A mean-variance frontier in discrete and continuous time

The paper presents a mean-variance frontier based on dynamic frictionless investment strategies in continuous time. The result applies to a finite number of risky assets whose price process is given by multivariate geometric Brownian motion with deterministically varying coefficients. The derivation is based on the solution for the frontier in discrete time. Using the same multiperiod framework as Li and Ng (2000), I provide an alternative derivation and an alternative formulation of the solution. It allows for a nice asymptotic formulation of the efficient hyperbola and its underlying efficient processes that applies in continuous time.

Finite-sample instrumental variables inference using an asymptotically pivotal statistic

This paper is concerned with investigating the role of accounting practices in radical change processes. The institutional framework has been taken as a starting point in investigating these processes. The research has been carried out at the Dutch Railways. This company was forced by the Dutch government to change from a public company into a private company. This decision by the Dutch Government has had radical consequences for Dutch Railways’ position in the (rail) transport market and for the way of managing the company. The research focuses on the processes in which the company has changed its template as a public company into a profit-oriented template. This paper examines the interaction of accounting practices with the environmental and organisational context. Emphasis is placed on how these mutual processes of interaction change internal and external positioning, create new visibilities, transform perspectives on organisational activities and performance and modify conditions for organisational change. Existing institutional concepts regarding change processes are evaluated in the light of the case findings and building blocks are developed for a comprehensive change framework.

Finite-sample instrumental variables inference using an asymptotically pivotal statistic

The paper considers the K-statistic, Kleibergen’s (2000) adaptation of the Anderson-Rubin (AR) statistic in instrumental variables regression. Compared to the AR-statistic this K-statistic shows improved asymptotic efficiency in terms of degrees of freedom in overidenti?ed models and yet it shares, asymptotically, the pivotal property of the AR statistic. That is, asymptotically it has a chi-square distribution whether or not the model is identi?ed. This pivotal property is very relevant for size distortions in ?nite-sample tests. Whereas Kleibergen (2000) focuses especially on the asymptotic behavior of the statistic, the present paper concentrates on finite-sample properties in a Gaussian framework. In that case the AR statistic has an F-distribution. However, the K-statistic is not exactly pivotal. Its finite-sample distribution is affected by nuisance parameters. Here we consider the two extreme cases, which provide tight bounds for the exact distribution. The first case amounts to perfect identification —which is similar to the asymptotic case—where the statistic has an F-distribution. In the other extreme case there is total underidentification. For the latter case we show how to compute the exact distribution. Thus we provide tight bounds for exact con?dence sets based on the efficient K-statistic. Asymptotically the two bounds converge, except when there is a large number of redundant instruments.

Exact Inference for the Linear Model with Groupwise Heteroscedasticity

Exact inference on a single coefficient in a linear regression model, as introduced by Bekker (1997), is elaborated for the case of normally distributed heteroscedastic disturbances. Instead of approximate inference based on feasible generalized least squares, exact confidence sets are formulated based on partial rotational invariance of the distribution of the vector of disturbances. The approach is applied to the random-effects and fixed-effects models for panel data.

Instrumental Variable Estimation Based on Grouped Data

The paper considers the estimation of the coefficients of a single equation in the presence of dummy intruments. We derive pseudo ML and GMM estimators based on moment restrictions induced either by the structural form or by the reduced form of the model. The performance of the estimators is evaluated for the non-Gaussian case. We allow for heteroscedasticity. The asymptotic distributions are based on parameter sequences where the number of instruments increases at the same rate as the sample size. Relaxing the usual Gaussian assumption is shown to affect the normal asymptotic distributions. As a result also recently suggested new specification tests for the validity of instruments depend on Gaussianity. Monte Carlo simulations confirm the accuracy of the asymptotic approach.

Instrumental variable estimation based on grouped data

The paper considers the estimation of the coefficients of a single equation in the presence of dummy intruments. We derive pseudo ML and GMM estimators based on moment restrictions induced either by the structural form or by the reduced form of the model. The performance of the estimators is evaluated for the non-Gaussian case. We allow for heteroscedasticity. The asymptotic distributions are based on parameter sequences where the number of instruments increases at the same rate as the sample size. Relaxing the usual Gaussian assumption is shown to affect the normal asymptotic distributions. As a result also recently suggested new specification tests for the validity of instruments depend on Gaussianity. Monte Carlo simulations confirm the accuracy of the asymptotic approach.

Efficiency bounds for instrumental variable estimators under group-asymptotics

This paper introduces a general, formal treatment of dynamic constraints, i.e., constraints on the state changes that are allowed in a given state space. Such dynamic constraints can be seen as representations of "real world" constraints in a managerial context. The notions of transition, reversible and irreversible transition, and transition relation will be introduced. The link with Kripke models (for modal logics) is also made explicit. Several (subtle) examples of dynamic constraints will be given. Some important classes of dynamic constraints in a database context will be identified, e.g. various forms of cumulativity, non-decreasing values, constraints on initial and final values, life cycles, changing life cycles, and transition and constant dependencies. Several properties of these dependencies will be treated. For instance, it turns out that functional dependencies can be considered as "degenerated" transition dependencies. Also, the distinction between primary keys and alternate keys is reexamined, from a dynamic point of view.