IMPACT OF TIME ILLIQUIDITY IN A MIXED MARKET WITHOUT FULL OBSERVATION

We study a problem of optimal investment/consumption over an infinite horizon in a market with two possibly correlated assets: one liquid and one illiquid. The liquid asset is observed and can be traded continuously, while the illiquid one can be traded only at discrete random times, corresponding to the jumps of a Poisson process with intensity λ, is observed at the trading dates, and is partially observed between two different trading dates. The problem is a nonstandard mixed discrete/continuous optimal control problem, which we solve by a dynamic programming approach. When the utility has a general form, we prove that the value function is the unique viscosity solution of the associated Hamilton–Jacobi–Bellman equation and characterize the optimal allocation in the illiquid asset. In the case of power utility, we establish the regularity of the value function needed to prove the verification theorem, providing the complete theoretical solution of the problem. This enables us to perform numerical simulations, so as to analyze the impact of time illiquidity and how this impact is affected by the degree of observation.     

Regression with imputed covariates: A generalized missing-indicator approach

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper, we formalize this trade-off by showing that one can augment the regression model with a set of auxiliary variables so as to obtain, under weak assumptions about the imputations, the same unbiased estimator of the parameters of interest as complete-case analysis. Given this augmented model, the bias-precision trade-off may then be tackled by either model reduction procedures or model averaging methods. We illustrate our approach by considering the problem of estimating the relation between income and the body mass index (BMI) using survey data affected by item non-response, where the missing values on the main covariates are filled in by imputations.     

Spatial spillovers in emerging market spreads

We use novel spatial econometrics techniques to explore spillovers in the sovereign bond market for 24 emerging economies during 1995–2010. The article extends the previous literature focusing on spillover effects from advanced to emerging economies by analyzing transmission of shocks across emerging markets. After controlling for the impact of global factors, we find strong evidence of spillovers from both sovereign spreads and macroeconomic fundamentals in neighboring emerging economies. In addition to the geographical proximity, the channels of spatial transmission include trade and financial linkages. The results of the article highlight the importance of accounting not only for spillovers from advanced economies to emerging markets but also across emerging markets when analyzing sovereign spreads.     

Family Involvement in the Board of Directors: Effects on Sales Internationalization

Previous research shows that family involvement in the board of directors can be both positive and negative for sales internationalization. The ambiguous nature of this relationship has hindered theory building on this important phenomenon. Integrating stewardship, stagnation, and upper echelons perspectives, we propose a nonlinear, J‐shaped relationship between family involvement in the board of directors and sales internationalization. Results from running ordinal regression analysis on data drawn from 203 U.S. family businesses confirmed our conjecture. We discuss the implications of our findings for family business theory and practice and indicate avenues for future research.     

International trade policies,industrialization, and economic development

During the 1950s and 1960s, most developing nations, particularly the larger ones, strongly opted for a policy of import substitution industrialization (ISI). This was based on heavy protection and generally led to very inefficient industries. Since the early 1970s, an increasing number of developing countries deregulated their economies and liberalized trade, and this stimulated efficiency and growth. Some developing nations also tried strategic trade policies and to endogenize growth (as postulated by endogenous growth theory), but with only limited success. It seems impossible and inconsistent under the new international trade rules, however, for other developing countries to duplicate the East Asia “miracle,” which was based on strong government support for domestic industry while stimulating competition and efficiency among domestic firms. The successful completion of the Uruguay Round is expected to greatly benefit developing countries through continued deregulation and increased access to developed-country markets.     

Unawareness, priors and posteriors

This note contains first thoughts on awareness of unawareness in a simple dynamic context where a decision situation is repeated over time. The main consequence of increasing awareness is that the model the decision maker uses, and the prior which it contains, becomes richer over time. The decision maker is prepared to this change, and we show that if a projection-consistency axiom is satisfied unawareness does not affect the value of her estimate of a payoff-relevant conditional probability (although it may weaken confidence in such estimate). Probability-zero events however, pose a challenge to this axiom, and if that fails, even estimate values will be different if the decision maker takes unawareness into account. In examining evolution of knowledge about relevant variable through time, we distinguish between transition from uncertainty to certainty and from unawareness to certainty directly, and argue that new knowledge may cause posteriors to jump more if it is also new awareness. Some preliminary considerations on convergence of estimates are included.      

A Measure of Representativeness of a Sample for Inferential Purposes

After defining the concept of representativeness of a random sample, the author proposes a measure of how much the observed sample represents its parent distribution. This measure is called Representativeness Index. The same measure, seen as a function of a sample and of a distribution, will be called Representativeness Function. For a given sample it provides the value of the index for the different distributions under examination, and for a given distribution it provides a measure of the representativeness of its possible samples. Such Representativeness Function can be used in an inferential framework just as the likelihood function, since it gives to any distribution the "experimental support" provided by the observed sample. This measure is distribution-free and it is shown to be a transformation of the wellknown Cramér–von Mises statistic. By using the properties of that statistic, criteria for providing set estimators and tests of hypotheses are introduced. The utilization of the representativeness function in many standard statistical problems is outlined through examples. The quality of the inferential decisions can be assessed with the usual techniques (MSE, power function, coverage probabilities). The most interesting examples turn out to be those of situations that are "non-regular", as for instance the estimation of parameters involved in the support of the parent distribution, or less explored (model choice).     

A fuzzy set theory based computational model to represent the quality of inter-rater agreement

In this paper we present a method to evaluate the quality of a rater’s judgement, which can integrate and enrich the use of inter-rater agreement as a reliability measure. Our proposal is an integrative one and evaluates the quality of a rater’s performance through an analysis of the profile of that individual rater’s performance. We discuss its rationale on the basis of the interpretation of inter-rater agreement, highlighting some critical issues. For this purpose, we adopt a computational model based on fuzzy set theory, demonstrating its main characteristics with an exemplary case study.     

Managing Environmental Risk in Presence of Climate Change: The Role of Adaptation in the Nile Basin of Ethiopia

This study investigates the impact of climate change adaptation on farm households’ downside risk exposure in the Nile Basin of Ethiopia. The analysis relies on a moment-based specification of the stochastic production function. We use an empirical strategy that accounts for the heterogeneity in the decision on whether to adapt or not, and for unobservable characteristics of farmers and their farm. We find that past adaptation to climate change (i) reduces current downside risk exposure, and so the risk of crop failure; (ii) would have been more beneficial to the non-adapters if they adapted, in terms of reduction in downside risk exposure; and (iii) is a successful risk management strategy that makes the adapters more resilient to climatic conditions.