Analytical Finite Sample Econometrics: From A. L. Nagar to Now View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-12-03

AUTHORS

Yong Bao, Aman Ullah

ABSTRACT

Professor A.L. Nagar was a world-renowned econometrician and an international authority on finite sample econometrics with many path-breaking papers on the statistical properties of econometric estimators and test statistics. His contributions to applied econometrics have been also widely recognized. Nagar’s 1959 Econometrica paper on the so-called k-class estimators, together with a later one in 1962 on the double-k-class estimators, provided a very general framework of bias and mean squared error approximations for a large class of estimators and had motivated researchers to study a wide variety of issues such as many and weak instruments for many decades to follow. This paper reviews Nagar’s seminal contributions to analytical finite sample econometrics by providing historical backgrounds, discussing extensions and generalization of Nagar’s approach, and suggesting future directions of this literature. More... »

PAGES

17-37

References to SciGraph publications

  • 2020-11-18. On the Exact Statistical Distribution of Econometric Estimators and Test Statistics in ADVANCES IN STATISTICS - THEORY AND APPLICATIONS
  • 2019-10-20. Simplified Matrix Methods for Multivariate Edgeworth Expansions in JOURNAL OF QUANTITATIVE ECONOMICS
  • 2021-11-09. Moments of a Wishart Matrix in JOURNAL OF QUANTITATIVE ECONOMICS
  • 1992. The Bootstrap and Edgeworth Expansion in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s40953-021-00261-z

    DOI

    http://dx.doi.org/10.1007/s40953-021-00261-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1143607310


    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/2008/14", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Economics", 
            "type": "DefinedTerm"
          }, 
          {
            "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"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Department of Economics, Purdue University, 47907, West Lafayette, IN, USA", 
              "id": "http://www.grid.ac/institutes/grid.169077.e", 
              "name": [
                "Department of Economics, Purdue University, 47907, West Lafayette, IN, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Bao", 
            "givenName": "Yong", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Department of Economics, University of California, 92521, Riverside, CA, USA", 
              "id": "http://www.grid.ac/institutes/grid.266097.c", 
              "name": [
                "Department of Economics, University of California, 92521, Riverside, CA, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ullah", 
            "givenName": "Aman", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s40953-019-00184-w", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1121954035", 
              "https://doi.org/10.1007/s40953-019-00184-w"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-62900-7_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1136818787", 
              "https://doi.org/10.1007/978-3-030-62900-7_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4612-4384-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045715699", 
              "https://doi.org/10.1007/978-1-4612-4384-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s40953-021-00267-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1142496383", 
              "https://doi.org/10.1007/s40953-021-00267-7"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2021-12-03", 
        "datePublishedReg": "2021-12-03", 
        "description": "Professor A.L. Nagar was a world-renowned econometrician and an international authority on finite sample econometrics with many path-breaking papers on the statistical properties of econometric estimators and test statistics. His contributions to applied econometrics have been also widely recognized. Nagar\u2019s 1959 Econometrica paper on the so-called k-class estimators, together with a later one in 1962 on the double-k-class estimators, provided a very general framework of bias and mean squared error approximations for a large class of estimators and had motivated researchers to study a wide variety of issues such as many and weak instruments for many decades to follow. This paper reviews Nagar\u2019s seminal contributions to analytical finite sample econometrics by providing historical backgrounds, discussing extensions and generalization of Nagar\u2019s approach, and suggesting future directions of this literature.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s40953-021-00261-z", 
        "inLanguage": "en", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1027984", 
            "issn": [
              "0971-1554", 
              "2364-1045"
            ], 
            "name": "Journal of Quantitative Economics", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "Suppl 1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "19"
          }
        ], 
        "keywords": [
          "k-class estimators", 
          "double k-class estimators", 
          "path-breaking paper", 
          "statistical properties", 
          "weak instruments", 
          "large class", 
          "error approximation", 
          "test statistic", 
          "estimator", 
          "general framework", 
          "econometric estimators", 
          "econometrics", 
          "seminal contributions", 
          "Econometrica paper", 
          "approximation", 
          "generalization", 
          "econometricians", 
          "statistics", 
          "class", 
          "approach", 
          "extension", 
          "wide variety", 
          "properties", 
          "framework", 
          "direction", 
          "contribution", 
          "bias", 
          "variety", 
          "literature", 
          "instrument", 
          "researchers", 
          "background", 
          "historical background", 
          "international authorities", 
          "issues", 
          "future directions", 
          "decades", 
          "authorities", 
          "Nagar", 
          "paper"
        ], 
        "name": "Analytical Finite Sample Econometrics: From A. L. Nagar to Now", 
        "pagination": "17-37", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1143607310"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s40953-021-00261-z"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s40953-021-00261-z", 
          "https://app.dimensions.ai/details/publication/pub.1143607310"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-05-10T10:31", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_882.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s40953-021-00261-z"
      }
    ]
     

    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/s40953-021-00261-z'

    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/s40953-021-00261-z'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40953-021-00261-z'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40953-021-00261-z'


     

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

    122 TRIPLES      22 PREDICATES      69 URIs      57 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s40953-021-00261-z schema:about anzsrc-for:14
    2 anzsrc-for:1403
    3 schema:author Na1b9a1cbf950454793f6b7d3e4861bc3
    4 schema:citation sg:pub.10.1007/978-1-4612-4384-7
    5 sg:pub.10.1007/978-3-030-62900-7_6
    6 sg:pub.10.1007/s40953-019-00184-w
    7 sg:pub.10.1007/s40953-021-00267-7
    8 schema:datePublished 2021-12-03
    9 schema:datePublishedReg 2021-12-03
    10 schema:description Professor A.L. Nagar was a world-renowned econometrician and an international authority on finite sample econometrics with many path-breaking papers on the statistical properties of econometric estimators and test statistics. His contributions to applied econometrics have been also widely recognized. Nagar’s 1959 Econometrica paper on the so-called k-class estimators, together with a later one in 1962 on the double-k-class estimators, provided a very general framework of bias and mean squared error approximations for a large class of estimators and had motivated researchers to study a wide variety of issues such as many and weak instruments for many decades to follow. This paper reviews Nagar’s seminal contributions to analytical finite sample econometrics by providing historical backgrounds, discussing extensions and generalization of Nagar’s approach, and suggesting future directions of this literature.
    11 schema:genre article
    12 schema:inLanguage en
    13 schema:isAccessibleForFree false
    14 schema:isPartOf N8b7c2a6b05b544fdb6120e3181318a4b
    15 Nb25ea36273fe453fb20c16c85cd59d0d
    16 sg:journal.1027984
    17 schema:keywords Econometrica paper
    18 Nagar
    19 approach
    20 approximation
    21 authorities
    22 background
    23 bias
    24 class
    25 contribution
    26 decades
    27 direction
    28 double k-class estimators
    29 econometric estimators
    30 econometricians
    31 econometrics
    32 error approximation
    33 estimator
    34 extension
    35 framework
    36 future directions
    37 general framework
    38 generalization
    39 historical background
    40 instrument
    41 international authorities
    42 issues
    43 k-class estimators
    44 large class
    45 literature
    46 paper
    47 path-breaking paper
    48 properties
    49 researchers
    50 seminal contributions
    51 statistical properties
    52 statistics
    53 test statistic
    54 variety
    55 weak instruments
    56 wide variety
    57 schema:name Analytical Finite Sample Econometrics: From A. L. Nagar to Now
    58 schema:pagination 17-37
    59 schema:productId Nae583fcca11142429ac0fc46dbb5ed45
    60 Ne6f0834f3c294c57bf3c2278ef90689f
    61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1143607310
    62 https://doi.org/10.1007/s40953-021-00261-z
    63 schema:sdDatePublished 2022-05-10T10:31
    64 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    65 schema:sdPublisher N49b3f9fcefbb4a4f864458fc7aef9b4f
    66 schema:url https://doi.org/10.1007/s40953-021-00261-z
    67 sgo:license sg:explorer/license/
    68 sgo:sdDataset articles
    69 rdf:type schema:ScholarlyArticle
    70 N00a3df0f6a3442b697d5535ae4c1ceb4 rdf:first Na2a239a7faf34d6199edf12588c20c2d
    71 rdf:rest rdf:nil
    72 N09fe1e8a08f349d5a891045672d1d9d9 schema:affiliation grid-institutes:grid.169077.e
    73 schema:familyName Bao
    74 schema:givenName Yong
    75 rdf:type schema:Person
    76 N49b3f9fcefbb4a4f864458fc7aef9b4f schema:name Springer Nature - SN SciGraph project
    77 rdf:type schema:Organization
    78 N8b7c2a6b05b544fdb6120e3181318a4b schema:volumeNumber 19
    79 rdf:type schema:PublicationVolume
    80 Na1b9a1cbf950454793f6b7d3e4861bc3 rdf:first N09fe1e8a08f349d5a891045672d1d9d9
    81 rdf:rest N00a3df0f6a3442b697d5535ae4c1ceb4
    82 Na2a239a7faf34d6199edf12588c20c2d schema:affiliation grid-institutes:grid.266097.c
    83 schema:familyName Ullah
    84 schema:givenName Aman
    85 rdf:type schema:Person
    86 Nae583fcca11142429ac0fc46dbb5ed45 schema:name doi
    87 schema:value 10.1007/s40953-021-00261-z
    88 rdf:type schema:PropertyValue
    89 Nb25ea36273fe453fb20c16c85cd59d0d schema:issueNumber Suppl 1
    90 rdf:type schema:PublicationIssue
    91 Ne6f0834f3c294c57bf3c2278ef90689f schema:name dimensions_id
    92 schema:value pub.1143607310
    93 rdf:type schema:PropertyValue
    94 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
    95 schema:name Economics
    96 rdf:type schema:DefinedTerm
    97 anzsrc-for:1403 schema:inDefinedTermSet anzsrc-for:
    98 schema:name Econometrics
    99 rdf:type schema:DefinedTerm
    100 sg:journal.1027984 schema:issn 0971-1554
    101 2364-1045
    102 schema:name Journal of Quantitative Economics
    103 schema:publisher Springer Nature
    104 rdf:type schema:Periodical
    105 sg:pub.10.1007/978-1-4612-4384-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045715699
    106 https://doi.org/10.1007/978-1-4612-4384-7
    107 rdf:type schema:CreativeWork
    108 sg:pub.10.1007/978-3-030-62900-7_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1136818787
    109 https://doi.org/10.1007/978-3-030-62900-7_6
    110 rdf:type schema:CreativeWork
    111 sg:pub.10.1007/s40953-019-00184-w schema:sameAs https://app.dimensions.ai/details/publication/pub.1121954035
    112 https://doi.org/10.1007/s40953-019-00184-w
    113 rdf:type schema:CreativeWork
    114 sg:pub.10.1007/s40953-021-00267-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1142496383
    115 https://doi.org/10.1007/s40953-021-00267-7
    116 rdf:type schema:CreativeWork
    117 grid-institutes:grid.169077.e schema:alternateName Department of Economics, Purdue University, 47907, West Lafayette, IN, USA
    118 schema:name Department of Economics, Purdue University, 47907, West Lafayette, IN, USA
    119 rdf:type schema:Organization
    120 grid-institutes:grid.266097.c schema:alternateName Department of Economics, University of California, 92521, Riverside, CA, USA
    121 schema:name Department of Economics, University of California, 92521, Riverside, CA, USA
    122 rdf:type schema:Organization
     




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


    ...