Pierre PICARDThe price of silence: tradeable noise permits and airports

This paper presents a market design for the management of noise disturbance created by aircraft traffic around large airports. A market for tradable noise permits allows noise generators to compensate harmed residents. We show that the noise permit markets allow the achievement of the planner’s optimal allocation of flights provided that she/he does not over-weight the benefit of economic activity compared to the disutility of noise disturbances. The fact that zones are likely to be strategic players does not fundamentally alter this finding. Because of the market auctioneer’s information constraints, noise permits are likely to redistribute windfall gains to residents located in non-critical zones. This entices landlords to increase their land/house rents there and to design smaller houses in the long run

THE RACE FOR POLLUTING PERMITS

International markets for tradable emission permits (TEP) co-exist with national energy taxation. A firm trading emission permits in the international market also pays energy taxes in its host country, thus creating an interaction between the international TEP-market and national energy taxes. In this paper we model that interaction in a framework of a perfectly competitive international TEP-market, where heterogeneous firms trade their TEP endowments. National governments set energy taxes non-cooperatively so as to maximize fiscal revenue from energy and profit taxes. We identify the driving forces behind Nash equilibrium taxes. We show how they depend on the total amount of TEPs in the market, on firms ’ TEP-endowment and on the number of participating countries. We also show how energy taxation varies with the introduction of the market on a previously unregulated world. Finally, we highlight the fact that the TEP-market does not achieve abatement cost efficiency, despite its being perfectly competitive. JEL Classification: Q48; Q52; H23; H73

Discussion paper. Conditional growth charts

Growth charts are often more informative when they are customized per subject, taking into account prior measurements and possibly other covariates of the subject. We study a global semiparametric quantile regression model that has the ability to estimate conditional quantiles without the usual distributional assumptions. The model can be estimated from longitudinal reference data with irregular measurement times and with some level of robustness against outliers, and it is also flexible for including covariate information. We propose a rank score test for large sample inference on covariates, and develop a new model assessment tool for longitudinal growth data. Our research indicates that the global model has the potential to be a very useful tool in conditional growth chart analysis.Comment: This paper discussed in: [math/0702636], [math/0702640], [math/0702641], [math/0702642]. Rejoinder in [math.ST/0702643]. Published at http://dx.doi.org/10.1214/009053606000000623 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Global dimensions for the recognition of prototypical urban roads in large-scale vector topographic maps

CISRG discussion paper ; 1

Sources of variability in cartographers' deconstruction of fractals : paper presented at the 18 November 1998 meeting of the British Cartographic Society's Design Group at the University of Glasgow

CISRG discussion paper ; 1

Optimal inference in a class of regression models

We consider the problem of constructing confidence intervals (CIs) for a linear functional of a regression function, such as its value at a point, the regression discontinuity parameter, or a regression coefficient in a linear or partly linear regression. Our main assumption is that the regression function is known to lie in a convex function class, which covers most smoothness and/or shape assumptions used in econometrics. We derive finite-sample optimal CIs and sharp efficiency bounds under normal errors with known variance. We show that these results translate to uniform (over the function class) asymptotic results when the error distribution is not known. When the function class is centrosymmetric, these efficiency bounds imply that minimax CIs are close to efficient at smooth regression functions. This implies, in particular, that it is impossible to form CIs that are tighter using data-dependent tuning parameters, and maintain coverage over the whole function class. We specialize our results to inference on the regression discontinuity parameter, and illustrate them in simulations and an empirical application.Comment: 39 pages plus supplementary material

Unbiased Instrumental Variables Estimation Under Known First-Stage Sign

We derive mean-unbiased estimators for the structural parameter in instrumental variables models with a single endogenous regressor where the sign of one or more first stage coefficients is known. In the case with a single instrument, there is a unique non-randomized unbiased estimator based on the reduced-form and first-stage regression estimates. For cases with multiple instruments we propose a class of unbiased estimators and show that an estimator within this class is efficient when the instruments are strong. We show numerically that unbiasedness does not come at a cost of increased dispersion in models with a single instrument: in this case the unbiased estimator is less dispersed than the 2SLS estimator. Our finite-sample results apply to normal models with known variance for the reduced-form errors, and imply analogous results under weak instrument asymptotics with an unknown error distribution