GMM and 2SLS estimation of mixed regressive, spatial autoregressive models

By Lung-fei Lee. 2007

480 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/18fbd52dfd0a15db7c7de014d8a59374935038c1

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In Economics and Econometrics

Citations in the corpus

A SPATIAL DYNAMIC PANEL DATA MODEL WITH BOTH TIME AND INDIVIDUAL FIXED EFFECTS (2009, methodology): “For a spatial dynamic panel model, if λ0 +ρ0 + γ0 = 1, Ynt might have deterministic trends and also a nonstationary stochastic component (for details, see Yu et al., 2007; Yu and Lee, 2007).”
An efficient GMM estimator of spatial autoregressive models (2010, methodology): “(2) At θ0, gn(θ0) = (Qn, P1nϵn, . . . , Pmnϵn)′ϵn has a zero mean because E(Q ′nϵn) = Q ′ nE(ϵn) = 0 and E(ϵ ′ nP ′ jnϵn) = σ 2 0 tr(Pjn) = 0 for j = 1, . . . ,m. Lee (2007) has shown the consistency and asymptotic normality of the GMME for theMRSARmodel with i.i.d. disturbances.”
Estimation of spatial autoregressive panel data models with fixed effects (2010, methodology): “One may have a deeper understanding of that model with the approach in Lee (2007b) via a group setting.”
Instrumental Variable Quantile Estimation of Spatial Autoregressive Models (2007, background): “One is the maximum likelihood (ML) or quasi-maximum likelihood (QML) estimator; see, among the others, Ord (1975), Anselin (1988), and Lee (2004).”
Determinants of International Migration Flows to and from Industrialized Countries: A Panel Data Approach beyond Gravity 1 (2010, methodology): “the exogenous independent variables in the model are irrelevant (Lee, 2007).”
A general method for third-order bias and variance corrections on a nonlinear estimator (2015, methodology): “Lee (2007b) shows that the group size variation plays an important role in the identification and estimation of econometric models with group interactions, contextual factors and fixed effects.”
EFFICIENT GMM ESTIMATION OF HIGH ORDER SPATIAL AUTOREGRESSIVE MODELS WITH AUTOREGRESSIVE DISTURBANCES (2009, methodology): “It follows from Lee (2007a) that n = ( 0kIV×kIV μ3 Q′nωnm μ3ω ′ nm Qn (μ4 −3σ 40 )ω′nmωnm ) + Vn, (9) with ωnm = [vecD(P1n), . . . ,vecD(Pmn)], and Vn = σ 40 ⎛⎜⎜⎜⎜⎝ 1 σ 20 Q′n Qn 0kIV×1 · · · 0kIV×1 01×kIV tr(Ps1n P1n) · · · tr(Ps1n Pmn) ... ... . . . ... 01×kIV tr(Psmn P1n) · · · tr(Psmn…”
Large sample properties of the matrix exponential spatial specification with an application to FDI (2015, methodology): “When the spatial process is stable, the MESS has many advantages over the SAR model.”
Estimation of Sample Selection Models with Spatial Dependence (2012, methodology): “For a related approach based on GMM for this linear model with SAE, see Lee (2001).”
GMM estimation of spatial autoregressive models with unknown heteroskedasticity (2010, methodology): “The consistency of the GMME in Lee (2001, 2007a) with Pn from P1n which is a class of constant n × n matrices Pn with tr(Pn) = 0; or P2n, a subclass of P1n with Diag(Pn) = 0, is based on the fundamental moment property that E( ′ nPn n) = 0.”
Identifying Peer Effects in Student Academic Achievement by Spatial Autoregressive Models with Group Unobservables (2010, methodology): “This study confronts these conceptual problems by using a spatial autoregressive (SAR) model with both endogenous and contextual effects as well as group fixed effects, a generalized version of the model in Lee (2007b).”
Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances (2010, background): “It is not difficult to see that this linear independence condition is an analog to the identification conditions postulated in Lee (2007b), Assumption 5(b), relating to quadratic forms under homoskedasticity.”
Cross-Sectional Dependence in Panel Data Analysis (2012, background): “(51) The main di¤erence with (39) is that the moment conditions above are non-linear because the time-specic nuisance parameters, %t, need to be estimated jointly with the structural parameter, .”