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.

Cross-Country Evidence on Output Growth Volatility: Nonstationary Variance and GARCH Models

This paper revisits the issue of conditional volatility in real GDP growth rates for Canada, Japan, the United Kingdom, and the United States. Previous studies find high persistence in the volatility. This paper shows that this finding largely reflects a nonstationary variance. Output growth in the four countries became noticeably less volatile over the past few decades. In this paper, we employ the modified ICSS algorithm to detect structural change in the unconditional variance of output growth. One structural break exists in each of the four countries. We then use generalized autoregressive conditional heteroskedasticity (GARCH) specifications modeling output growth and its volatility with and without the break in volatility. The evidence shows that the time-varying variance falls sharply in Canada, Japan, and the U.K. and disappears in the U.S., excess kurtosis vanishes in Canada, Japan, and the U.S. and drops substantially in the U.K., once we incorporate the break in the variance equation of output for the four countries. That is, the integrated GARCH (IGARCH) effect proves spurious and the GARCH model demonstrates misspecification, if researchers neglect a nonstationary unconditional variance.

The Secret Life of Mundane Transaction Costs

Transaction costs, one often hears, are the economic equivalent of friction in physical systems. Like physicists, economists can sometimes neglect friction in formulating theories; but like engineers, they can never neglect friction in studying how the system actually does let alone should work. Interestingly, however, the present-day economics of organization also ignores friction. That is, almost single-mindedly, the literature analyzes transactions from the point of view of misaligned incentives and (especially) transaction-specific assets. The costs involved are certainly costs of running the economic system in some sense, but they are not obviously frictions. Stories about frictions in trade are not nearly as intriguing as stories about guileful trading partners and expensive assets placed at risk. But I will argue that these seemingly dull categories of cost what Baldwin and Clark (2003) call mundane transaction costs actually have a secret life. They are at least as important as, and quite probably far more important than, the more glamorous costs of asset specificity in explaining the partition between firm and market. These costs also have a secret life in another sense: they have a secret life cycle. I will argue that these mundane transaction costs provide much better material for helping us understanding how the boundaries among firms, markets, and hybrid forms change over time.