Measurement error and latent variables in econometrics

By T. Wansbeek. 2000

294 citations recorded by the source corpus.

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Citations in the corpus

Cross-Sectional Dependence in Panel Data Analysis (2012, methodology): “An alternative approach, valid for small T , is to impose a factor structure, as in ( 6 ), and use a GMM estimator that makes use of the second-order moment restrictions imposed on the covariance matrix of Φ λ i + ϵ i , where Φ = ( φ 1 ,…, φ T )′ and ϵ i = (ϵ i 1 ,…”
Inflation of Type I error rate in multiple regression when independent variables are measured with error (2009, unknown): “Today, there is a well-developed literature on regression models that incorporate measurement error; for example, see the discussions and references in Fuller (1987), Cheng & Van Ness (1999), Wansbeek & Meijer (2000) and Carroll, Ruppert, Stefanski & C. M. Crainiceanu (2006).”
Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models (2005, background): “Comparable books are those of Bartholomew and Knott (1999), who focus on latent variable models and factor analysis; Bollen (1989), who focuses mainly on structural equations with latent variables; and Wansbeek and Meijer (2000), who analyze factor and structural equation models.”
On the measurement of political instability and its impact on economic growth (2009, unknown): “4 A rigorous treatment of factor analysis can be found in Wansbeek and Meijer (2000).”
The Use of Discrete Data in PCA: Theory, Simulations, and Applications to Socioeconomic Indices (2004, background): “See also reviews of the factor models in Bai (1993) and Wansbeek & Meijer (2000).”
Minimum Distance Estimation of the Errors-In-Variables Model Using Linear Cumulant Equations (2014, unknown): “This result is unexpected because measurement error in more than one regressor typically results in large coefficient biases (e.g. Klepper and Leamer 1984; Wansbeek and Meijer 2000), and because we find low proxy quality (i.”
Estimation of Nonlinear Models with Measurement Error Using Marginal Information1 (2004, background): “Let η > 0 and define φx∗(t, η) = φx∗(t)I μ |φx∗(t)| > 1 2 η ¶ + 1 2 ηI μ |φx∗(t)| ≤ 1 2 η ¶ (69)”