要旨：This paper investigates how airport congestion pricing affects airline network choices. Highlighting a network that links three congestible public airports and adopting a vertical structure approach, we show that governments can use take-off and landing charges to lead an airline to choose between a hub-spoke (hub) and point-to-point (ptp) network that maximizes social welfare. Given that the hub network uses large aircraft, socially optimal charges set below airport operating costs can lead the airline to choose this network even when the (potential) hub airport is more congested than local airports. When the hub network is chosen, the total subsidy required is almost always smaller than that of a ptp network. This also yields that the hub network is socially preferable from the perspective of subsidies. In addition, asymmetric congestion degrees and complementary relationships among the airports can result in the optimal charges of some airports to be positive.

[Abstract]
　We derive the finite sample bias and mean squared error of the fully aggregated estimator (FAE) for the stationary AR(1) model with intercept, which is proposed by Han, Phillips, and Sul (2011, Econometric Theory). Our analytical results show why the FAE is less biased than the ordinary least square estimator in finite sample case and is not biased by non-normality of error distribution and by intercept term at least O(1/T ), where T is sample size. We also propose a second order unbiased FAE using the analytical result. Finally, we examine the Monte Carlo simulation and show that it is consistent with the theoretical results.