Bayesian inference for nonlinear structural time series models

We consider efficient methods for likelihood inference applied to structural models. In particular, we introduce a particle filter method which concentrates upon disturbances in the Markov state of the approximating solution to the structural model. A particular feature of such models is that the conditional distribution of interest for the disturbances is often multimodal. We provide a fast and effective method for approximating such distributions. We estimate a neoclassical growth model using this approach. An asset pricing model with persistent habits is also considered. The methodology we employ allows many fewer particles to be used than alternative procedures for a given precision.     

The legacy of Paul Volcker

Business Economics - Paul Volcker was a model public servant, not only his service as Fed Chair, but also in his work on international economic policy at the Treasury, and in his service in many...     

When is an aggregate of a time series efficiently forecast by its past?

The paper gives necessary and sufficient conditions for a linear combination of the elements of a vector time series to be efficiently forecast by its own past values, where by past we mean both the infinite and also the finite past. We observe that the problem is equivalent to testing a single element of a vector for exogeneity, this observation also suggesting a method for empirically testing the derived conditions.     

On some properties of Markov chain Monte Carlo simulation methods based on the particle filter

Andrieu et al. (2010) prove that Markov chain Monte Carlo samplers still converge to the correct posterior distribution of the model parameters when the likelihood estimated by the particle filter (with a finite number of particles) is used instead of the likelihood. A critical issue for performance is the choice of the number of particles. We add the following contributions. First, we provide analytically derived, practical guidelines on the optimal number of particles to use. Second, we show that a fully adapted auxiliary particle filter is unbiased and can drastically decrease computing time compared to a standard particle filter. Third, we introduce a new estimator of the likelihood based on the output of the auxiliary particle filter and use the framework of Del Moral (2004) to provide a direct proof of the unbiasedness of the estimator. Fourth, we show that the results in the article apply more generally to Markov chain Monte Carlo sampling schemes with the likelihood estimated in an unbiased manner.     

Dissecting the Random Component of Utility

We illustrate and discuss several general issues associated with the random component of utility, or more generally unobserved variability. We posit a general conceptual framework that suggests a variance components view as an appropriate structure for unobserved variability. This framework suggests that unobserved heterogeneity is only one component of unobserved variability; hence, a more general view is required. We review a considerable amount of empirical research that suggests that random components are unlikely to be independent of systematic components, and random component variances are unlikely to be constant between or within individuals, time periods, locations, etc. We also review evidence that random components are functions of (elements of) systematic components. The latter suggests considerable caution in the use and interpretation of complex choice model specifications, in particular recently introduced forms of random parameter models that purport to estimate distributions of preference parameters. Several areas for future research are identified and discussed.     

Environmental Barriers to Trade: The Case of Endangered Sea Turtles

In April of 1998, the World Trade Organization pronounced the US Sea Turtle Conservation Act in violation of the GATT. This paper presents a stylized Heckscher–Ohlin–Samuelson model in which the fatal entrapment of sea turtles in nets of the shrimping industry is a negative externality that reduces global utility. Three trade equilibria are simulated: free trade, trade ban, and free trade with subsidization. With free trade, a transfer of abatement capital from the North to the South results in a Pareto improvement upon the trade ban. The simulations indicate that a cooperative outcome which obtains global efficiency may be feasible but is improbable.