Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights (original) (raw)
Related papers
Predictive Gains from Forecast Combinations Using Time Varying Model Weights
SSRN Electronic Journal, 2000
Several frequentist and Bayesian model averaging schemes, including a new one that simultaneously allows for parameter uncertainty, model uncertainty and time varying model weights, are compared in terms of forecast accuracy over a set of simulation experiments. Artificial data are generated, characterized by low predictability, structural instability, and fat tails, which is typical for many financial-economic time series. Sensitivity of results with respect to misspecification of the number of included predictors and the number of included models is explored. Given the set up of our experiments, time varying model weight schemes outperform other averaging schemes in terms of predictive gains both when the correlation among individual forecasts is low and the underlying data generating process is subject to structural locations shifts. In an empirical application using returns on the S&P 500 index, time varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs.
Bayesian Model Averaging and Forecasting
2011
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 prior assumptions in these procedures is highlighted, and some recommendations for applied users are given.
The Bayesian model of combined macroeconomic forecasts
2012
There is a wide variety of institutions which periodically publish forecasts about differentmacroeconomic indexes. Because each source has its own criteria and models, each onecommits different errors. Regarding this data, it is reasonable to create a parasitic modelwhich uses various forecasts as input and combine them. In this way, more independentinformation is obtained and the error of prediction is reduced.This thesis proposes the Bayesian approach to develop the distribution of errors of fore-casts, including the information about past errors of each source and considering variousinfluential factors. Afterwards, there is an evaluation of combined forecast simulations in or-der to acquire the distribution of the future macroeconomic indexes and verify the accuracyof the model.
Forecasting Using Bayesian and Information-Theoretic Model Averaging
Journal of Business & Economic Statistics, 2008
In recent years there has been increasing interest in forecasting methods that utilise large data sets, driven partly by the recognition that policymaking institutions need to process large quantities of information. Factor analysis is a popular way of doing this. Forecast combination is another, and it is on this that we concentrate. Bayesian model averaging methods have been widely employed in this area, but a neglected alternative approach employed in this paper uses information theoretic based weights. We consider the use of model averaging in forecasting UK inflation with a large data set from this perspective. We find that an information theoretic model averaging scheme can be a powerful alternative both to the more widely used Bayesian model averaging scheme and to factor models.
8. Bayesian 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 prior assumptions in these procedures is highlighted, and some recommendations for applied users are given.
2006
Similar to other Central Banks, the BCRA publishes monthly a REM that summaries the forecasts and projections of a group of economic analysts and consultants who volunteer to participate in the program. The BCRA publishes only the median, and the standard deviation of the sample received. The logic for using these statistics is that all participants are to be treated equally. Under the assumption that some forecasters have better underlying models than others, one might be able to improve the accuracy of the aggregate forecast by giving greater priority to those who have historically predicted better. The BCRA does not have access to the models used to make the predictions, only the forecasts are provided. An averaging method that puts higher weights on the predictions of those forecasters who have done best in the past should be able to produce a better aggregate forecast. The problem is how to determine these weights. In this paper, we develop a Bayesian averaging method that can ...
Combining Forecasts from Linear and Nonlinear Models Using Sophisticated Approaches
International Journal of Economics and Finance, 2015
This paper aims at improving the prediction accuracy through using combining forecasts approaches. In forecast combination, the crucial issue is the selection of the weights to be assigned to each model. In addition to traditional methods, we propose, also, two sophisticated approaches. These suggested methods are modified Bayesian Moving Average (BMA) and Extended Time-varying coefficient (ETVC). The first technique is based on merging the traditional BMA with other frequentist combination schemes to avoid the subjective prior inside the traditional Bayesian technique. The suggested ETVC approach provides consistent time-varying parameters even if there are some measurement errors, omitted variables bias and if the true functional form is unknown. Concerning the included models, we consider both linear and nonlinear models in order to calculate the forecasts of quarterly Egyptian CPI inflation. We find that our proposed scheme ETVC is superior to the best model and all other static...