57 research outputs found
Forecasting inflation using dynamic model averaging
We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period
Characterizing Monetary and Fiscal Policy Rules and Interactions when Commodity Prices Matter.
Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data
Markov Chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models
A dynamic change-point model for detecting the onset of growth in bacteriological infections.
Model Switching and Model Averaging in Time-Varying Parameter Regression Models ∗
Abstract: This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an inflation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results
Inference on Filtered and Smoothed Probabilities in Markov-Switching Autoregressive Models
© 2018. The authors. This document is made available under the CC-BY-NC 4.0 license http://creativecommons.org/licenses/by-nc /4.0/
This document is the submitted version of a published work that appeared in final form in
Journal of Business & Economic Statistics.We derive a statistical theory that provides useful asymptotic approximations to the distributions of the single inferences of filtered and smoothed probabilities, derived from time series characterized by Markov-switching dynamics. We show that the uncertainty in these probabilities diminishes when the states are separated, the variance of the shocks is low, and the time series or the regimes are persistent. As empirical illustrations of our approach, we analyze the U.S. GDP growth rates and the U.S. real interest rates. For both models, we illustrate the usefulness of the confidence intervals when identifying the business cycle phases and the interest rate regimes
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