Seasonal adjustment procedures attempt to estimate the sample realizations of an unobservable economic time series in the presence of both seasonal factors and irregular factors. In this paper we consider a factor which has not been considered explicitly in previous treatments of seasonal...

Some public goods are provided entirely with private contributions, others with a mixture of public and private funding, and still others are entirely publicly funded. To explain this variation, a model of dual provision is developed that endogenizes public and private funding. Members of the...

We study the impact of two dimensions of trust, namely trust in business elites and trust in government, on policy preferences. Using a randomized online survey, we find that our two treatments are effective in changing trust in Major Companies and in Courts/Government. In contrast to previous...

We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/√T. This local embedding makes...

Different beliefs about how fair social competition is and what determines income inequality, influence the redistributive policy chosen democratically in a society. But the composition of income in the first place depends on equilibrium tax policies. If a society believes that individual effort...

The current debate in Western Europe centers on the relationship between economic and political integration. To address this problem, we construct a simple general equilibrium model in which the returns to trading are directly affected by the availability of a public good. In our model,...

An appropriate metric for the success of an algorithm to forecast the variance of the rate of return on a capital asset could be the incremental profit from substituting it for the next best alternative. We propose a framework to assess incremental profits for competing algorithms to forecast...