Replication data for: How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It
King, Gary; Roberts, Margaret, 2014, "Replication data for: How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It", doi:10.7910/DVN/26935, Harvard Dataverse, 7.1

11 Files
Files found: 11
# id name / description filesystemname contentType filesize_bytes checksum restricted download created published
1 2491878 bootstrapIM.poisson.R 224179 text/plain; charset=US-ASCII 2,712 3e82413d6660f1173f070b4ebe4ec1fc (MD5) False download 2014-08-04 13:11:19 2014-08-03 20:00:00
2 2491880 bootstrapIM.nb.R 224177 text/plain; charset=US-ASCII 3,851 8d5de10d96ffdadafaa39ad00909457e (MD5) False download 2014-08-04 13:11:19 2014-08-03 20:00:00
3 2491881 Neumayer_ISQ_2003.R
replicates the Neumayer (2003) example from the paper
224182 text/plain; charset=US-ASCII 8,330 46a79d146977c56da89ad94cbdd4ecd0 (MD5) False download 2014-08-04 13:11:19 2014-08-03 20:00:00
4 2491882 ReadMe.txt
ReadMe file related to this data
224188 text/plain; charset=UTF-8 632 8446cf916bedcdd873a76b62bcf05b13 (MD5) False download 2014-08-04 13:11:19 2014-08-03 20:00:00
5 2491883 DreherJensen.R
replicates the Dreher and Jensen (2007) example from the paper
224181 text/plain; charset=US-ASCII 7,394 66c2165ca1a4e639de816f5c0842afe9 (MD5) False download 2014-08-04 13:11:19 2014-08-03 20:00:00
6 2491884 bootstrapIM.normal.R 224178 text/plain; charset=US-ASCII 4,205 e02facd1a3ac4c3237b8fbca346a1962 (MD5) False download 2014-08-04 13:11:19 2014-08-03 20:00:00
7 2491885 ButheMilner.R
replicates the Buthe and Milner (2008) example from the paper.
224180 text/plain; charset=US-ASCII 10,841 dcee264071860da438475f5742c2df2a (MD5) False download 2014-08-04 13:11:19 2014-08-03 20:00:00
8 2491887 Article for ISQ (aid).tab 224184 text/tab-separated-values 130,826 0f8534bdf7d31fdacc923e990a55c6b6 (MD5) False download 2014-08-04 13:11:24 2014-08-03 20:00:00
9 2491888 DreherandJensenJLEreplication.tab 224185 text/tab-separated-values 321,903 d03a6a8468e1e4a3c0cf3172f08dce12 (MD5) False download 2014-08-04 13:11:24 2014-08-03 20:00:00
10 2493899 MilnerButhe2.tab
Some of the data from the article we replicated by Tim Buthe and Hellen Millner data is proprietary; to obtain it, you must contact them directly.
226236 text/tab-separated-values 1,267,188 edb06411ed0d6242638163b503919843 (MD5) True download 2014-08-05 13:06:13 2014-08-04 20:00:00
11 2732877 vcovSACCchange.R 151642c1e50-fff554769afe type/x-r-syntax 6,035 224a626776b6bf2e955f7cb6ecbefb85 (MD5) False download 2015-12-02 14:29:56 2015-12-02 14:31:44
to do..
{
    "citation": {
        "title": "Replication data for: How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It", 
        "dateOfDeposit": "2014-08-04", 
        "subject": [
            "Social Sciences"
        ], 
        "distributionDate": "2014", 
        "author": [
            {
                "authorName": "King, Gary", 
                "authorAffiliation": "Harvard University"
            }, 
            {
                "authorName": "Roberts, Margaret"
            }
        ], 
        "publication": [
            {
                "publicationCitation": "Forthcoming, Political Analysis  <br /><br />  King, Gary, and Margaret Roberts. 2014. How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It. Political Analysis:  <a href=\"http://j.mp/InK5jU\" target=\"_blank\">Link to article</a>"
            }
        ], 
        "distributor": [
            {
                "distributorName": "Harvard Dataverse", 
                "distributorLogoURL": "https://dataverse.harvard.edu/resources/images/dataverseproject_logo.jpg"
            }
        ], 
        "dsDescription": [
            {
                "dsDescriptionValue": "\"Robust standard errors\" are used in a vast array of scholarship to correct standard errors for model misspecification. However, when misspecification is bad enough to make classical and robust standard errors diverge, assuming that it is nevertheless not so bad as to bias everything else requires considerable optimism. And even if the optimism is warranted, settling for a misspecified model, with or without robust standard errors, w\r\nill still bias estimators of all but a few quantities of interest. Even though this message is well known to methodologists, it has failed to reach most applied researchers. The resulting cavernous gap between theory and practice suggests that considerable gains in applied statistics may be possible. We seek to help applied researchers realize these gains via an alternative perspective that offers a productive way to use robust standard errors; a new general and easier-to-use \"generalized information matrix test\" statistic; and practical illustrations via simulations and real examples from published research. Instead of jettisoning this extremely popular tool, as some suggest, we show how robust and classical standard error differences can provide effective clues about model misspecification, likely biases, and a guide to more reliable inferences.\r\n<br /><br /> See also: <a href=\"http://gking.harvard.edu/category/research-interests/methods/unifying-statistical-analysis\" target=\"_blank\">Unifying Statistical Analysis</a>", 
                "dsDescriptionDate": "2014"
            }
        ], 
        "datasetContact": [
            {
                "datasetContactEmail": "king@harvard.edu"
            }
        ]
    }
}
.Terms..
.Versions..