.wikidata.org/wiki/Q317088","display_name":"Commodity","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C2984556133","wikidata":"https://www.wikidata.org/wiki/Q297279","display_name":"Oil price","level":2,"score":0.4106999933719635},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.39010000228881836},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C2987168347","wikidata":"https://www.wikidata.org/wiki/Q22656","display_name":"Crude oil","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C200941418","wikidata":"https://www.wikidata.org/wiki/Q273508","display_name":"Inflation (cosmology)","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C134024062","wikidata":"https://www.wikidata.org/wiki/Q1401229","display_name":"Error correction model","level":3,"score":0.3513999879360199},{"id":"https://openalex.org/C3019931467","wikidata":"https://www.wikidata.org/wiki/Q4917","display_name":"Us dollar","level":3,"score":0.3416999876499176},{"id":"https://openalex.org/C2778908629","wikidata":"https://www.wikidata.org/wiki/Q4391528","display_name":"Effective exchange rate","level":3,"score":0.3327000141143799},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.2962999939918518},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.29440000653266907},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C204208750","wikidata":"https://www.wikidata.org/wiki/Q1519415","display_name":"Short run","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C70784835","wikidata":"https://www.wikidata.org/wiki/Q1049071","display_name":"West Texas Intermediate","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C68593654","wikidata":"https://www.wikidata.org/wiki/Q1270246","display_name":"Brent Crude","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.30598/barekengvol20iss4pp3241-3258","doi":"https://doi.org/10.30598/barekengvol20iss4pp3241-3258","is_oa":true,"landing_page_url":"https://doi.org/10.30598/barekengvol20iss4pp3241-3258","pdf_url":"https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/22268/13152","source":{"id":"https://openalex.org/S4210230638","display_name":"BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN","issn_l":"1978-7227","issn":["1978-7227","2615-3017"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BAREKENG: Jurnal Ilmu Matematika dan Terapan","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:21ffe0e9261d4f01aa925cdabb00bba4","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/21ffe0e9261d4f01aa925cdabb00bba4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Barekeng, Vol 20, Iss 4 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.30598/barekengvol20iss4pp3241-3258","doi":"https://doi.org/10.30598/barekengvol20iss4pp3241-3258","is_oa":true,"landing_page_url":"https://doi.org/10.30598/barekengvol20iss4pp3241-3258","pdf_url":"https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/22268/13152","source":{"id":"https://openalex.org/S4210230638","display_name":"BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN","issn_l":"1978-7227","issn":["1978-7227","2615-3017"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BAREKENG: Jurnal Ilmu Matematika dan Terapan","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320325143","display_name":"Universitas Airlangga","ror":"https://ror.org/04ctejd88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204248317.pdf","grobid_xml":"https://content.openalex.org/works/W7204248317.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W1966910378","https://openalex.org/W2019459021","https://openalex.org/W2069216164","https://openalex.org/W2153799213","https://openalex.org/W2170611972","https://openalex.org/W2608132654","https://openalex.org/W2790084516","https://openalex.org/W2809134636","https://openalex.org/W3107332974","https://openalex.org/W3125749663","https://openalex.org/W3147294950","https://openalex.org/W4292890447","https://openalex.org/W4297445415","https://openalex.org/W4311051930","https://openalex.org/W4392854643","https://openalex.org/W4401191277","https://openalex.org/W4401349157","https://openalex.org/W4402416423"],"related_works":["https://openalex.org/W3110270317","https://openalex.org/W2169915864","https://openalex.org/W7113555913","https://openalex.org/W4394968714","https://openalex.org/W2366724951","https://openalex.org/W7023801242","https://openalex.org/W7204921188","https://openalex.org/W7103994816","https://openalex.org/W3082716186","https://openalex.org/W2568555403"],"abstract_inverted_index":{"The":[0,111,130,150,181,206,225,246],"mining":[1,48],"sector":[2],"plays":[3],"a":[4],"strategic":[5],"role":[6],"in":[7,46,163,289],"the":[8,29,78,95,120,197,202,214,222,250,255,269],"global":[9,22,89],"economy,":[10],"especially":[11],"during":[12,71],"periods":[13],"of":[14,236,273],"high":[15],"economic":[16,296],"uncertainty.":[17],"Between":[18],"2020":[19,107],"and":[20,37,54,84,87,99,132,144,155,166,188,239,262,276,279,287],"2024,":[21],"markets":[23],"experienced":[24],"severe":[25],"volatility":[26],"due":[27],"to":[28,108,244],"COVID-19":[30],"pandemic,":[31],"geopolitical":[32],"tensions,":[33],"energy":[34],"price":[35,44,69,194,292],"shocks,":[36],"monetary":[38],"policy":[39],"tightening.":[40],"These":[41,266],"conditions":[42],"intensified":[43],"fluctuations":[45,162],"major":[47],"commodities":[49],"such":[50],"as":[51],"nickel,":[52,82,164],"gold,":[53,83,165],"tin.":[55],"However,":[56],"limited":[57],"empirical":[58],"research":[59],"has":[60,201],"compared":[61],"different":[62],"multivariate":[63,127],"modeling":[64],"approaches":[65],"for":[66,283],"analyzing":[67],"commodity":[68,291],"dynamics":[70],"this":[72],"volatile":[73,295],"period.":[74],"This":[75],"study":[76],"examines":[77,123],"dynamic":[79],"relationships":[80,125],"between":[81],"tin":[85,167],"prices":[86,168,190],"key":[88],"factors,":[90],"namely":[91],"crude":[92,186],"oil":[93,187],"prices,":[94,101],"USD":[96,198],"exchange":[97,199],"rate,":[98],"silver":[100,189],"using":[102],"monthly":[103],"data":[104],"from":[105],"January":[106],"December":[109],"2024.":[110],"VAR":[112,223,256,275],"model":[113,122,216,227,252],"captures":[114],"temporal":[115],"interdependencies":[116],"among":[117,126],"variables,":[118],"while":[119,196],"MRR":[121,182,215,226,251,277],"simultaneous":[124],"response":[128],"variables.":[129],"stationarity":[131],"cointegration":[133],"tests":[134],"show":[135],"that":[136,161,185,213,249],"all":[137],"variables":[138],"become":[139],"stationary":[140],"after":[141],"first":[142],"differencing":[143],"exhibit":[145],"no":[146],"long-term":[147],"equilibrium":[148],"relationship.":[149],"Impulse":[151],"Response":[152],"Function":[153],"(IRF)":[154],"Variance":[156],"Decomposition":[157],"(VDC)":[158],"analyses":[159],"reveal":[160],"are":[169],"primarily":[170],"driven":[171],"by":[172],"their":[173],"own":[174],"past":[175],"values,":[176],"with":[177,241],"minor":[178],"cross-commodity":[179],"effects.":[180],"results":[183,247],"indicate":[184],"significantly":[191],"influence":[192],"metal":[193],"variations,":[195],"rate":[200],"strongest":[203],"overall":[204],"effect.":[205],"comparison":[207],"across":[208],"three":[209],"evaluation":[210],"metrics":[211],"shows":[212],"provides":[217],"better":[218],"predictive":[219,264],"performance":[220],"than":[221,231],"model.":[224],"yields":[228],"higher":[229,263],"R\u00b2":[230,235],"VAR,":[232],"which":[233],"records":[234],"0.339,":[237],"0.584,":[238],"0.529,":[240],"MAPE":[242],"up":[243],"22.42%.":[245],"demonstrate":[248],"consistently":[253],"outperforms":[254],"model,":[257],"providing":[258],"stronger":[259],"explanatory":[260],"power":[261],"accuracy.":[265],"findings":[267],"highlight":[268],"added":[270],"methodological":[271],"value":[272],"comparing":[274],"models":[278],"offer":[280],"practical":[281],"insights":[282],"investors,":[284],"industry":[285],"stakeholders,":[286],"policymakers":[288],"managing":[290],"risk":[293],"under":[294],"conditions.":[297]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-27T00:00:00"}