Estimating Regression Models in Which the Dependent Variable Is Based on Estimates
By Jeffrey B. Lewis, Drew A. Linzer. Political Analysis, 2005
598 citations recorded by the source corpus.
Subfields: Political Science and International Relations, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/0575172b931858d6a713f8f10d41ae8ad9104edb
How researchers see this paper
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In Political Science and International Relations
- M. Gebel
- Christopher H. Achen
- Ronald B. Davies
- Johannes Giesecke
- R. Franzese
- C. D. Vries
- Z. Studnicka
- Georgios Efthyvoulou
In Statistics and Probability
- M. Gebel
- Johannes Giesecke
- C. D. Vries
- Christopher H. Achen
- Ronald B. Davies
- R. Franzese
- Z. Studnicka
Citations in the corpus
- Empirical Strategies for Various Manifestations of Multilevel Data (2005, methodology): “Those two parameter estimates emerge by regressing b̂1j on (a constant and) zj, weighted by the estimated variance of b̂1j (Jusko and Shively 2005; Lewis and Linzer 2005).”
- Two-Step Hierarchical Estimation: Beyond Regression Analysis (2005, methodology): “However, it may be estimated by feasible GLS (FGLS) in the manner pioneered by Hanushek (1974), improved by Saxonhouse (1976), and set out in Jusko and Shively (2005) and Lewis and Linzer (2005).”
- EU Issue voting; asset or liability: How European integration affects parties' electoral fortunes (2010, methodology): “In order to explore and explain variation in EU issue voting across parties, I use a two- stage estimation procedure (Duch and Stevenson, 2005, 2007; Kedar, 2005; Lewis and Linzer, 2005).”
- Monopsony in Labor Markets: A Meta-Analysis (2020, unknown): “Thus, uij likely reflects both the sampling error and the variation in h. Lewis and Linzer (2005) and Solon, Haider, and Wooldridge (2015) argued that when the latter is relatively important, unweighted OLS may perform better than the Weighted Least Squares (WLS) approach.”
- Economic geography and wages in Brazil: Evidence from micro-data (2010, unknown): “Nevertheless, Monte Carlo experiments by Lewis and Linzer (2005) suggest that WLS can only surpass White standard error estimates in efficiency when a very high proportion (80% or more) of the residual in the final regression results from errors in the dependent variable estimation.”