Manifest Contracts

Eiffel popularized design by contract, a software design philosophy where programmers specify the requirements and guarantees of functions via executable pre- and post-conditions written in code. Findler and Felleisen brought contracts to higher-order programming, inspiring the PLT Racket implementation of contracts. Existing approaches for runtime checking lack reasoning principles and stop short of their full potential---most Racket contracts check only simple types. Moreover, the standard algorithm for higher-order contract checking can lead to unbounded space consumption and can destroy tail recursion. In this dissertation, I develop so-called manifest contract systems which integrate more coherently in the type system, and relate them to Findler-and-Felleisen-style latent contracts. I extend a manifest system with type abstraction and relational parametricity, and also show how to integrate dynamic types and contracts in a space efficient way, i.e., in a way that doesn\u27t destroy tail recursion. I put manifest contracts on a firm type-theoretic footing, showing that they support extensions necessary for real programming. Developing these principles is the first step in designing and implementing higher-order languages with contracts and refinement types

Equilibrium in Competitive Insurance Markets with Moral Hazard

This paper examines the existence and nature of competitive equilibrium with moral hazard. The more insurance an individual has, the less care will he take. Consequently, insurance firms attempt to restrict their clients' aggregate insurance purchases. If individuals' aggregate insurance purchases are observable, each firm will ration the amount of insurance its clients can purchase and insist that they purchase no insurance from other firms. This paper focuses on the alternative situation where firms cannot observe their clients' aggregate insurance purchases. We show that firms will still attempt to restrict their clients' aggregate purchases, but now they must do so indirectly. One possibility is that all firms sell only policies with a sufficiently large amount of coverage that individuals choose to purchase insurance from only one firm. Another possibility is that each firm offers a latent policy in addition to its regular policy. Latent policies are not purchased in equilibrium, but serve to restrict entry. If an entering firm offers a supplementary policy, an individual will purchase not only this policy plus his previous policy but also the latent policy. The latent policy is designed so that the individual reduces effort by enough to render any entering policy unprofitable.

Locally Adaptive Dynamic Networks

Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with current methods include the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actors' connectivity behaviors. Motivated by this application, we develop a novel method for Locally Adaptive DYnamic (LADY) network inference. The proposed model relies on a dynamic latent space representation in which each actor's position evolves in time via stochastic differential equations. Using a state space representation for these stochastic processes and P\'olya-gamma data augmentation, we develop an efficient MCMC algorithm for posterior inference along with tractable procedures for online updating and forecasting of future networks. We evaluate performance in simulation studies, and consider an application to face-to-face contacts among individuals in a primary school

Investigating farmers' preferences for the design of agri-environment schemes: a choice experiment approach

In recent decades agri-environment schemes (AES) have become an increasingly important tool for policy makers aiming to reverse the post-war decline in environmental quality on agricultural land. The voluntary nature of such schemes means that the decision of farmers to participate is central to achieving policy objectives. Therefore, this paper uses a choice experiment approach to investigate the role that scheme design can have on encouraging farmers to participate. Choice data was gathered from a survey of farmers in 10 case study areas across the EU and analysed using both mixed logit and latent class models. In general, farmers were found to require greater financial incentives to join schemes with longer contracts or that offer less flexibility or higher levels of paperwork. It was also observed that a large segment of farmers ('low resistance adopters') would be willing to accept relatively small incentive payments for their participation in schemes offering relatively little flexibility and high levels of additional paperwork, when compared to a contrasting segment of 'high resistance adopters'. © 2009 University of Newcastle upon Tyne

Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices

Modeling correlation (and covariance) matrices can be challenging due to the positive-definiteness constraint and potential high-dimensionality. Our approach is to decompose the covariance matrix into the correlation and variance matrices and propose a novel Bayesian framework based on modeling the correlations as products of unit vectors. By specifying a wide range of distributions on a sphere (e.g. the squared-Dirichlet distribution), the proposed approach induces flexible prior distributions for covariance matrices (that go beyond the commonly used inverse-Wishart prior). For modeling real-life spatio-temporal processes with complex dependence structures, we extend our method to dynamic cases and introduce unit-vector Gaussian process priors in order to capture the evolution of correlation among components of a multivariate time series. To handle the intractability of the resulting posterior, we introduce the adaptive $\Delta$-Spherical Hamiltonian Monte Carlo. We demonstrate the validity and flexibility of our proposed framework in a simulation study of periodic processes and an analysis of rat's local field potential activity in a complex sequence memory task.Comment: 49 pages, 15 figure

How Licensing Resolves Hold-Up: Evidence from a Dynamic Panel Data Model with Unobserved Heterogeneity

In a patent thicket licensing provides a mechanism to either avoid or resolve hold-up. Firms' R&D incentives will differ depending on how licensing is used. In this paper we study the choice between ex ante licensing to avoid hold-up and ex post licensing to resolve it. Building on a theoretical model of a patent portfolio race, firms' choices of licensing contracts are modelled. We derive several hypotheses from the model and find support for these using data from the semiconductor industry. The empirical results show that firms' relationships in product markets and technology space jointly determine the type of licensing contract chosen. Implications for the regulation of licensing are discussed. We estimate a dynamic panel data model with unobserved heterogeneity and a lagged dependent variable. A method suggested by Wooldridge (2005) is employed to estimate a random effects probit model using conditional maximum likelihood

A State-Space Estimation of the Lee-Carter Mortality Model and Implications for Annuity Pricing

In this article we investigate a state-space representation of the Lee-Carter model which is a benchmark stochastic mortality model for forecasting age-specific death rates. Existing relevant literature focuses mainly on mortality forecasting or pricing of longevity derivatives, while the full implications and methods of using the state-space representation of the Lee-Carter model in pricing retirement income products is yet to be examined. The main contribution of this article is twofold. First, we provide a rigorous and detailed derivation of the posterior distributions of the parameters and the latent process of the Lee-Carter model via Gibbs sampling. Our assumption for priors is slightly more general than the current literature in this area. Moreover, we suggest a new form of identification constraint not yet utilised in the actuarial literature that proves to be a more convenient approach for estimating the model under the state-space framework. Second, by exploiting the posterior distribution of the latent process and parameters, we examine the pricing range of annuities, taking into account the stochastic nature of the dynamics of the mortality rates. In this way we aim to capture the impact of longevity risk on the pricing of annuities. The outcome of our study demonstrates that an annuity price can be more than 4% under-valued when different assumptions are made on determining the survival curve constructed from the distribution of the forecasted death rates. Given that a typical annuity portfolio consists of a large number of policies with maturities which span decades, we conclude that the impact of longevity risk on the accurate pricing of annuities is a significant issue to be further researched. In addition, we find that mis-pricing is increasingly more pronounced for older ages as well as for annuity policies having a longer maturity.Comment: 9 pages; conference pape

Uncovering predictability in the evolution of the WTI oil futures curve

Accurately forecasting the price of oil, the world's most actively traded commodity, is of great importance to both academics and practitioners. We contribute by proposing a functional time series based method to model and forecast oil futures. Our approach boasts a number of theoretical and practical advantages including effectively exploiting underlying process dynamics missed by classical discrete approaches. We evaluate the finite-sample performance against established benchmarks using a model confidence set test. A realistic out-of-sample exercise provides strong support for the adoption of our approach with it residing in the superior set of models in all considered instances.Comment: 28 pages, 4 figures, to appear in European Financial Managemen