GMM estimation of linear panel data models with time-varying individual effects

By Seung C. Ahn, Young Hoon Lee, P. Schmidt. 2001

267 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/48cc905992384fa176fc8f892813f2de24909256

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In Economics and Econometrics

Citations in the corpus

Large Covariance Estimation by Thresholding Principal Orthogonal Complements (2011, unknown): “The above panel regression model has been considered by many researchers, such as Ahn, Lee and Schmidt (2001), Pesaran (2006), and has broad applications in social sciences.”
IV Estimation of Panels with Factor Residuals (2010, background): “One can interpret such a procedure as allowing some degree of cross-sectional dependence in the model residuals.(2) Consider the simplest case of a one-factor, one-regressor model in the standard form x1it = φx2it + λift + εit t = 1, .”
Controlling for Individual Heterogeneity in Longitudinal Models, with Applications to Student Achievement (2007, background): “For example, Ahn, Lee and Schmidt (2) and Han, Orea and Schmidt (18) develop and compare consistent generalized method of moments and concentrated least squares estimators under structural models similar to our Example 1, where the coefficients on the individual-specific factors vary across measurem”
Stochastic Frontier Analysis using Stata (2013, methodology): “However, their consistency requires white noise errors and they are less efficient than the GMM estimator proposed by Ahn et al. (2001) and Ahn et al.”
Random Coefficient Models (2008, background): “Examples of studies that have used the residual factor structure to model cross section dependence include Holtz-Eakin, Newey, and Rosen (1988), Ahn, Lee and Schmidt (2001) , Coakley, Fuertes and Smith (2005), Bai and Ng (2004), Kapetanios and Pesaran (2007), Phillips and Sul (2003), Moon and Perro”
A test of cross section dependence for a linear dynamic panel model with regressors (2009, background): “i=1 Z Y i u o i ! = N , where N = O(1) is a (T 1 1) vector whose rst element is N 1 PN i=1 E(yi0 i 0) f2 and the (t 1) elements are N 1 PN i=1 E y i0 i 0 ft+2 + f 0 t+2 i i 0Pt 1 j=0 ft j , for t = 2; 3; :::; T 1.”
Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls (2016, unknown): “Estimation could also proceed with the estimation method proposed by Ahn et al. (2013) and thus dispense with the assumption that T → ∞.”
Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity (2018, methodology): “yt = λMyt + Ztβ + εt = Wtδ + εt, εt = μ+ ut, (1)”
The Impact of Piped Water Provision on Infant Mortality in Brazil: A Quantile Panel Data Approach (2010, unknown): “…the impact of these programs on the subpopulations that are most at risk; if unobservables are important determinants 6 The quantile panel data technique we employ, like other fixed effect models, cannot correct the bias arising from time varying unobservables (Ahn, Lee, and Schmidt, 2001).”
General Diagnostic Tests for Cross Section Dependence in Panels (2020, unknown): “However,(6) ε′iMiεi T = 1+ op (1) , ε′iAiεj √ T = ( ε′iXi T )( X ′ iXi T )−1 ( X ′ iεj √ T ) = op (1) , ε′iAiAjεj √ T = ( ε′iXi T )( X ′ iXi T )−1 ( X ′ iXj T )( X ′ jXj T )−1 ( X ′ jεj √ T ) = op (1) .”
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure (2006, background): “Theorem 1 Consider the panel data model (1) and (2), and suppose that k ik < K, k ik < K, assumptions 1,2, and 5a hold, (N;T ) j !1 (in no particular order), and the rank condition (21) is satised.”
Estimation of a Panel Data Model with Parametric Temporal Variation in Individual Effects (2004, background): “lated in Ahn, Lee and Schmidt (2001, p. 251) .”