Working papers results

We characterize consistent random choice rules in terms of the optimality of the support. We then proceed to study stochastic choice in a consumer theory setting. We prove a law of demand for stochastic choice. We then move to a temporal setting where we characterize the softmax decision criterion.

The purpose of this note is to discuss the relation between model uncertainty in risk analysis and decision theory.

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-Switching three-pass regression filter (MS-3PRF), is suitable for datasets with large cross-sectional dimensions since estimation and inference are straightforward, as opposed to existing regime-switching factor models, where computational complexity limits applicability to few variables. In a Monte- Carlo experiment, we study the finite sample properties of the MS-3PRF and find that it performs favorably compared with alternative modelling approaches whenever there is structural instability in factor loadings. As empirical applications, we consider forecasting economic activity and bilateral exchange rates, finding that the MS-3PRF approach is competitive in both cases.

We study whether providing information about immigrants affects people's attitude towards them. First, we use a large representative cross-country experiment to show that, when people are told the share of immigrants in their country, they become less likely to state that there are too many of them. Then, we conduct two online experiments in the U.S., where we provide half of the participants with five statistics about immigration, before evaluating their attitude towards immigrants with self-reported and behavioral measures. This more comprehensive intervention improves people's attitude towards existing immigrants, although it does not change people's policy preferences regarding immigration. Republicans become more willing to increase legal immigration after receiving the information treatment. Finally, we also measure the same self-reported policy preferences, attitudes, and beliefs in a four-week follow-up, and we show that the treatment effects persist.

We study a decision maker characterized by two binary relations. The first reflects his judgments about well-being, his mental preferences. The second describes the decision maker's choice behavior, his behavioral preferences, the ones that govern choice (see Rubin- stein and Salant, 2008a,b). Specififically, in the context of decision making under uncertainty, we propose axioms that may describe the rationality of these two relations. These axioms allow a joint representation by a single set of probabilities and a single utility function. It is mentally rational to prefer f over g if and only if the expected utility of f is at least as high as that of g for all probabilities in the set. It is behaviorally rationalizable to choose f over g if and only if the expected utility of f is at least as high as that of g for some probability in the set. In other words, mental and behavioral preferences admit, respectively, a representation a la Bewley (2002) and a la Lehrer and Teper (2011). Our results also provide foundation for a decision analysis procedure called robust ordinal regression and proposed by Greco, Mousseau, and Slowinski (2008).

We consider an uncertainty averse, sophisticated decision maker facing a recurrent decision problem where information is generated endogenously. In this context, we study self-confirming strategies as the outcomes of a process of active experimentation. We provide inter alia a learning foundation for self-confirming equilibrium with model uncertainty (Battigalli et al., 2015). We also argue that ambiguity aversion tends to stifle experimentation, increasing the likelihood that decision maker get stuck into suboptimal certainty traps.

This paper addresses theoretically the question whether culture has an effect on economic performance in team production, and which would be the optimal team culture. The members of the team are guided both by economic incentives and by personal norms, weighed according to their prevailing level of materialism. We assume that personal norms evolve following a dynamics driven by a combination of psychological mechanisms such as consistency and conformism. The different vectors of materialism, consistency and conformity shared by the group result in a continuum of cultures with different combinations of individualism and collectivism. Our main results show how team culture turns out to be a fundamental determinant for group performance. When income distribution is not completely egalitarian or the members of the team display heterogeneous levels of skills, culture matters in the sense that there exists an optimal culture that maximizes team production and its characteristics depend on the specific distributions of income and skills. A higher average productivity or a more inegalitarian dispersion of remunerations requires a more collectivist culture. And a higher dispersion of individual productivities requires a more individualist culture.

We use impulse response functions computed from linear and nonlinear, Markov switching models to investigate the strength of four alternative contagion channels. These are the flight-to-quality, flight-to-liquidity, risk premium, and correlated information channels. We study the differences among estimates and impulse response functions across linear and nonlinear models to identify and measure cross-asset contagion. An application to weekly Eurozone data for a 2007-2014 sample, reveals that a two-state Markov switching model shows accurately estimated but economically weak contagion effects in a crisis regime. These results are mainly explained by a flight-to-quality channel. Furthermore, we extend our analysis the analysis to investigate whether European market may be subject to contagion when exposed to external shocks, such as those originated from the US subprime crisis.

We propose a Bayesian panel model for mixed frequency data whose parameters can change over time according to a Markov process. Our model allows for both structural instability and random effects. We develop a proper Markov Chain Monte Carlo algorithm for sampling from the joint posterior distribution of the model parameters and test its properties in simulation experiments. We use the model to study the effects of macroeconomic uncertainty and financial uncertainty on a set of variables in a multi-country context including the US, several European countries and Japan. We find that for most of the variables financial uncertainty dominates macroeconomic uncertainty. Furthermore, we show that uncertainty coefficients differ if the economy is in a contraction regime or in an expansion regime.

Recent cases in the US (Meritor, Eisai) and in the EU (Intel ) have revived the debate on the use of price-cost tests in loyalty discount cases. We draw on existing recent economic theories of exclusion and develop new formal material to argue that economics alone does not justify applying a price-cost test to predation but not to loyalty discounts. Still, the latter contain features (they reference rivals and allow to discriminate across buyers and/or units bought) that have a higher exclusionary potential than the former, and this may well warrant closer scrutiny and more severe treatment from antitrust agencies and courts.