The long-run Fisher effect: can it be tested?

Empirical support for the long-run Fisher effect, a hypothesis that a permanent change in inflation leads to an equal change in the nominal interest rate, has been hard to come by. This paper provides a plausible explanation of why past studies have been unable to find support for the long-run Fisher effect. This paper argues that the necessary permanent change to the inflation rate following a monetary shock has not occurred in the industrialized countries of Australia, Austria, Belgium, Canada, Denmark, France, Germany, Greece, Ireland, Italy, Japan, the Netherlands, Norway, Sweden, Switzerland, the United Kingdom, and the United States. Instead, this paper shows that inflation in these countries follows a mean-reverting, fractionally integrated, long-memory process, not the nonstationary inflation process that is integrated of order one or larger found in previous studies of the Fisher effect. Applying a bivariate maximum likelihood estimator to a fractionally integrated model of inflation and the nominal interest rate, the inflation rate in all seventeen countries is found to be a highly persistent, fractionally integrated process with a positive differencing parameter significantly less than one. Hence, in the long run, inflation in these countries will be unaffected by a monetary shock, and a test of the long-run Fisher effect will be invalid and uninformative as to the truthfulness of the long-run Fisher effect hypothesis.

Bayesian Semiparametric Stochastic Volatility Modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility modelsClassification-JEL:

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and kurtosis of the distribution while continuing to model the dynamics of volatility with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. We present a Markov chain Monte Carlo sampling approach to estimation with theoretical and computational issues for simulation from the posterior predictive distributions. The new model is assessed based on simulation evidence, an empirical example, and comparison to parametric models.Econometric models ; Stochastic analysis

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. The new model is assessed based on simulation evidence, an empirical example, and comparison to parametric models.Dirichlet process mixture, MCMC, block sampler

Representing disease courses: An application of the Neurological Disease Ontology to Multiple Sclerosis Typology

The Neurological Disease Ontology (ND) is being developed to provide a comprehensive framework for the representation of neurological diseases (Diehl et al., 2013). ND utilizes the model established by the Ontology for General Medical Science (OGMS) for the representation of entities in medicine and disease (Scheuermann et al., 2009). The goal of ND is to include information for each disease concerning its molecular, genetic, and environmental origins, the processes involved in its etiology and realization, as well as its clinical presentation including signs and symptoms

Tree identification and age project

The purpose of the Tree Identification and Age Project is to use authentic learning activities to extend the current curriculum to include learning that takes place at high levels of cognition. The methods employed integrate higher-order thinking into learning through a hands-on, problem-based approach to authentic scientific investigation. Using a problem-based approach, the learners apply knowledge and skills to solve real problems. The process involves focusing on the problem, identifying relative information, categorizing, critically analyzing, synthesizing that information and effectively communicating the results