BIAS REDUCTION FOR DYNAMIC NONLINEAR PANEL MODELS WITH FIXED EFFECTS

By Jinyong Hahn, G. Kuersteiner. Econometric Theory, 2011

184 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d21e2e5d152563142633514c8f649eb556a6555f

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In Economics and Econometrics

Citations in the corpus

Discretizing Unobserved Heterogeneity (2017, background): “Moreover, the first term on the right-hand side of (12) coincides with the bias of FE (e.”
TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS (2013, background): “For this reason, Hahn and Kuersteiner (2011) assume that the individual fixed effects are nonrandom and uniformly bounded in their study of nonlinear dynamic panel data models.”
PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA (2009, background): “In order to rule out the incidental parameters problem in the models considered in this literature, it is theoretically necessary for N = o(T ); see Hahn and Kuersteiner (2002, 2004), Woutersen (2002), Carro (2007), Hahn and Newey (2004), Fernandez-Val (2005), or Arellano and Hahn (2007).”
Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects (2016, unknown): “For this reason, Hahn and Kuersteiner (2011) assume that the individual fixed effects are nonrandom and uniformly bounded in their study of nonlinear dynamic panel data models.”
Penalized Quantile Regression for Dynamic Panel Data (2010, background): “See, for example, Arellano and Hahn (2007) for a survey, and Hahn and Kuersteiner (2002, 2003), Alvarez and Arellano (2003), Hahn and Newey (2004), Arellano and Hahn (2006), Bester and Hansen (2009), for specific approaches.”
Determining the number of groups in latent panel structures with an application to income and democracy (2017, background): “In the case of dynamic panel data models, Hahn and Kuersteiner (2011) assume that ’s are nonrandom and uniformly bounded, in which case the strong mixing condition can also be verified.”
Estimation of Censored Quantile Regression for Panel Data With Fixed Effects (2013, methodology): “Condition E1 is similar to condition 1 of Hahn and Kuersteiner (2011) and Kato, Galvao, and Montes-Rojas (2012). Condition E2 imposes a new restriction on the conditional densities, but it is also standard as in Kato, Galvao, and Montes-Rojas (2012). Finally, E3 defines the long-run variance-covaria”
Fixed Effects Estimation of Large-TPanel Data Models (2018, background): “For nonlinear models, Hahn & Newey (2004) and Hahn & Kuersteiner (2011) derived B in the static and dynamic case, respectively.”
Estimation of random coefficients logit demand models with interactive fixed effects (2012, unknown): “in Hahn and Newey (2004), Bai (2009), etc.”
DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS (2017, unknown): “Furthermore, as in Hahn & Kuersteiner (2011), let F tτ (i) = C∨σ ({(eis,vis) : τ ≤ s ≤ t}), and define the conditional α-mixing coefficient on C: αm(i) = sup A∈F t−∞(i),B∈F∞t+m (i) [P(A ∩ B)−P(A)P(B) |C] .”
Sieve Estimation of Time-Varying Panel Data Models With Latent Structures (2019, background): “If λi’s are stochastic, we can follow Hahn and Kuersteiner (2011) and Su and Chen (2013) and adopt the concept of conditional strong mixing, where the mixing coefficients are defined by conditioning on the fixed effects.”
Asymptotics for panel quantile regression models with individual effects (2012, methodology): “The present results are extended below (Section 5) to the dependent case under suitable mixing conditions as in Hahn and Kuersteiner (2004).4 Condition (A2) corresponds to the moment condition of Fernandez-Val (2005, p.12).”
Specification Test for Panel Data Models with Interactive Fixed Effects (2014, unknown): “Like Hahn and Kuersteiner (2011), we assume the stationarity condition mainly for notational simplicity.”
Split-Panel Jackknife Estimation of Fixed-Effect Models (2015, methodology): “Fernández-Val and Vella (2011) provide the exact bias expression for this case and extend the analytical bias-correction approach of Hahn and Kuersteiner (2011) to two-step estimators.”
Model Selection in the Presence of Incidental Parameters (2015, unknown): “Bias reduction The standard bias corrected estimators in nonlinear (dynamic) fixed effect regressions correspond to  ψ in (6) and are given by (e.g., Hahn and Newey, 2004; Arellano and Hahn, 2007; Hahn and Kuersteiner, 2011)  ψ =  ψ − 1 T  1 n n ”