Estimation of Limited-Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice

By Joshua D. Angrist. 2000

252 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Gender Studies

View Paper: https://www.semanticscholar.org/paper/468ebc40def7f86ba5159a4a730e9f1d5c97896b

Citations in the corpus

• Dif-in-Dif Estimators of Multiplicative Treatment Effects (2014, unknown): “However, if it is heterogeneous in the population, exp(δ) − 1 captures the multiplicative treatment effect on the average, and not the average multiplicative effect (Angrist 2001, makes a similar point for the identification of treatment effects in IV estimation of exponential models).”
• The Effect of Information on Voter Turnout: Evidence from a Natural Experiment (2005, unknown): “†David Dreyer Lassen is Assistant Professor of Economics and research fellow of Economic Policy Research Unit, University of Copenhagen, Studiestræde 6, DK-1455 Copenhagen K, Denmark.”
• Fixed effects estimation of structural parameters and marginal effects in panel probit models (2007, unknown): “In empirical analysis, the ultimate quantities of interest are often the marginal effects of specific changes in the regressors on the conditional response probability (see, e.g., Ruud, 2000; Angrist, 2001; Wooldridge, 2002; and Wooldridge, 2005).”
• Money for Nothing? Universal Child Care and Maternal Employment (2011, unknown): “As noted by Angrist (2001), if there are no covariates or they are discrete, as in our case, linear models are no less appropriate for limited dependent variables than for other types of dependent variables.”
• Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh (2007, unknown): “household income falls below the poverty line – makes sense when it is to estimate causal effects ( Angrist, 2001 ).”
• Controlling for endogeneity with instrumental variables in strategic management research (2008, unknown): “Researchers should refrain from using such methods and use, instead, a linear regression for the first stage (see Angrist, 2001, 2006; Angrist and Krueger, 2001; and, for a more general treatment, Kelejian, 1971).”
• Spousal Control and Intra-household Decision Making: An Experimental Study in the Philippines (2009, unknown): “Columns (2), (4), and (6) estimate with both own and spouse's control variables.”
• Does Collateral Help Mitigate Adverse Selection? A Cross-Country Analysis (2011, unknown): “We follow Angrist (2001) and estimate the equation explaining Collateral with a probit regression in a first stage.”
• Dropping Out: Why are Students Leaving Junior High in China's Poor Rural Areas? (2012, unknown): “In the extreme case of a fully saturated model, the linear probability model is completely general and has the fitted probabilities within the interval [0,1] (Angrist, 2001; Wooldridge, 2001).”
• Is the Magic Still There? The Use of the Heckman Two-Step Correction for Selection Bias in Criminology (2007, unknown): “A number of classic econometric treatments of these issues already exist (Amemiya 1985; Heckman 1979; Maddala 1985, Vella 1999), along with versions written specifcally for sociologists (Berk 1983; Berk and Ray 1982; Stolzenberg and Relles 1997; Winship and Mare 1992).”
• No Child Left Behind: Subsidized Child Care and Children's Long-Run Outcomes (2011, unknown): “As noted by Angrist (2001), if there are no covariates or they are discrete, as in our case, linear models are no less appropriate for limited dependent variables than for other types of dependent variables.”
• Sample Selection Bias and Heckman Models in Strategic Management Research (2016, unknown): “In contrast, Heckman models incorporate exclusion restrictions to compute an adjustment factor (i.e., IMR) that is included in the second-stage estimation (Angrist, 2001; Bushway, Johnson, and Slocum, 2007).”