A theory of forward induction in finitely repeated games

A forward induction solution for finitely repeated games with complete information is developed. This notion is motivated in terms of its implications on the way deviations affect the opponents' expectations about the future behavior of the deviating player. We argue that the inability of the notion of perfect equilibrium to take account of forward induction is a key factor responsible for a number of difficulties encountered in the use of perfect equilibria in repeated games. It is then shown that the solution proposed in this paper remedies some of these problems in the study of three important classes of repeated games: (i) finitely repeated coordination games; (ii) repeated games where one long-term player plays a sequence of short-term players; (iii) repeated battle of the sexes games.     

Public expenditures on education, human capital and growth in Canada: An OLG model analysis

Much of the current debate in ageing countries focuses on whether governments should increase investments in human capital. We address this issue by simulating the effects of additional education spending using an overlapping-generations model applied to Canada. In the context of population ageing, the results indicate that how the policy is funded has powerful impacts on the targeted outcomes. Higher education incentives may increase the rate of human capital accumulation and mitigate the negative effects of slowing labour force growth. However, the impact depends on the distortions implied by alternative tax instruments and the efficiency of public expenditures on education.     

Decision Makers as Statisticians: Diversity,Ambiguity, and Learning

I study individuals who use frequentist models to draw uniform inferences from independent and identically distributed data. The main contribution of this paper is to show that distinct models may be consistent with empirical evidence, even in the limit when data increases without bound. Decision makers may then hold different beliefs and interpret their environment differently even though they know each other's model and base their inferences on the same evidence. The behavior modeled here is that of rational individuals confronting an environment in which learning is hard, rather than individuals beset by cognitive limitations or behavioral biases.     

Multiplicative bias correction for discrete kernels

In this paper, we prove that two multiplicative bias correction techniques (MBC) can be applied for discrete kernels in the context of probability mass function estimation. First, some properties of the MBC discrete kernel estimators (bias, variance and mean integrated squared error) are investigated. Second, the popular cross-validation technique is adapted for bandwidth selection. Finally, a simulation study and a real data application for discrete data illustrate the performance of the MBC estimators based on dirac discrete uniform and triangular discrete kernels.     

Bayesian Approach in Nonparametric Count Regression with Binomial Kernel

Recently, Kokonendji et al. have adapted the well-known Nadaraya–Watson kernel estimator for estimating the count function m in the context of nonparametric discrete regression. The authors have also investigated the bandwidth selection using the cross-validation method. In this article, we propose a Bayesian approach in the context of nonparametric count regression for estimating the bandwidth and the variance of the model error, which has not been estimated in Kokonendji et al. The model error is considered as Gaussian with mean of zero and a variance of σ². The Bayes estimates cannot be obtained in closed form and then, we use the well-known Markov chain Monte Carlo (MCMC) technique to compute the Bayes estimates under the squared errors loss function. The performance of this proposed approach and the cross-validation method are compared through simulation and real count data.     

Inter‐temporal and Inter‐industry Effects of Population Ageing: A General Equilibrium Assessment for Canada

Abstract. This paper examines the inter‐industry and labour market occupational effects of future demographic changes in Canada, using a computable overlapping‐generations model. In addition to the impact of slower labour force growth, the model captures the shift in sectoral composition of final demand of older individuals. The simulation results indicate that the growth in real GDP per capita could decline by nearly one percentage point between 2006 and 2050. The results also suggest that the equilibrium unemployment rate is likely to decline by more than two percentage points in the long run. However, the impact varies significantly at the occupational level.     

East-European immigrants responding to the recession in Britain: is there a trade-off between unemployment and over-qualification?

Most studies demonstrating the vulnerability of labour migrants following the recession have focused on unemployment. This article examines how the labour market performance of East-European workers in the U.K. has been affected by the recession by focussing on four possible employment outcomes: unemployment, self-employment, over-qualification and part-time jobs. By showing the relatively low rates of unemployment amongst East-European migrants, which have become even lower following the recession, it argues that the vulnerability of immigrants in periods of economic downturn cannot always be solely measured in higher rates of immigrant unemployment. Labour migrants may be prompted to take jobs (any jobs) below their skills and qualifications, thus suggesting a ‘trade-off’ between unemployment and over-qualification.     

New sensitivity analysis subordinated to a contrast

ABSTRACT

In a model of the form Y = h(X₁, …, X_d) where the goal is to estimate a parameter of the probability distribution of Y, we define new sensitivity indices which quantify the importance of each variable X_i with respect to this parameter of interest. The aim of this paper is to define goal oriented sensitivity indices and we will show that Sobol indices are sensitivity indices associated to a particular characteristic of the distribution Y. We name the framework we present as Goal Oriented Sensitivity Analysis (GOSA).