Stéphane Vigeant

Cost and Production Duality: The Case of the Regulated Firm

This paper examines a generalization of cost-production duality for regulated firms. It derives an equivalency between the production function and conditional factor demands for the case where the firms optimization problem is subject to a set of additional (regulatory) constraints. This procedure is extended to an optimization problem within a dynamic framework which leads to the recovery of the firms technology.

On the existence of a regulated production function

This paper examines the generalization of the regulated production function. It characterizes the set of admissible regulatory constraints that are compatible with the existence of a regulated production function in a sufficiently weak framework to encompass the usual rate-of-return constraints à la Averch and Johnson and value constraints.

Measuring returns to scale in DEA models when the firm is regulated

In the standard framework of data envelopment analysis (DEA) models, the returns to scale are fully characterized using the multiplier on the convexity constraint of inefficient decision making units (DMU) using the projection of the input–output vector on the frontier. In this note, we investigate how the returns to scale measurements in DEA models are affected by the presence of regulatory constraints. These additional constraints change the role played by the convexity constraint. In order to avoid biased estimation of the returns to scale, we show that the interaction between the regulatory and the convexity constraints has to be taken into account.

From partial derivatives of DEA frontiers to marginal products, marginal rates of substitution, and returns to scale

The characterization of a technology, from an economic point of view, often uses the first derivatives of either the transformation or the production function. In a parametric setting, these quantities are readily available as they can be easily deduced from the first derivatives of the specified function. In the standard framework of data envelopment analysis (DEA) models these quantities are not so easily obtained. The difficulty resides in the fact that marginal changes of inputs and outputs might affect the position of the frontier itself while the calculation of first derivatives for economic purposes assumes that the frontier is held constant. We develop here a procedure to recover first derivatives of transformation functions in DEA models and we show how we can evacuate the problem of the (marginal) shift of the frontier. We show how the knowledge of the first derivatives of the frontier estimated by DEA can be used to deduce and compute marginal products, marginal rates of substitution, and returns to scale for each decision making unit (DMU) in the sample.

Technological choices and regulation: the case of the Canadian manufacturing sectors

The economic environment in which Canadian manufacturing firms operate has changed substantially over the last 40 years. Technological changes, new regulations, deregulation, and exogenous economic shocks all have been important aspects of this economic environment. In this article, we show how to include such changes in the economic environment faced by the firms in a behavioural model that includes the investment decision of the firm under uncertainty. Assumptions regarding the expectation formation process and technology are kept minimal. We estimate the effects of innovations such as the free trade agreement, the foreign investment review agency, and the federal environmental policy on the economic decisions of fifteen Canadian manufacturing sectors.

A mean-maverick game cross-efficiency approach to portfolio selection: An application to Paris stock exchange

Abstract We propose a novel framework to portfolio selection based on a combination between the maverick index and Data Envelopment Analysis (DEA) game cross-efficiency approach. While game cross-efficiency is developed as a remedy for weight multiplicity in the original cross-efficiency and peer-evaluation, we improve its use as a tool for portfolio selection. In our analysis, each financial asset is viewed as a player competing for investment funds through boosting its ranking compared to its opponents. Thus, a set of unique Nash equilibrium DEA scores to shares are provided. In addition to unique rank among financial assets, we suggest the deviation from the equilibrium rating score, the maverick index, as a consistent risk measure, that can be used as a good indicator for sensitivity to environment volatility in portfolio management. The empirical part employs a large database of European firms listed in Paris Stock Exchange to demonstrate that our approach can constitute a promising tool for stock portfolio selection. We show that the resulting portfolio is well diversified and yields higher risk-adjusted returns than other benchmark portfolios for a 6-year sample period from 2010 to 2015.