Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package

Raftery, Karny, and Ettler (2010) introduce an estimation technique, which they refer to as Dynamic Model Averaging (DMA). In their application, DMA is used to predict the output strip thickness for a cold rolling mill, where the output is measured with a time delay. Recently, DMA has also shown to be useful in macroeconomic and financial applications. In this paper, we present the eDMA package for DMA estimation implemented in R. The eDMA package is especially suited for practitioners in economics and finance, where typically a large number of predictors are available. Our implementation is up to 133 times faster then a standard implementation using a single-core CPU. Thus, with the help of this package, practitioners are able to perform DMA on a standard PC without resorting to large clusters, which are not easily available to all researchers. We demonstrate the usefulness of this package through simulation experiments and an empirical application using quarterly U.S. inflation data.Comment: 21 pages, 5 figures, 2 table

Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

This paper details particle Markov chain Monte Carlo (PMCMC) techniques for analysis of unobserved component time series models using several economic data sets. PMCMC provides a very compelling, computationally fast and efficient framework for estimation and model comparison. For instance, we estimate a stochastic volatility model with leverage effect and one with Student-t distributed errors. We also model time series characteristics of US inflation rate by considering a heteroskedastic ARFIMA model where heteroskedasticity is specified by means of a Gaussian stochastic volatility process

Particle Gibbs with Ancestor Sampling Methods for Unobserved Component Time Series Models with Heavy Tails, Serial Dependence and Structural Breaks

Particle Gibbs with ancestor sampling (PG-AS) is a new tool in the family of sequential Monte Carlo methods. We apply PG-AS to the challenging class of unobserved component time series models and demonstrate its flexibility under different circumstances. We also combine discrete structural breaks within the unobserved component model framework. We do this by modeling and forecasting time series characteristics of postwar US inflation using a long memory autoregressive fractionally integrated moving average model with stochastic volatility where we allow for structural breaks in the level, long and short memory parameters contemporaneously with breaks in the level, persistence and the conditional volatility of the volatility of inflation

Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

This paper details particle Markov chain Monte Carlo (PMCMC) techniques for analysis of unobserved component time series models using several economic data sets. PMCMC provides a very compelling, computationally fast and efficient framework for estimation and model comparison. For instance, we estimate a stochastic volatility model with leverage effect and one with Student-t distributed errors. We also model time series characteristics of US inflation rate by considering a heteroskedastic ARFIMA model where heteroskedasticity is specified by means of a Gaussian stochastic volatility process

Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package

Raftery, Kárný, and Ettler (2010) introduce an estimation technique, which they refer to as dynamic model averaging (DMA). In their application, DMA is used to predict the output strip thickness for a cold rolling mill, where the output is measured with a time delay. Recently, DMA has also shown to be useful in macroeconomic and financial applications. In this paper, we present the eDMA package for DMA estimation implemented in R. The eDMA package is especially suited for practitioners in economics and finance, where typically a large number of predictors are available. Our implementation is up to 133 times faster than a standard implementation using a single-core CPU. Thus, with the help of this package, practitioners are able to perform DMA on a standard PC without resorting to large computing clusters, which are not easily available to all researchers. We demonstrate the usefulness of this package through simulation experiments and an empirical application using quarterly US inflation data