COWLES FOUNDATION FOR RESEARCH IN
ECONOMICS Box 208281
COWLES FOUNDATION DISCUSSION PAPER NO. 1550 Indirect Inference for Dynamic Panel Models Christian Gouriéroux, Peter C. B. Phillips and Jun Yu January 2006 It is well-known that maximum likelihood (ML) estimation of the autoregressive
parameter of a dynamic panel data model with fixed effects is inconsistent under fixed
time series sample size (T) and large cross section sample size (N)
asymptotics. The estimation bias is particularly relevant in practical applications when T
is small and the autoregressive parameter is close to unity. The present paper proposes a
general, computationally inexpensive method of bias reduction that is based on indirect
inference (Gouriéroux et al., 1993), shows unbiasedness and analyzes efficiency. The
method is implemented in a simple linear dynamic panel model, but has wider applicability
and can, for instance, be easily extended to more complicated frameworks such as nonlinear
models. Monte Carlo studies show that the proposed procedure achieves substantial bias
reductions with only mild increases in variance, thereby substantially reducing root mean
square errors. The method is compared with certain consistent estimators and
bias-corrected ML estimators previously proposed in the literature and is shown to have
superior finite sample properties to GMM and the bias-corrected ML of Hahn and Kuersteiner
(2002). Finite sample performance is compared with that of a recent estimator proposed by
Han and Phillips (2005). |
