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)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
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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

2014-08-04

7

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", https://doi.org/10.7910/DVN/26935, Harvard Dataverse, V7, UNF:5:Bc1yVsbYLpjnS0Bx6FDnNA== [fileUNF]

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

King, Gary (Harvard University)

Roberts, Margaret (Harvard University)

Harvard Dataverse

Harvard Dataverse

2014-08-04

2014

https://doi.org/10.7910/DVN/26935

Social Sciences

"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 ill 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. <br /><br /> See also: <a href="http://gking.harvard.edu/category/research-interests/methods/unifying-statistical-analysis" target="_blank">Unifying Statistical Analysis</a>

<b>One data file is impacted by a copyright license</b>: <br /><br /> MilnerButhe2.tab<br /><br /> 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.

<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>

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>

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>

• Number of cases: 800

• No. of variables per record: 27

• Type of File: text/tab-separated-values

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• Number of cases: 1276

• No. of variables per record: 62

• Type of File: text/tab-separated-values

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• Number of cases: 2673

• No. of variables per record: 67

• Type of File: text/tab-separated-values

UNF:5:/jhzaGz9a1paDAHaSZ4iLw==

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.

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• arms
• freedom
• graft
• lncolony
• lndist
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• icrgcorrupt
• intamsh
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• country
• year
• sum
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• gdp_r_wk
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• pc
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• t19974
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• t19991
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• t19993
• t19994
• t20001
• t20002
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• t20011
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• t20013
• t20014
• t20021
• t20022
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• t20031
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• gg_l
• mg_l
• def_gdp_l
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• sum_ii_l
• sum_i_l
• sum_l
• libor_l
• res_c_l
• gg_oecd_l
• cab_gdp_l
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bootstrapIM.nb.R

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bootstrapIM.normal.R

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bootstrapIM.poisson.R

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ButheMilner.R

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replicates the Buthe and Milner (2008) example from the paper.

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DreherJensen.R

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replicates the Dreher and Jensen (2007) example from the paper

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Neumayer_ISQ_2003.R

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replicates the Neumayer (2003) example from the paper

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ReadMe.txt

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ReadMe file related to this data

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vcovSACCchange.R

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