The Making of Optimal and Consistent Policy: An Analytical Framework for Monetary Models

This paper shows that optimal policy and consistent policy outcomes require the use of control-theory and game-theory solution techniques. While optimal policy and consistent policy often produce different outcomes even in a one-period model, we analyze consistent policy and its outcome in a simple model, finding that the cause of the inconsistency with optimal policy traces to inconsistent targets in the social loss function. As a result, the central bank should adopt a loss function that differs from the social loss function. Carefully designing the central bank s loss function with consistent targets can harmonize optimal and consistent policy. This desirable result emerges from two observations. First, the social loss function reflects a normative process that does not necessarily prove consistent with the structure of the microeconomy. Thus, the social loss function cannot serve as a direct loss function for the central bank. Second, an optimal loss function for the central bank must depend on the structure of that microeconomy. In addition, this paper shows that control theory provides a benchmark for institution design in a game-theoretical framework.

Bayesian models for the analysis of genetic structure when populations are correlated

Motivation: Population allele frequencies are correlated when populations have a shared history or when they exchange genes. Unfortunately, most models for allele frequency and inference about population structure ignore this correlation. Recent analytical results show that among populations, correlations can be very high, which could affect estimates of population genetic structure. In this study, we propose a mixture beta model to characterize the allele frequency distribution among populations. This formulation incorporates the correlation among populations as well as extending the model to data with different clusters of populations. Results: Using simulated data, we show that in general, the mixture model provides a good approximation of the among-population allele frequency distribution and a good estimate of correlation among populations. Results from fitting the mixture model to a dataset of genotypes at 377 autosomal microsatellite loci from human populations indicate high correlation among populations, which may not be appropriate to neglect. Traditional measures of population structure tend to over-estimate the amount of genetic differentiation when correlation is neglected. Inference is performed in a Bayesian framework.