GMM ESTIMATION FOR DYNAMIC PANELS WITH FIXED EFFECTS AND STRONG INSTRUMENTS AT UNITY
By Chirok Han, Peter C. B. Phillips. Econometric Theory, 2009
223 citations recorded by the source corpus.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/bd11a57cd644d6becc1478c923222abacd6975e0
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In Economics and Econometrics
- Sven-Olov Daunfeldt
- Daniel Halvarsson
- Kazuhiko Hayakawa
- S. Kripfganz
- Michael A. Clemens
- Massimo Filippini
- Anna Alberini
- Samuel Bazzi
Citations in the corpus
- Are high-growth firms one-hit wonders? Evidence from Sweden (2015, methodology): “for each T1 as n→ ∞ (Han and Phillips, 2010).”
- Estimation of fractionally integrated panels with fixed effects and cross-section dependence (2015, unknown): “that of Han and Phillips (2010), yit = (1− ρ)αi + ρyit−1 + γift + εit.”
- Equity Market Misvaluation, Financing, and Investment (2015, unknown): “If the firm were to maximize its expected present value, given the model ingredients thus far, we would have a neoclassical q model of the sort in Hayashi (1982) or Abel and Eberly (1994).”
- X-DIFFERENCING AND DYNAMIC PANEL MODEL ESTIMATION (2013, unknown): “This equation can usefully be compared with the AR(1) bias-correction transformation model 2 yit + yit−1 = ρ yit−1 + errori t (9) that was used in Phillips and Han (2008) and Han and Phillips (2010).”
- A SIMPLE EFFICIENT INSTRUMENTAL VARIABLE ESTIMATOR FOR PANEL AR(p) MODELS WHEN BOTH N AND T ARE LARGE (2009, unknown): “Recently, Han and Phillips (2009) proposed a new estimator that addresses the weak instruments problem at unity.17 They showed that the estimator does not suffer from the weak instruments problem at unity, is consistent as long as NT is large, i.e., either N or T can be fixed, and has a very small…”
- THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE (2015, background): “In line with the former approach, Hahn, Hausman, and Kuersteiner (2007), Han and Phillips (2010), and Han, Phillips, and Sul (2014) propose variants of IV/GMM estimators, while Hayakawa and Pesaran (2012) extend the ML approach of Hsiao et al. (2002) to allow for crosssectional heteroskedasticity.”