Efficient estimation of panel data models with sequential moment restrictions

By Jinyong Hahn. 1997

Abstract This paper considers asymptotically efficient estimation of the panel data models with sequential moment restrictions in an environment with i.i.d. observations. It is shown that the GMM estimator with an increasing set of instruments attains the semiparametric efficiency bound of the model. The estimator considered here allows for the heteroscedasticity of the unknown form.

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Citations in the corpus

Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning (2019, methodology): “Another approach is to use a sieve GMM estimator, using a basis expansion for fw(s) (Carrasco and Florens, 2014; Hahn, 1997).”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, methodology): “When the q -model may be nonlinear, we can obtain an eﬃcient estimator by instead applying the method of Hahn (1997) to our set of conditional moment equations.”
Semiparametric Efficiency Bound for Models of Sequential Moment Restrictions Containing Unknown Functions (2009, unknown): “Without unknown functions ho( ), model (1) becomes the one studied by Chamberlain (1992a), Hahn (1997) and Brown and Newey (1998).”
Econometric Analysis of Panel Data (2020, background): “Hahn and Newey (2004) consider two approaches to reducing the bias from fixed effects estimators in nonlinear models as T gets large.”
Nonstationary panel data analysis: an overview of some recent developments (2000, background): “(e.g., Ahn and Schmidt, 1995, Ahn and Schmidt, 1997, Arellano and Bond, 1991, Arellano and Bover, 1995, Chamberlain, 1992, and Hahn, 1997, Blundell and Bond, 1998).”
Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both N and T are Large (2002, background): “2For discussion of various instrumental variables estimators and moment restrictions, see Holtz-Eakin, Newey, and Rosen (1988), Arellano and Bond (1992), Chamberlain (1992), Ahn and Schmidt (1995), Arellano and Bover (1995), Blundell and Bond (1995), and Hahn (1997).”
Bias Corrected Instrumental Variables Estimation for Dynamic Panel Models with Fixed Effects (2001, unknown): “Ahn and Schmidt (1995), Hahn (1997), and Blundell and Bond (1998) considered further moment restrictions.”
Empirical likelihood estimation and consistent tests with conditional moment restrictions (2003, background): “Newey (1993), Hahn (1997), and Koenker and Machado (1999). The consistency result for IV is similar to that of Newey and Powell (1989). The GMM consistency result under growing numbers of moments is new.”
GMM estimation of linear panel data models with time-varying individual effects (2001, background): “Chamberlain (1992) derives the efficiency bound for the model with sequential moment restrictions, and Hahn (1997) provides the efficient GMM estimator whose asymptotic covariance matrix coincides with the efficiency bound.”
Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes (2019, methodology): “Another approach is to use a sieve GMM estimator, using a basis expansion for f w ( s ) (Carrasco and Florens, 2014; Hahn, 1997).”