Understanding Bias in Nonlinear Panel Models: Some Recent Developments ∗

By M. Arellano, Jinyong Hahn. 2005

277 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cb6b0867f61c2a9d9d2b73c58b7fa406dfcd2e47

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In Economics and Econometrics

Citations in the corpus

Quantile regression for dynamic panel data with fixed effects (2011, background): “…asymptotic approximations derived as both the number of individuals, N , and the number time series, T , go to infinity jointly; see, for example, Arellano and Hahn (2005) for a survey, and Hahn and Kuersteiner (2002), Alvarez and Arellano (2003), Hahn and Newey (2004), Bester and Hansen (2007),…”
Fixed Effects Estimation of Large-TPanel Data Models (2018, methodology): “In particular, Arellano & Hahn (2007) considered almost exclusively models with only unobserved individual effects, whereas we consider models with unobserved effects in multiple dimensions such as individual and time effects.”
Smoothed quantile regression for panel data (2015, unknown): “One can find some other options to correct the bias of the FE-SQR estimator from the literature (see, for instance, Hahn and Newey, 2004; Arellano and Hahn, 2005; Bester and Hansen, 2009; Dhaene and Jochmans, 2009).”
Hyperbolic Transformation and Average Elasticity in the Framework of the Fixed Effects Logit Model (2011, background): “jp 1 The origin of the CMLE proposed by Chamberlain (1980) is Rasch (1960), (1961).”
Identifying distributional characteristics in random coefficients panel data models (2009, background): “While equation (4) expresses the difference between the least-squares estimate of γi (for known δ) and its true value, equation (3) shows the link between the residuals in the individual-specific least-squares regressions and the population errors.”
Grouped effects estimators in fixed effects models (2016, background): “Nonparametric random effects estimators are proposed by Lin and Carroll (2000) and Ullah and Roy (1998), with general properties of such estimators established in Henderson and Ullah (2005).”
Robust priors in nonlinear panel data models (2007, methodology): “Up to a term in O(1/T ), this is a first-order partial differential equation of the form: ∂g ∂α f + ∂g ∂θ + h = 0, (6)”
Bias Corrections for Probit and Logit Models with Two-way Fixed Effects (2016, methodology): “d eﬀects. These corrections apply to panel datasets or other pseudo-panel c yyyyStataCorp LP st0001 2 probitfe and logitfe data structures where the two dimensions (N and T) are moderately large; see Arellano and Hahn (2007) for a survey on bias correction methods to deal with the incidental paramet”
Panel Data Models with Grouped Factor Structure Under Unknown Group Membership (2013, unknown): “i;ĝi=j ∣∣∣∣∣∣zi −Xiβ̂ − F̂ĝiλ̂ĝi,i∣∣∣∣∣∣2  , (5)”
DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS (2017, methodology): “See also Arellano and Hahn (2007) and Moon, Perron, and Phillips (2014) and references therein. (5)The “likelihood ratio” and the score used in the tests are based on the LS objective function, which can be interpreted as the (misspecified) conditional Gaussian likelihood function. (6)Another type o”
PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA (2009, unknown): “in view of ( 3 ) in Theorem 2 together with (4), for any � > 0; we can choose a small � > 0 such that infjxj�� (x) +� = inf (�� ( �); �� (�)) +� 0; we can choose a small � > 0 such that infjxj�� (x) +� = inf (�� ( �); �� (�)) +� 3 ) implies that if T is large,”
Nonparametric Identification in Nonseparable Panel Data Models with Generalized Fixed Effects (2009, background): “7 If we are interested in the effects of all regressors, there are effectively no Z’s to condition on, and the condition becomes closely related to the strict exogeneity condition in textbook linear panel data models, with the only (important) exceptions that we can allow for correlation between A…”
Estimation of spatial autoregressive panel data models with fixed effects (2010, methodology): “and Arellano and Hahn (2005), which involve approximations and related to bias corrected estimation.”