MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-09

AUTHORS

David A. Swanson, Jeff Tayman, T. M. Bryan

ABSTRACT

Accurately measuring a population and its attributes at past, present, and future points in time has been of great interest to demographers. Within discussions of forecast accuracy, demographers have often been criticized for their inaccurate prognostications of the future. Discussions of methods and data are usually at the centre of these criticisms, along with suggestions for providing an idea of forecast uncertainty. The measures used to evaluate the accuracy of forecasts also have received attention and while accuracy is not the only criterion advocated for evaluating demographic forecasts, it is generally acknowledged to be the most important. In this paper, we continue the discussion of measures of forecast accuracy by concentrating on a rescaled version of a measure that is arguably the one used most often in evaluating cross-sectional, subnational forecasts, Mean Absolute Percent Error (MAPE). The rescaled version, MAPE-R, has not had the benefit of a major empirical test, which is the central focus of this paper. We do this by comparing 10-year population forecasts for U.S. counties to 2000 census counts. We find that the MAPE-R offers a significantly more meaningful representation of average error than MAPE in the presence of substantial outlying errors, and we provide guidelines for its implementation. More... »

PAGES

225-243

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12546-011-9054-5

DOI

http://dx.doi.org/10.1007/s12546-011-9054-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029770019


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1403", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Econometrics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of California, Riverside", 
          "id": "https://www.grid.ac/institutes/grid.266097.c", 
          "name": [
            "Department of Sociology, University of California Riverside, 92521, Riverside, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Swanson", 
        "givenName": "David A.", 
        "id": "sg:person.010015377733.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010015377733.69"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California, San Diego", 
          "id": "https://www.grid.ac/institutes/grid.266100.3", 
          "name": [
            "Department of Economics, University of California San Diego, 92093, La Jolla, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tayman", 
        "givenName": "Jeff", 
        "id": "sg:person.012330665434.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012330665434.70"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "McKibben Demographic Research, PO Box 2921, 29732, Rock Hill, SC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bryan", 
        "givenName": "T. M.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/0169-2070(93)90079-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001296929"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2307/2061167", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002080192", 
          "https://doi.org/10.2307/2061167"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4020-8329-7_10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007617125", 
          "https://doi.org/10.1007/978-1-4020-8329-7_10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4020-8329-7_10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007617125", 
          "https://doi.org/10.1007/978-1-4020-8329-7_10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1006166418051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009058061", 
          "https://doi.org/10.1023/a:1006166418051"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/08898489509525406", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010609532"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01074460", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015181035", 
          "https://doi.org/10.1007/bf01074460"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01074460", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015181035", 
          "https://doi.org/10.1007/bf01074460"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01074460", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015181035", 
          "https://doi.org/10.1007/bf01074460"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/0-387-28392-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015199783", 
          "https://doi.org/10.1007/0-387-28392-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1015199783", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0169-2070(95)00615-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017333581"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11113-007-9030-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017394720", 
          "https://doi.org/10.1007/s11113-007-9030-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1005766424443", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017918637", 
          "https://doi.org/10.1023/a:1005766424443"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2307/2648121", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018468093", 
          "https://doi.org/10.2307/2648121"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0169-2070(88)90015-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020680492"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1016537822148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020758577", 
          "https://doi.org/10.1023/a:1016537822148"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/08898489509525402", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022082197"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0169-2070(92)90008-w", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028959465"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2307/2061544", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036273387", 
          "https://doi.org/10.2307/2061544"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03031880", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039557472", 
          "https://doi.org/10.1007/bf03031880"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0169-2070(92)90060-m", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039665812"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1006317430570", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042365450", 
          "https://doi.org/10.1023/a:1006317430570"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0169-2070(92)90010-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047574033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijforecast.2006.03.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052401159"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01944367708977786", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053030078"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0739456x07313925", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053940421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/0739456x07313925", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053940421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1987.10478528", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058303505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1990.10476209", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058303988"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aoms/1177703732", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064400228"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1403192", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069473555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2684359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070057745"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011-09", 
    "datePublishedReg": "2011-09-01", 
    "description": "Accurately measuring a population and its attributes at past, present, and future points in time has been of great interest to demographers. Within discussions of forecast accuracy, demographers have often been criticized for their inaccurate prognostications of the future. Discussions of methods and data are usually at the centre of these criticisms, along with suggestions for providing an idea of forecast uncertainty. The measures used to evaluate the accuracy of forecasts also have received attention and while accuracy is not the only criterion advocated for evaluating demographic forecasts, it is generally acknowledged to be the most important. In this paper, we continue the discussion of measures of forecast accuracy by concentrating on a rescaled version of a measure that is arguably the one used most often in evaluating cross-sectional, subnational forecasts, Mean Absolute Percent Error (MAPE). The rescaled version, MAPE-R, has not had the benefit of a major empirical test, which is the central focus of this paper. We do this by comparing 10-year population forecasts for U.S. counties to 2000 census counts. We find that the MAPE-R offers a significantly more meaningful representation of average error than MAPE in the presence of substantial outlying errors, and we provide guidelines for its implementation.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12546-011-9054-5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1027277", 
        "issn": [
          "1443-2447", 
          "1835-9469"
        ], 
        "name": "Journal of Population Research", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2-3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "28"
      }
    ], 
    "name": "MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts", 
    "pagination": "225-243", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "8dd1ad26c51cfa28f5a6a882a14e4fe8c51b252371abaaa648f69ad95c62237e"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12546-011-9054-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1029770019"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12546-011-9054-5", 
      "https://app.dimensions.ai/details/publication/pub.1029770019"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T17:34", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000522.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs12546-011-9054-5"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12546-011-9054-5'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12546-011-9054-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12546-011-9054-5'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12546-011-9054-5'


 

This table displays all metadata directly associated to this object as RDF triples.

177 TRIPLES      21 PREDICATES      56 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12546-011-9054-5 schema:about anzsrc-for:14
2 anzsrc-for:1403
3 schema:author N97247acfa7bd44678049578f4f55de6e
4 schema:citation sg:pub.10.1007/0-387-28392-7
5 sg:pub.10.1007/978-1-4020-8329-7_10
6 sg:pub.10.1007/bf01074460
7 sg:pub.10.1007/bf03031880
8 sg:pub.10.1007/s11113-007-9030-0
9 sg:pub.10.1023/a:1005766424443
10 sg:pub.10.1023/a:1006166418051
11 sg:pub.10.1023/a:1006317430570
12 sg:pub.10.1023/a:1016537822148
13 sg:pub.10.2307/2061167
14 sg:pub.10.2307/2061544
15 sg:pub.10.2307/2648121
16 https://app.dimensions.ai/details/publication/pub.1015199783
17 https://doi.org/10.1016/0169-2070(88)90015-5
18 https://doi.org/10.1016/0169-2070(92)90008-w
19 https://doi.org/10.1016/0169-2070(92)90010-7
20 https://doi.org/10.1016/0169-2070(92)90060-m
21 https://doi.org/10.1016/0169-2070(93)90079-3
22 https://doi.org/10.1016/0169-2070(95)00615-x
23 https://doi.org/10.1016/j.ijforecast.2006.03.001
24 https://doi.org/10.1080/01621459.1987.10478528
25 https://doi.org/10.1080/01621459.1990.10476209
26 https://doi.org/10.1080/01944367708977786
27 https://doi.org/10.1080/08898489509525402
28 https://doi.org/10.1080/08898489509525406
29 https://doi.org/10.1177/0739456x07313925
30 https://doi.org/10.1214/aoms/1177703732
31 https://doi.org/10.2307/1403192
32 https://doi.org/10.2307/2684359
33 schema:datePublished 2011-09
34 schema:datePublishedReg 2011-09-01
35 schema:description Accurately measuring a population and its attributes at past, present, and future points in time has been of great interest to demographers. Within discussions of forecast accuracy, demographers have often been criticized for their inaccurate prognostications of the future. Discussions of methods and data are usually at the centre of these criticisms, along with suggestions for providing an idea of forecast uncertainty. The measures used to evaluate the accuracy of forecasts also have received attention and while accuracy is not the only criterion advocated for evaluating demographic forecasts, it is generally acknowledged to be the most important. In this paper, we continue the discussion of measures of forecast accuracy by concentrating on a rescaled version of a measure that is arguably the one used most often in evaluating cross-sectional, subnational forecasts, Mean Absolute Percent Error (MAPE). The rescaled version, MAPE-R, has not had the benefit of a major empirical test, which is the central focus of this paper. We do this by comparing 10-year population forecasts for U.S. counties to 2000 census counts. We find that the MAPE-R offers a significantly more meaningful representation of average error than MAPE in the presence of substantial outlying errors, and we provide guidelines for its implementation.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree true
39 schema:isPartOf Nb4f80afd22204731889b67d032301067
40 Ndde1456ec779407492547cbd083af300
41 sg:journal.1027277
42 schema:name MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts
43 schema:pagination 225-243
44 schema:productId N11c941d6e7264833ab5eac83fb482beb
45 N454a885035974105a20ec9ca04c936ca
46 Neae17a72520a4b17bbf14d04491f950e
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029770019
48 https://doi.org/10.1007/s12546-011-9054-5
49 schema:sdDatePublished 2019-04-10T17:34
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher N9f439cc1d64d447bbd6799f41ac3bb56
52 schema:url http://link.springer.com/10.1007%2Fs12546-011-9054-5
53 sgo:license sg:explorer/license/
54 sgo:sdDataset articles
55 rdf:type schema:ScholarlyArticle
56 N11c941d6e7264833ab5eac83fb482beb schema:name dimensions_id
57 schema:value pub.1029770019
58 rdf:type schema:PropertyValue
59 N13c9ef2c398e4ba7bd2432bf52e15788 rdf:first Ncdf68df4a3d942ecafca84848b0866b0
60 rdf:rest rdf:nil
61 N454a885035974105a20ec9ca04c936ca schema:name readcube_id
62 schema:value 8dd1ad26c51cfa28f5a6a882a14e4fe8c51b252371abaaa648f69ad95c62237e
63 rdf:type schema:PropertyValue
64 N6cce0d5e97c84d0ab52d67d841ad5f73 rdf:first sg:person.012330665434.70
65 rdf:rest N13c9ef2c398e4ba7bd2432bf52e15788
66 N97247acfa7bd44678049578f4f55de6e rdf:first sg:person.010015377733.69
67 rdf:rest N6cce0d5e97c84d0ab52d67d841ad5f73
68 N9f439cc1d64d447bbd6799f41ac3bb56 schema:name Springer Nature - SN SciGraph project
69 rdf:type schema:Organization
70 Nb4f80afd22204731889b67d032301067 schema:volumeNumber 28
71 rdf:type schema:PublicationVolume
72 Nc8d47128fb4b4babb7b6925847eaeaf1 schema:name McKibben Demographic Research, PO Box 2921, 29732, Rock Hill, SC, USA
73 rdf:type schema:Organization
74 Ncdf68df4a3d942ecafca84848b0866b0 schema:affiliation Nc8d47128fb4b4babb7b6925847eaeaf1
75 schema:familyName Bryan
76 schema:givenName T. M.
77 rdf:type schema:Person
78 Ndde1456ec779407492547cbd083af300 schema:issueNumber 2-3
79 rdf:type schema:PublicationIssue
80 Neae17a72520a4b17bbf14d04491f950e schema:name doi
81 schema:value 10.1007/s12546-011-9054-5
82 rdf:type schema:PropertyValue
83 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
84 schema:name Economics
85 rdf:type schema:DefinedTerm
86 anzsrc-for:1403 schema:inDefinedTermSet anzsrc-for:
87 schema:name Econometrics
88 rdf:type schema:DefinedTerm
89 sg:journal.1027277 schema:issn 1443-2447
90 1835-9469
91 schema:name Journal of Population Research
92 rdf:type schema:Periodical
93 sg:person.010015377733.69 schema:affiliation https://www.grid.ac/institutes/grid.266097.c
94 schema:familyName Swanson
95 schema:givenName David A.
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010015377733.69
97 rdf:type schema:Person
98 sg:person.012330665434.70 schema:affiliation https://www.grid.ac/institutes/grid.266100.3
99 schema:familyName Tayman
100 schema:givenName Jeff
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012330665434.70
102 rdf:type schema:Person
103 sg:pub.10.1007/0-387-28392-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015199783
104 https://doi.org/10.1007/0-387-28392-7
105 rdf:type schema:CreativeWork
106 sg:pub.10.1007/978-1-4020-8329-7_10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007617125
107 https://doi.org/10.1007/978-1-4020-8329-7_10
108 rdf:type schema:CreativeWork
109 sg:pub.10.1007/bf01074460 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015181035
110 https://doi.org/10.1007/bf01074460
111 rdf:type schema:CreativeWork
112 sg:pub.10.1007/bf03031880 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039557472
113 https://doi.org/10.1007/bf03031880
114 rdf:type schema:CreativeWork
115 sg:pub.10.1007/s11113-007-9030-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017394720
116 https://doi.org/10.1007/s11113-007-9030-0
117 rdf:type schema:CreativeWork
118 sg:pub.10.1023/a:1005766424443 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017918637
119 https://doi.org/10.1023/a:1005766424443
120 rdf:type schema:CreativeWork
121 sg:pub.10.1023/a:1006166418051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009058061
122 https://doi.org/10.1023/a:1006166418051
123 rdf:type schema:CreativeWork
124 sg:pub.10.1023/a:1006317430570 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042365450
125 https://doi.org/10.1023/a:1006317430570
126 rdf:type schema:CreativeWork
127 sg:pub.10.1023/a:1016537822148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020758577
128 https://doi.org/10.1023/a:1016537822148
129 rdf:type schema:CreativeWork
130 sg:pub.10.2307/2061167 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002080192
131 https://doi.org/10.2307/2061167
132 rdf:type schema:CreativeWork
133 sg:pub.10.2307/2061544 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036273387
134 https://doi.org/10.2307/2061544
135 rdf:type schema:CreativeWork
136 sg:pub.10.2307/2648121 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018468093
137 https://doi.org/10.2307/2648121
138 rdf:type schema:CreativeWork
139 https://app.dimensions.ai/details/publication/pub.1015199783 schema:CreativeWork
140 https://doi.org/10.1016/0169-2070(88)90015-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020680492
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/0169-2070(92)90008-w schema:sameAs https://app.dimensions.ai/details/publication/pub.1028959465
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/0169-2070(92)90010-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047574033
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/0169-2070(92)90060-m schema:sameAs https://app.dimensions.ai/details/publication/pub.1039665812
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/0169-2070(93)90079-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001296929
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/0169-2070(95)00615-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1017333581
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/j.ijforecast.2006.03.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052401159
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1080/01621459.1987.10478528 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303505
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1080/01621459.1990.10476209 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303988
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1080/01944367708977786 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053030078
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1080/08898489509525402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022082197
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1080/08898489509525406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010609532
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1177/0739456x07313925 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053940421
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1214/aoms/1177703732 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064400228
167 rdf:type schema:CreativeWork
168 https://doi.org/10.2307/1403192 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069473555
169 rdf:type schema:CreativeWork
170 https://doi.org/10.2307/2684359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070057745
171 rdf:type schema:CreativeWork
172 https://www.grid.ac/institutes/grid.266097.c schema:alternateName University of California, Riverside
173 schema:name Department of Sociology, University of California Riverside, 92521, Riverside, CA, USA
174 rdf:type schema:Organization
175 https://www.grid.ac/institutes/grid.266100.3 schema:alternateName University of California, San Diego
176 schema:name Department of Economics, University of California San Diego, 92093, La Jolla, CA, USA
177 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...