Forecasting Equity Premia Using Bayesian Dynamic Model Averaging
نویسندگان
چکیده
منابع مشابه
Forecasting in Dynamic Factor Models Using Bayesian Model Averaging
This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justi cations for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space de ned by all possible models...
متن کاملPredicting waste generation using Bayesian model averaging
A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate th...
متن کاملBayesian Model Averaging and Forecasting
This paper focuses on the problem of variable selection in linear regression models. I briefly review the method of Bayesian model averaging, which has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. Some of the literature is discussed with particular emphasis on forecasting in economics. The role of the p...
متن کاملForecasting Ination Using Dynamic Model Averaging
We forecast quarterly US ination based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and...
متن کاملProbabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging
Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2014
ISSN: 1556-5068
DOI: 10.2139/ssrn.2502356