How far from identifiability? A systematic overview of the statistical matching problem in a non parametric framework

Statistical matching consists in estimating the joint characteristics of two variables observed in two distinct and independent sample surveys, respectively. In a parametric setup, ranges of estimates for non identifiable parameters are the only estimable items, unless restrictive assumptions on the probabilistic relationship between the non jointly observed variables are imposed. These ranges correspond to the uncertainty due to the absence of joint observations on the pair of variables of interest. The aim of this paper is to analyze the uncertainty in statistical matching in a non parametric setting. A measure of uncertainty is introduced, and its properties studied: this measure studies the “intrinsic” association between the pair of variables, which is constant and equal to 1/6 whatever the form of the marginal distribution functions of the two variables when knowledge on the pair of variables is the only one available in the two samples. This measure becomes useful in the context of the reduction of uncertainty due to further knowledge than data themselves, as in the case of structural zeros. In this case the proposed measure detects how the introduction of further knowledge shrinks the intrinsic uncertainty from 1/6 to smaller values, zero being the case of no uncertainty. Sampling properties of the uncertainty measure and of the bounds of the uncertainty intervals are also proved.     

Paradigmatic experiments: The Dictator Game

Recent experiments with the Dictator Game (and the ensuing discussions) have been affected by considerable confusion regarding the purpose of this design. A common complaint is that the design gives rise to fragile regularities and therefore is of little use for theory-testing. We take issue with this view, and instead argue that the Dictator Game is potentially a very useful tool for experimental game theory, if properly used. It is particularly useful for investigating social norms, but economists have failed to take advantage of the Dictator Game because they still lack an adequate theory of norms.     

Emergency Workers’ Quality of Life: The Protective Role of Sense of Community, Efficacy Beliefs and Coping Strategies

This study, involving a sample of 764 emergency workers, investigates dimensions of quality of life at work (Compassion fatigue, Burnout and Compassion satisfaction), and their relationships with Coping strategies and some psychosocial variables (Sense of Community, Collective Efficacy and Self-efficacy). Results indicate the usefulness of distinguishing between positive and negative indicators of emergency workers’ quality of life. Compassion satisfaction is positively correlated with efficacy beliefs, Sense of Community and the use of Active coping strategies. Burnout and Compassion fatigue are especially correlated with the use of dysfunctional coping strategies like distraction and self-criticism. Volunteer emergency workers enjoy a higher well being than full-time professional workers. Results and their implications for interventions aimed at increasing rescue workers’ quality of life by enhancing psychosocial competences are discussed.     

ENDOGENOUS NETWORKS IN RANDOM POPULATION GAMES

Population learning in dynamic economies traditionally has been studied in contexts where payoff landscapes are smooth. Here, dynamic population games take place over “rugged” landscapes, where agents are uncertain about payoffs from bilateral interactions. Notably, individual payoffs from playing a binary action against everyone else are uniformly distributed over [0, 1]. This random population game leads the population to adapt over time, with agents updating both actions and partners. Agents evaluate payoffs associated to networks thanks to simple statistics of the distributions of payoffs associated to all combinations of actions performed by agents out of the interaction set. Simulations show that: (1) allowing for endogenous networks implies higher average payoff compared to static networks; (2) the statistics used to evaluate payoffs affect convergence to steady-state; and (3) for statistics MIN or MAX, the likelihood of efficient population learning strongly depends on whether agents are change-averse or not in discriminating between options delivering the same expected payoff.     

About the “economic” origin of altruism

The principal aim of this paper is to present an evolutionary model based on a simple inequality system which shows how altruism can increase exchanges of goods and services, in order to study the conditions which can permit the emergence and prevalence of altruistic behaviors. We will show that given certain conditions, and even without considering group selection, kin selection or reciprocal altruism, altruists may have a greater probability of survival than egoists.     

Reversible jump and the label switching problem in hidden Markov models

Reversible jump Markov chain Monte Carlo (RJMCMC) algorithms can be efficiently applied in Bayesian inference for hidden Markov models (HMMs), when the number of latent regimes is unknown. As for finite mixture models, when priors are invariant to the relabelling of the regimes, HMMs are unidentifiable in data fitting, because multiple ways to label the regimes can alternate during the MCMC iterations; this is the so-called label switching problem. HMMs with an unknown number of regimes are considered here and the goal of this paper is the comparison, both applied and theoretical, of five methods used for tackling label switching within a RJMCMC algorithm; they are: post-processing, partial reordering, permutation sampling, sampling from a Markov prior and rejection sampling. The five strategies we compare have been proposed mostly in the literature of finite mixture models and only two of them, i.e. rejection sampling and partial reordering, have been presented in RJMCMC algorithms for HMMs. We consider RJMCMC algorithms in which the parameters are updated by Gibbs sampling and the dimension of the model changes in split-and-merge and birth-and-death moves. Finally, an example illustrates and compares the five different methodologies.     

LIQUIDITY AND INEFFICIENT INVESTMENT

We study consumer liquidity in a general equilibrium model where the friction is the nonpledgeability of future income. Liquidity helps to overcome the absence of a double coincidence of wants. Consumers over‐hoard liquidity and the resulting competitive equilibrium is constrained inefficient. Fiscal policy following a large negative shock can increase ex‐ante welfare. If the government cannot commit, the ex‐post optimal fiscal policy will be too small from an ex‐ante perspective. The model throws light on the holding of foreign reserves in international markets.     

The problem of being a paradigm: the emergence of neural stem cells as example for “Kuhnian” revolution in biology or misconception of the scientific community?

In a thought experiment we want to test how the emergence of adult neural stem cells could constitute an example for a scientific revolution in the sense of Thomas Kuhn. In his major work, “The structure of scientific revolutions, 3rd edn, University of Chicago Press, Chicago” (Kuhn 1996), the philosopher of science, Thomas Kuhn, states that scientific progress is not a cumulative process, but new theories appear by a rather revolutionary sequence of events. Kuhn built his theory on landmark events taken from chemistry and physics, lacking examples from biology. Beginning with Ramon y Cajal’s famous quote, “no new neurons after birth”, from the early years of the twentieth century, and Reynolds and Weiss’s conflicting finding in 1992 of adult neural stem cells giving rise to new neurons, we will test how the finding of neural stem cells in the adult brain matches with Kuhn’s theory. The pivotal problem of defining a paradigm will be our main focus, since the emergence of adult neural stem cells has been acclaimed by the scientific community as the rebuttal of Ramon y Cajal’s paradigm.