We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior...

We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior...

This paper examines the issue of variable selection in linear regression modeling, where there is a potentially large amount of possible covariates and economic theory offers insufficient guidance on how to select the appropriate subset. In this context, Bayesian Model Averaging presents a...

We examine the issue of variable selection in linear regression have a potentially large amount of possible covariates and economic theory offers insufficient guidance on how to select the Model Averaging presents uncertainty. Our main interest here is the effect of the prior on the results,...

Prais (1958) showed that the CPI computed by statistical agencies can be interpreted as a weighed average of household price indexes, the weight of each household determined by its total expenditures. We decompose the difference between the standard CPI and a democratically weighed index (i.e.,...

This paper extends the analogy, previously established by Learner (1978a), between a Bayesian inference problem and an economics allocation problem to show that posterior modes can be interpreted as optimal outcomes of a bargaining game. This bargaining game, over a parameter value, is played...

This paper discusses Bayesian inference for stochastic volatility models based on continuous superpositions of Ornstein-Uhlenbeck processes. These processes represent an alternative to the previously considered discrete superpositions. An interesting class of continuous superpositions is defined...

This paper discusses Bayesian inference for stochastic volatility models based on continuous superpositions of Ornstein-Uhlenbeck processes. These processes represent an alternative to the previously considered discrete superpositions. An interesting class of continuous superpositions is defined...