統計学輪講（第22回）

統計学輪講（第22回）
日時      2015年11月24日(火)    14時55分～16時35分
場所      経済学部新棟３階第３教室
講演者    岩倉 相雄 (経済)
演題      Deriving the Information Bounds for Nonlinear Panel Data Models with Fixed Effects

概要
This paper studies the asymptotic efficiency of estimates in nonlinear panel data models with
fixed effects when both the cross-sectional sample size and the length of time series tend to infinity.
The efficiency bounds for regular estimators are derived using the infinite-dimensional convolution
theorem by van der Varrt and Wellner (1996). It should be noted that the number of fixed effects
increases with the sample size, so they constitute an infinite-dimensional nuisance parameter. The
presence of fixed effects makes our derivation of the efficiency bounds non-trivial, and the techniques
to overcome the difficulties caused by fixed effects will be discussed in detail. Our results include the
efficiency bounds for models containing unknown functions (for instance, a distribution function of
error terms). We apply our results to show that the bias-corrected fixed effects estimator of Hahn
and Newey (2004) is asymptotically efficient.