A dynamic Tobit model with Time-varying parameters is proposed for the daily reaction function of the Open Market Desk of the US Federal Reserve. Such a model offers a more realistic depiction of the Desk's behavior than those of past contributions in the literature as it allows for both...

We develop extensions of the variance-ratio statistic for testing the hypothesis a time series is uncorrelated and investigate their finite-sample performance. The tests employ an estimator of the asymptotic covariance matrix of the sample autocorrelations that is consistent under the null for...

This paper studies a Monte Carlo algorithm for computing distributions of state variables when the underlying model is a Markov process. It is shown that the $L_1$ error of the estimator always converges to zero with probability one, and often at a parametric rate. A related technique for...

This paper examines evidence of long- and short-run co-movement in Canadian sectoral output data. Our framework builds on a vector-error-correction representation that allows to test for and compute full-information maximum-likelihood estimates of models with codependent cycle restrictions. We...

This paper utilizes two different classification techniques to explore issues in the development of an early warning system for sovereign default. Specifically, the paper develops K-means clustering and logit models to illustrate how the optimal choice of parameters, such as assignment rule of...

The shape of the likelihood of several recently developed econometric models is often non-elliptical. Learning this shape using Gibbs sampling is discussed in this paper. A systematic analysis using graphical and computational methods is presented. Examples of the models considered in this paper...

We analyze the issue of the impact of multiple breaks on monetary neutrality results, using a long annual international data set. We empirically verify whether neutrality propositions remain addressable (and if so, whether they hold or not), when unit root tests are carried out allowing for...

The fast increase in computing power makes it possible to rapidly generate synthetic high frequency financial time series by Monte Carlo with any desired distribution of the increments and of the waiting times between increments, even for sets of securities as large as those traded on a whole...