Bayesian model comparison for time‐varying parameter VARs with stochastic volatility

We develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and the deviance information criterion (DIC) for time‐varying parameter vector autoregressions (TVP‐VARs), where both the regression coefficients and volatilities are drifting over time. The proposed estimators are based on the integrated likelihood, which are substantially more reliable than alternatives. Using US data, we find overwhelming support for the TVP‐VAR with stochastic volatility compared to a conventional constant coefficients VAR with homoskedastic innovations. Most of the gains, however, appear to have come from allowing for stochastic volatility rather than time variation in the VAR coefficients or contemporaneous relationships. Indeed, according to both criteria, a constant coefficients VAR with stochastic volatility outperforms the more general model with time‐varying parameters.     

In Memory of the Father: Laurence S. Moss

A personal reflection on the life and philosophy of the late Laurence S. Moss (former editor of the AJES) by his son, Joshua Louis Moss. Mixing personal anecdote with a general academic analysis, Moss informally examines his father's intellectual beginnings in the 1960s drawn from the lectures of Ludwig Von Mises, and traces this through his father's development of innovative teaching techniques like the incorporation of stage magic. Moss examines his father's intellectual contrarianism and canonical skepticism as key developmental foundations used to build his father's academic and pedagogical approach. Moss examines his father's interest in expanding economics through a cross‐disciplinary approach utilizing philosophy, history, sociology, and performance studies through his father's innovative examination of points of contact between the principles of stage magic and the principles of economic theory.     

A Bounded Model of Time Variation in Trend Inflation,Nairu and the Phillips Curve

In this paper, we develop a bivariate unobserved components model for inflation and unemployment. The unobserved components are trend inflation and the non‐accelerating inflation rate of unemployment (NAIRU). Our model also incorporates a time‐varying Phillips curve and time‐varying inflation persistence. What sets this paper apart from the existing literature is that we do not use unbounded random walks for the unobserved components, but rather bounded random walks. For instance, NAIRU is assumed to evolve within bounds. Our empirical work shows the importance of bounding. We find that our bounded bivariate model forecasts better than many alternatives, including a version of our model with unbounded unobserved components. Our model also yields sensible estimates of trend inflation, NAIRU, inflation persistence and the slope of the Phillips curve. Copyright © 2015 John Wiley & Sons, Ltd.     

Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments

In this paper we, like several studies in the recent literature, employ a Bayesian approach to estimation and inference in models with endogeneity concerns by imposing weaker prior assumptions than complete excludability. When allowing for instrument imperfection of this type, the model is only partially identified, and as a consequence standard estimates obtained from the Gibbs simulations can be unacceptably imprecise. We thus describe a substantially improved ‘semi‐analytic’ method for calculating parameter marginal posteriors of interest that only require use of the well‐mixing simulations associated with the identifiable model parameters and the form of the conditional prior. Our methods are also applied in an illustrative application involving the impact of body mass index on earnings. Copyright © 2014 John Wiley & Sons, Ltd.     

Introduction: How to Deal with Uncertainty in Population Forecasting?

Demographers can no more be held responsible for inaccuracy in forecasting population 20 years ahead than geologists, meteorologists, or economists when they fail to announce earthquakes, cold winters, or depressions 20 years ahead. What we can be held responsible for is warning one another and our public what the error of our estimates is likely to be.–Nathan Keyfitz (1981)     

Replication of the results in ‘learning about heterogeneity in returns to schooling’

A recent article (Koop and Tobias, 2004 ) proposes a direct way to characterize the extent of heterogeneity in returns to education. They investigate the adequacy of several competing models and conclude that returns to schooling are heterogeneous and are best modelled as a bivariate normal distribution. The results of this replication paper basically agree with the authors. Copyright © 2005 John Wiley & Sons, Ltd.     

The Effects of Government Regulations on the Supply of Pawn Loans: Evidence from 51 Jurisdictions in the U.S.

In the online and printed version of this article, and in the XML file, the corresponding author’s email address is listed incorrectly. Glen Tenney’s correct e-mail address is tenney@gbcnv.edu. The online version of the original article can be found at: