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Author: Holm, Anders
Resulting in 2 citations.
1. Breen, Richard
Choi, Seongsoo
Holm, Anders
Heterogeneous Causal Effects and Sample Selection Bias
Sociological Science published online (8 July 2015): DOI: 10.15195/v2.a17.
Also: https://www.sociologicalscience.com/articles-v2-17-351/
Cohort(s): NLSY79
Publisher: Sociological Science
Keyword(s): College Degree; Educational Returns; Heterogeneity; Selectivity Bias/Selection Bias

Permission to reprint the abstract has not been received from the publisher.

The role of education in the process of socioeconomic attainment is a topic of long standing interest to sociologists and economists. Recently there has been growing interest not only in estimating the average causal effect of education on outcomes such as earnings, but also in estimating how causal effects might vary over individuals or groups. In this paper we point out one of the under-appreciated hazards of seeking to estimate heterogeneous causal effects: conventional selection bias (that is, selection on baseline differences) can easily be mistaken for heterogeneity of causal effects. This might lead us to find heterogeneous effects when the true effect is homogenous, or to wrongly estimate not only the magnitude but also the sign of heterogeneous effects. We apply a test for the robustness of heterogeneous causal effects in the face of varying degrees and patterns of selection bias, and we illustrate our arguments and our method using National Longitudinal Survey of Youth 1979 (NLSY79) data.
Bibliography Citation
Breen, Richard, Seongsoo Choi and Anders Holm. "Heterogeneous Causal Effects and Sample Selection Bias." Sociological Science published online (8 July 2015): DOI: 10.15195/v2.a17.
2. Holm, Anders
Hjorth-Trolle, Anders
Andersen, Robert
Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate
Holm, A., Hjorth-Trolle, A., & Andersen, R. (2023). Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate. Sociological Methods & Research, 0(0).
Also: https://doi.org/10.1177/00491241231176845
Cohort(s): Children of the NLSY79
Publisher: Sage Publications
Keyword(s): Methods/Methodology; Peabody Individual Achievement Test (PIAT- Reading); Statistics

Permission to reprint the abstract has not been received from the publisher.

Lagged dependent variables (LDVs) are often used as predictors in ordinary least squares (OLS) models in the social sciences. Although several estimators are commonly employed, little is known about their relative merits in the presence of classical measurement error and different longitudinal processes. We assess the performance of four commonly used estimators: (1) the standard OLS estimator, (2) an average of past measures (AVG), (3) an instrumental variable (IV) measured at one period previously (IV), and (4) an IV derived from information from more than one time before (IV2). We also propose a new estimator for fixed effects models—the first difference instrumental variable (FDIV) estimator. After exploring the consistency of these estimators, we demonstrate their performance using an empirical application predicting primary school test scores. Our results demonstrate that for a Markov process with classic measurement error (CME), IV and IV2 estimators are generally consistent; LDV and AVG estimators are not. For a semi-Markov process, only the IV2 estimator is consistent. On the other hand, if fixed effects are included in the model, only the FDIV estimator is consistent. We end with advice on how to select the appropriate estimator.
Bibliography Citation
Holm, Anders, Anders Hjorth-Trolle and Robert Andersen. "Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate." Holm, A., Hjorth-Trolle, A., & Andersen, R. (2023). Lagged Dependent Variable Predictors, Classical Measurement Error, and Path Dependency: The Conditions Under Which Various Estimators are Appropriate. Sociological Methods & Research, 0(0).