State Space Methods in gretl

gretl is a general-purpose econometric package, whose most important characteristic is being free software. This ensures that its source code is freely available under the general public license (GPL) and, like most GPL software, that it can be used free of charge. As of version 1.8.1 (released in May 2009), it offers a mechanism for handling linear state space models in a reasonably general and efficient way. This article illustrates its main features with two examples.

Nonlinear Adjustment in US Bond Yields: an Empirical Analysis with Conditional Heteroskedasticity

Starting from the work by Campbell and Shiller (1987), empirical analysis of interest rates has been conducted in the framework of cointegration. However, parts of this approach have been questioned recently, as the adjustment mechanism may not follow a simple linear rule; another line of criticism points out that stationarity of the spreads is difficult to maintain empirically. In this paper, we analyse data on US bond yields by means of an augmented VAR specification which approximates a generic nonlinear adjustment model. We argue that nonlinearity captures macro information via the shape of the yield curve and thus provides an alternative explanation for some findings recently appeared in the literature. Moreover, we show how conditional heteroskedasticity can be taken into account via GARCH specifications for the conditional variance, either univariate and multivariate.interest rates, cointegration, nonlinear adjustment, conditional heteroskedasticity

Lindahl prices solve the NIMBY problem

The siting of public facilities such as prisons or waste disposal facilities typically faces rejection by local populations (the "NIMBY" syndrome, for Not In My BackYard). These public goods exhibit a private bad aspect creating an asymmetry: all involved communities benefit from their existence, but only the host bears the local negative externality. We show that the well-known Lindahl pricing scheme constitutes the only cost-sharing method satisfying a set of properties specifically designed to handle the siting problem.Public Goods; Externalities; NIMBY; Location; Cost sharing.

Axiomatic foundation for Lindahl pricing in the NIMBY context

The siting of public facilities, such as prisons, airports or incinerators for hazardous waste typically faces social rejection by local populations (the "NIMBY" syndrome, for Not In My BackYard). These public goods exhibit a private bad aspect which creates an asymmetry: all involved communities benet from their existence, but only one (the host community) bears the local negative externality. We view the siting problem as a cost sharing issue and provide an axiomatic foundation for Lindahl pricing in this context. The set of axioms we introduce are specically designed to overcome the asymmetry of the problem.Public goods; Externalities; NIMBY; Location; Cost sharing

Choosing and Sharing

Implementing a project, like a nationwide nuclear waste disposal, which benefits all involved agents but brings major costs only to the host is often problematic. In practice, revelation issues and redistributional concerns are significant obstacles to achieving stable agreements. We address these issues by proposing the first mechanism to implement the efficient site (the host with the lowest cost) and share the exact cost while retaining total control over realized transfers. Our mechanism is simple and in the vein of the well-known Divide and Choose procedure. The unique Nash equilibrium outcome of our mechanism coincides with truthtelling, is budget-balanced, individually rational and immune to coalitional deviations. More generally, our mechanism can also handle the symmetric case of positive local externalities (e.g., Olympic Games) and even more complex situations where the usefulness of the project---regardless of its location---is not unanimous.Public goods; local externalities; NIMBY; implementation; mechanism design; VCG mechanisms

Choosing and Sharing

Choosing a project for which benefits accrue to all involved agents but brings major costs or additional benefits to only one agent is often problematic. Siting a nationwide nuclear waste disposal or hosting a major sporting event are examples of such a problem: costs or benefits are tied to the identity of the host of the project. Our goals are twofold: to choose the efficient site (the host with the lowest cost or the highest localized surplus) and to share the cost, or surplus, in a predetermined way so as to achieve redistributive goals. We propose a simple mechanism to implement both objectives. The unique subgame-perfect Nash equilibrium of our mechanism coincides with truthtelling, is efficient, budget-balanced and immune to coalitional deviations.

Why Me ? Siting a Locally Unwanted Public Good

The siting of public facilities, such as prisons, airports or incinerators for hazardous wastes faces social rejection by local population. These public goods have a private bad aspect which creates a siting problem: all communities benefit from its existence, but only one (the host) bears its cost. We tackle this inevitable asymmetry from a responsibility and equity viewpoint: the host should not be perceived as a "victim". To realize this objective, we design a method to share the total cost (the disutility of the host plus the construction cost) in a way that bypasses the natural asymmetry of the problem. We also introduce a basic incentives property which has strangely been overlooked in the existing literature: voluntary participation.

Can you do the wrong thing and still be right? Hypothesis testing in I(2) and near-I(2) cointegrated VARs

In this paper, we investigate the small-sample performance of LR tests on long-run coefficients in the I(2) model; we focus on a comparison between I(2) and near-I(2) data, i.e. I(1) data with a second root very close to unity, and report the results of some Monte Carlo experiments. With near-I(2) data, the finite-sample properties of the tests are (i) similar to those found with genuine I(2) data, (ii) systematically superior to those of the analogous tests constructed in the I(1) model, even if the latter is, in principle, correctly specified and the former is not. Therefore, there seems to be strong support to the idea that, in practice, modelling near-I(2) data using the I(2) model may be a good idea, despite the inherent misspecification