{"id":"https://openalex.org/W2187980047","doi":null,"title":"Multi-Step Forecast Model Selection","display_name":"Multi-Step Forecast Model Selection","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W2187980047","mag":"2187980047"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.233.3959","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.233.3959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ssc.wisc.edu/%7Ebhansen/papers/hstep.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5024558520","display_name":"Bruce E. Hansen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bruce E. Hansen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5024558520"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1405,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.86580638,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9731000065803528,"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"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9731000065803528,"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"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.02329999953508377,"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/T13141","display_name":"Statistical Methods and Applications","score":0.0005000000237487257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/multi-step-forecasting","display_name":"multi-step forecasting","score":0.9110000133514404},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.8840000033378601},{"id":"https://openalex.org/keywords/forecast-combination","display_name":"forecast combination","score":0.8130000233650208},{"id":"https://openalex.org/keywords/mean-squared-forecast-error","display_name":"mean squared forecast error","score":0.9520000219345093},{"id":"https://openalex.org/keywords/information-criteria","display_name":"information criteria","score":0.7900000214576721},{"id":"https://openalex.org/keywords/leave-one-out-cross-validation","display_name":"leave-one-out cross-validation","score":0.8429999947547913},{"id":"https://openalex.org/keywords/long-run-variance","display_name":"long-run variance","score":0.7250000238418579},{"id":"https://openalex.org/keywords/overparameterization","display_name":"overparameterization","score":0.7400000095367432},{"id":"https://openalex.org/keywords/forecast-horizon","display_name":"forecast horizon","score":0.6439999938011169}],"concepts":[{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.8292222023010254},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7211635112762451},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6166049242019653},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.6009702682495117},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.542327880859375},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.530741274356842},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5229314565658569},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5173508524894714},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5033149123191833},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.48233649134635925},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.46087533235549927},{"id":"https://openalex.org/C103545067","wikidata":"https://www.wikidata.org/wiki/Q796265","display_name":"Best linear unbiased prediction","level":3,"score":0.45387569069862366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.16921502351760864},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","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}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.233.3959","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.233.3959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ssc.wisc.edu/%7Ebhansen/papers/hstep.pdf","raw_type":"text"},{"id":"mag:2187980047","doi":null,"is_oa":false,"landing_page_url":"http://economics.sas.upenn.edu/system/files/event_papers/Econometrics03282011.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W43353312","https://openalex.org/W1503022246","https://openalex.org/W1603903339","https://openalex.org/W1979519992","https://openalex.org/W1981251392","https://openalex.org/W1984340473","https://openalex.org/W1986528915","https://openalex.org/W1989780425","https://openalex.org/W1993387039","https://openalex.org/W1994672023","https://openalex.org/W2001746770","https://openalex.org/W2026590277","https://openalex.org/W2028147019","https://openalex.org/W2038074608","https://openalex.org/W2041902797","https://openalex.org/W2048831473","https://openalex.org/W2050297026","https://openalex.org/W2052025831","https://openalex.org/W2055301467","https://openalex.org/W2056481711","https://openalex.org/W2058815839","https://openalex.org/W2061317273","https://openalex.org/W2062772460","https://openalex.org/W2070681743","https://openalex.org/W2082080397","https://openalex.org/W2084194399","https://openalex.org/W2093503007","https://openalex.org/W2094597214","https://openalex.org/W2100000728","https://openalex.org/W2103358330","https://openalex.org/W2108731026","https://openalex.org/W2112081648","https://openalex.org/W2112341373","https://openalex.org/W2121286295","https://openalex.org/W2122196572","https://openalex.org/W2146855196","https://openalex.org/W2164493700","https://openalex.org/W2171791029","https://openalex.org/W2177791359","https://openalex.org/W2327938815","https://openalex.org/W2914369697","https://openalex.org/W3026721701","https://openalex.org/W3099674110","https://openalex.org/W3121743397","https://openalex.org/W3147070131"],"related_works":["https://openalex.org/W2142566359","https://openalex.org/W1984717679","https://openalex.org/W2677629642","https://openalex.org/W2114449311","https://openalex.org/W2557306731","https://openalex.org/W2484345041","https://openalex.org/W3122242562","https://openalex.org/W1997357221","https://openalex.org/W2171791029","https://openalex.org/W2022544187"],"abstract_inverted_index":{"This":[0],"paper":[1],"examines":[2],"model":[3,74,154,163],"selection":[4,161],"and":[5,89,100,120,147],"combination":[6],"in":[7,56,75,109,125],"the":[8,26,29,39,44,52,59,64,68,72,78,107,126,134,145],"context":[9],"of":[10,28,32,38,51,54,71,81,106,144,162],"multi-step":[11,18,82,110],"linear":[12],"forecasting.":[13],"We":[14,24,41],"start":[15],"by":[16,87],"investigating":[17],"mean":[19],"squared":[20,33],"forecast":[21,73,83,111,127,166],"error":[22],"(MSFE).":[23],"derive":[25],"bias":[27,45,65],"in-sample":[30],"sum":[31],"residuals":[34],"as":[35,85],"an":[36,140],"estimator":[37,143],"MSFE.":[40],"\u2026nd":[42],"that":[43,133],"is":[46,123,139,148,157],"not":[47],"generically":[48],"a":[49,150],"scale":[50],"number":[53],"parameters,":[55],"contrast":[57],"to":[58,77],"one-step-ahead":[60],"forecasting":[61],"case.":[62],"Instead,":[63],"depends":[66],"on":[67],"long-run":[69],"variance":[70],"analogy":[76],"covariance":[79],"matrix":[80],"regressions,":[84],"found":[86],"Hansen":[88],"Hodrick":[90],"(1980).":[91],"In":[92,129],"consequence,":[93],"standard":[94],"information":[95],"criterion":[96,138,152],"(Akaike,":[97],"FPE,":[98],"Mallows":[99],"leave-one-out":[101],"cross-validation)":[102],"are":[103,115],"biased":[104],"estimators":[105],"MSFE":[108,146],"models.":[112],"These":[113],"criteria":[114],"generally":[116],"under-penalizing":[117],"for":[118,153,160,165],"over-parameterization":[119],"this":[121],"discrepancy":[122],"increasing":[124],"horizon.":[128],"contrast,":[130],"we":[131],"show":[132],"leave-h-out":[135],"cross":[136],"validation":[137],"approximately":[141],"unbiased":[142],"thus":[149],"suitable":[151,159],"selection.":[155],"Leave-h-out":[156],"also":[158],"weights":[164],"combination.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
