Approximate maximum entropy on the mean for instrumental variable regression

2012 ◽  
Vol 82 (5) ◽  
pp. 972-978
Author(s):  
Jean-Michel Loubes ◽  
Paul Rochet
Keyword(s):  
Maximum Entropy ◽  
Instrumental Variable ◽  
Instrumental Variable Regression ◽  
The Mean

scholarly journals The Maximum Entropy on the Mean Method for Image Deblurring

2020 ◽  
Author(s):  
Gabriel Rioux ◽  
Rustum Choksi ◽  
Tim Hoheisel ◽  
Pierre Marechal ◽  
Christopher Scarvelis
Keyword(s):  
Maximum Entropy ◽  
Image Deblurring ◽  
The Mean

scholarly journals Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data

2018 ◽  
Vol 115 (22) ◽  
pp. E4970-E4979 ◽  
Author(s):  
Thomas A. DiPrete ◽  
Casper A. P. Burik ◽  
Philipp D. Koellinger
Keyword(s):  
Instrumental Variable ◽  
Fixed Effects ◽  
Genome Wide Association Study ◽  
Body Height ◽  
Outcome Variable ◽  
Endogeneity Bias ◽  
Multiple Indicators ◽  
Instrumental Variable Regression ◽  
Genome Wide ◽  
Polygenic Scores

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA.

scholarly journals Retrospectives: Who Invented Instrumental Variable Regression?

2003 ◽  
Vol 17 (3) ◽  
pp. 177-194 ◽  
Author(s):  
James H Stock ◽  
Francesco Trebbi
Keyword(s):  
Instrumental Variables ◽  
Vegetable Oils ◽  
Instrumental Variable ◽  
Historical Evidence ◽  
Iv Estimation ◽  
Instrumental Variable Regression ◽  
Sewall Wright ◽  
Father And Son

The instrumental variables estimator first appeared explicitly in Appendix B of The Tariff on Animal and Vegetable Oils by Philip G. Wright (1928). It has been suggested that this appendix was written by Philip's son Sewall Wright, then already an important genetic statistician. To find out who wrote Appendix B, we use stylometric statistics to compare it to other texts known to have been written solely by the father and son. The sharp results are consistent with contextual and historical evidence on the authorship of Appendix B and on the origination of the idea of IV estimation.

scholarly journals Early-life Medicaid Coverage and Intergenerational Economic Mobility

2018 ◽  
Vol 59 (2) ◽  
pp. 300-315 ◽  
Author(s):  
Rourke L. O’Brien ◽  
Cassandra L. Robertson
Keyword(s):  
Low Income ◽  
Early Life ◽  
Instrumental Variable ◽  
Regression Models ◽  
Causal Effect ◽  
Economic Mobility ◽  
Medicaid Coverage ◽  
Instrumental Variable Regression ◽  
Normative Evaluation ◽  
Medicaid Eligibility

New data reveal significant variation in economic mobility outcomes across U.S. localities. This suggests that social structures, institutions, and public policies—particularly those that influence critical early-life environments—play an important role in shaping mobility processes. Using new county-level estimates of intergenerational economic mobility for children born between 1980 and 1986, we exploit the uneven expansions of Medicaid eligibility across states to isolate the causal effect of this specific policy change on mobility outcomes. Instrumental-variable regression models reveal that increasing the proportion of low-income pregnant women eligible for Medicaid improved the mobility outcomes of their children in adulthood. We find no evidence that Medicaid coverage in later childhood years influences mobility outcomes. This study has implications for the normative evaluation of this policy intervention as well as our understanding of mobility processes in an era of rising inequality.

scholarly journals Port-of-Entry Neighborhood and Its Effects on the Economic Success of Refugees in Sweden

2018 ◽  
Vol 53 (3) ◽  
pp. 671-705 ◽  
Author(s):  
Roger Andersson ◽  
Sako Musterd ◽  
George Galster
Keyword(s):  
Labor Market ◽  
Panel Data ◽  
Ethnic Group ◽  
Instrumental Variable ◽  
Group Composition ◽  
Economic Success ◽  
Instrumental Variable Regression ◽  
Working Age ◽  
Permanent Settlement ◽  
The Impact

We investigate the degree to which the ethnic group composition of “port-of-entry neighborhood” (PoE), the first permanent settlement after immigration, affects the employment prospects of refugees in Sweden during the subsequent 10 years. We use panel data on working-age adults from Iran, Iraq, and Somalia immigrating into Sweden from 1995 to 2004. We control for initial individual and labor market characteristics, use instrumental variable regression to avoid bias from geographic selection, and stratify models by gender and co-ethnic employment and education rates within the neighborhood. We find that the impact of co-ethnic neighbors in the PoE varies dramatically by gender.

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