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.

Empirical Likelihood Testing for Linear Instrumental Variable Regression Models

2014 ◽  
Vol 1008-1009 ◽  
pp. 1501-1504
Author(s):  
Pei Xin Zhao
Keyword(s):  
Linear Regression ◽  
Empirical Likelihood ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Regression Models ◽  
Testing Procedure ◽  
Likelihood Method ◽  
Linear Regression Models ◽  
Testing Method ◽  
Instrumental Variable Regression

Based on the empirical likelihood method, an instrumental variable based testing procedure is proposed for linear regression models with instrumental variables. The proposed testing method can attenuate the effect of endogeneity of covariates. Some simulations indicate that the proposed testing method is more powerful.

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 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.

Economic Crisis, Natural Resources, and Irregular Leader Removal in Autocracies

2019 ◽  
Vol 63 (3) ◽  
pp. 726-741
Author(s):  
Suthan Krishnarajan
Keyword(s):  
Natural Resources ◽  
Economic Crisis ◽  
Direct Effect ◽  
Natural Resource ◽  
Instrumental Variable ◽  
Iv Estimation ◽  
International Credit ◽  
Leader Survival ◽  
The 1960S ◽  
The Relationship

AbstractWhy do autocratic leaders escape revolution, coups, and assassination during times of economic crisis? I argue that the spike in natural resource revenues since the 1960s has increased autocratic crisis resilience. The availability of this alternative revenue stream provides autocratic leaders with a constant inflow of money, increases their ability to repress dissent, and improves their access to international credit. Extending the analysis back to 1875, I show that the relationship between economic crisis and irregular leader removal in autocracies is strong and robust before the 1960s, but disappears in more recent periods. Interaction analyses confirm that the effects of economic crisis are moderated by natural resource income. These findings are robust to an array of alternative specifications, including analyses that address endogeneity concerns via instrumental variable (IV) estimation. A more particular examination of the theoretical mechanisms also supports the argument. These findings challenge widely held beliefs in the literature of a strong, direct effect of economic crisis on autocratic leader survival; they explain why economic crisis seems to destabilize some autocrats, but not others.

Intergenerational Income Elasticities, Instrumental Variable Estimation, and Bracketing Strategies

2020 ◽  
Vol 50 (1) ◽  
pp. 1-46
Author(s):  
Pablo A. Mitnik
Keyword(s):  
Instrumental Variable ◽  
Income Elasticity ◽  
Geometric Mean ◽  
Panel Study ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Point Estimate ◽  
Set Estimation ◽  
Iv Estimation ◽  
Short Run

The fact that the intergenerational income elasticity (IGE)—the workhorse measure of economic mobility—is defined in terms of the geometric mean of children’s income generates serious methodological problems. This has led to a call to replace it with the IGE of the expectation, which requires developing the methodological knowledge necessary to estimate the latter with short-run measures of income. This article contributes to this aim. The author advances a “bracketing strategy” for the set estimation of the IGE of the expectation that is equivalent to that used to set estimate (rather than point estimate) the conventional IGE with estimates obtained with the ordinary least squares and instrumental variable (IV) estimators. The proposed bracketing strategy couples estimates generated with the Poisson pseudo–maximum likelihood estimator and a generalized method of moments IV estimator of the Poisson or exponential regression model. The author develops a generalized error-in-variables model for the IV estimation of the IGE of the expectation and compares it with the corresponding model underlying the IV estimation of the conventional IGE. By considering both bracketing strategies from the perspective of the partial-identification approach to inference, the author specifies how to construct confidence intervals for the IGEs, in particular when the upper bound is estimated more than once with different sets of instruments. Finally, using data from the Panel Study of Income Dynamics, the author shows that the bracketing strategies work as expected and assesses the information they generate and how this information varies across instruments and short-run measures of parental income. Three computer programs made available as companions to the article make the set estimation of IGEs, and statistical inference, very simple endeavors.

Testing Based on Empirical Likelihood for Partially Linear Models with Instrumental Variables

2014 ◽  
Vol 624 ◽  
pp. 500-504
Author(s):  
Pei Xin Zhao
Keyword(s):  
Empirical Likelihood ◽  
Instrumental Variables ◽  
Instrumental Variable ◽  
Linear Models ◽  
Testing Procedure ◽  
Likelihood Method ◽  
Partially Linear Models ◽  
Testing Method ◽  
Partially Linear ◽  
Instrumental Variable Method

This paper considers the model testing for partially linear models with instrumental variables. By combining the instrumental variable method and the empirical likelihood method, an instrumental variable type testing procedure is proposed. The proposed testing procedure can attenuate the effect of endogeneity of covariates. Some simulations imply that the instrumental variable based empirical likelihood testing method is more poweful.

Efficient IV Estimation in Nonstationary Regression

1995 ◽  
Vol 11 (5) ◽  
pp. 1095-1130 ◽  
Author(s):  
Yuichi Kitamura ◽  
Peter C.B. Phillips
Keyword(s):  
Instrumental Variables ◽  
Generalized Method Of Moments ◽  
Full Rank ◽  
Ordinary Least Squares ◽  
Small Sample ◽  
Estimation Methods ◽  
Limit Theory ◽  
Iv Estimation ◽  
Small Sample Properties ◽  
The Difference

A limit theory for instrumental variables (IV) estimation that allows for possibly nonstationary processes was developed in Kitamura and Phillips (1992, Fully Modified IV, GIVE, and GMM Estimation with Possibly Non-stationary Regressors and Instruments, mimeo, Yale University). This theory covers a case that is important for practitioners, where the nonstationarity of the regressors may not be of full rank, and shows that the fully modified (FM) regression procedure of Phillips and Hansen (1990) is still applicable. FM. versions of the generalized method of moments (GMM) estimator and the generalized instrumental variables estimator (GIVE) were also developed, and these estimators (FM-GMM and FM-GIVE) were designed specifically to take advantage of potential stationarity in the regressors (or unknown linear combinations of them). These estimators were shown to deliver efficiency gains over FM-IV in the estimation of the stationary components of a model.This paper provides an overview of the FM-IV, FM-GMM, and FM-GIVE procedures and investigates the small sample properties of these estimation procedures by simulations. We compare the following five estimation methods: ordinary least squares, crude (conventional) IV, FM-IV, FM-GMM, and FM-GIVE. Our findings are as follows, (i) In terms of overall performance in both stationary and nonstationary cases, FM-IV is more concentrated and better centered than OLS and crude IV, though it has a higher root mean square error than crude IV due to occasional outliers, (ii) Among FM-IV, FM-GMM, and FM-GIVE, (a) when applied to the stationary coefficients, FM-GIVE generally outperforms FM-IV and FM-GMM by a wide margin, whereas the difference between the latter two is quite small when the AR roots of the stationary processes are rather large; and (b) when applied to the nonstationary coefficients, the three estimators are numerically very close. The performance of the FM-GIVE estimator is generally very encouraging.

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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