On the Role of Weight Restrictions in Data Envelopment Analysis

This paper examines the role that weight restrictions play in Data Envelopment Analysis (DEA). It is argued that the decision to include a factor (input or output) in a DEA model represents an implicit judgement that the factor has a non-trivial weight. It therefore seems perverse to allow DEA to assign a trivial weight to that factor in assessing the efficiency of a unit. There is therefore a strong case for imposing restrictions on factor weights. However, many existing methods of weight restriction are in practice unwieldy. This paper proposes an alternative approach we term contingent weight restriction which is both practical and intellectually consistent with the DEA philosophy. The paper explores the implications of alternative methods of weight restriction using simulated data from a well known production process.     

Conditional beta pricing models: A nonparametric approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.     

Foresight analysis of tilapia supply chains (Sistema Producto) in four states in Mexico: Scenarios and strategies for 2018

In order to increase competitiveness among individual producers and to encourage their integration, the federal government of Mexico has pursued the establishment, at both the state and national levels, of supply chains, or Sistema Producto (SP). For fisheries and aquaculture, 33 SPs exist at the state level and 7 at the national level, 1 of them for tilapia. The objective of this study is to carry out a foresight analysis (FA) of 4 tilapia SPs in Mexico—for the States of Colima, Sonora, Tabasco and Yucatán—analyzing their 2018 vision for the development of the tilapia chain. FA provides an integral vision of both the internal and external environments, identifying the key factors for each SP and helping to develop the best strategies in order to compete successfully. Sonora, Tabasco and Yucatán identify marketing aspects as key elements for their 10-year future: adding value to the product by means of new processing technologies, developing their own brand, and identifying and differentiating their product based on sanitary issues. Colima focuses its industry future success on efficiency improvements. Finally, the main technological disruptions identified by the 4 states are the incremental use of information technology, product and process certification, new hatcheries with genetically-improved fry, and new processing methodologies developed and/or applied.     

Correlations in emerging market bonds: The role of local and global factors

This paper empirically assesses co-movements in emerging market bond returns and disentangles the roles of external and domestic factors during episodes of heightened market volatility. The conceptual framework, set in the context of asset allocation, allows us to describe the channels through which shocks originating in a particular emerging or mature market are transmitted across countries and markets. We show that a simple measure of cross-country correlations, when presented together with the more commonly used average correlation coefficient, can be more informative during episodes of heightened market volatility. Data for the period 1997–2008 are analysed for evidence of true contagion and common external shocks.