The debt crisis in the euro area led to obvious changes in the structure of euro area bond markets. To model the process of disintegration that has taken place as a result of this crisis, this analysis uses a dynamic factor model with time-varying loadings and two factors. While some core...

Exchange rate arrangements and trade are inherently connected. Exchange rate volatility has a significant impact on trade volumes, and trading partners thus could affect each other’s exchange rate regime choice. This spatial effect among trading partners has been overlooked in empirical...

A trade-off exists between the Gelfand and Dey (1994) and Chib (1995) methods to calculate the marginal likelihood in Bayesian estimation. Using the Markov Chain Monte Carlo method, we demonstrate that the performance of the two methods is fairly close.

There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. An economic argument is that in an attempt to maintain consumption in the...

This paper characterizes the co-movements in commodity prices with a dynamic latent factor model that decomposes commodity returns into global, sectoral, and idiosyncratic components. The results indicate that global and sectoral factors are important sources of co-movements in commodity...

We propose to model endogeneity bias using prior distributions of moment conditions. The estimator can be obtained both as a method-of-moments estimator and in a Ridge penalized regression framework. We show the estimator’s relation to a Bayesian estimator.

In this note, we provide the application of HCCME-type refinements to nonlinear GMM models with Bayesian interpretations.

Using GARCH models for density prediction of stock index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between qualities of whole density forecasts, whereas the Bayesian approach exhibits significantly better left-tail forecast...

There have been numerous applications of partial observability bivariate probit models. These models identify determinants of individual discrete outcomes when all that is observed are collective outcomes and individual characteristics. Numerical difficulties with their likelihood functions are...

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.