# Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R
## 2 Economic Motivation  
Now, the economic motivation for the use of the implemented methods is provided. Indeed, DMA joins a few features of econometric modelling together. First of all, the final forecast is produced out of several regression models by model averaging. Secondly, the method is a Bayesian one, i.e., probabilities are interpreted in a degree-of-belief way. Indeed, for example the DMA forecast for time \(t\) is made on the basis of data up to time \(t-1\) only. Moreover, the gain of a new data results in a direct parameters' updating. Therefore, in DMA both regression coefficients and weights ascribed to the models vary in time. Bayesian methods are not the mainstream of modern econometrics. However, these methods are gaining more and more interest recently. There are various reason for this. First of all, one can link this to the increasing amount of data that a potential researcher has to cope with in his or her research. Due to the technological progress usually one is faced with the case of many potential explanatory variables. Despite the fact that most of them are probably unimportant, the researcher usually does not know which ones should be rejected. Of course, up to some point conventional methods can still be used. But unfortunately precise estimates of parameters usually cannot be done due to the lack of enough information. The simplest example is when the number of explanatory variables is greater than the number of observations in the time-series. For example, even in the case of a linear regression the standard ordinary least squares estimation a singular matrix would emerge, resulting in the impossibility of taking its inverse. In the Bayesian framework, still a meaningful formula is derived. It also seems that the Bayesian methods deal better with over-parametrization and over-fitting issues. Some kind of conventional methods to deal with omitted variables bias, like hypothesis testing to select the \"true\" model out of many possible is also problematic in case of many variables due to computational issues [35]. All this results in an increasing concern about the Bayesian methods in new researches, which is thoroughly described in various reviews and monographs [36, 37, 38, 39, 40, 41]. In this place it is worth to mention that various approaches can be found in recent trends in forecasting. Narrowing, as an example, to oil price, forecasting methods can usually be classified into time-series models, structural models and some other methods like machine learning, neural networks, etc. This is well described in the recent extensive surveys [42, 43, 44, 45, 46]. Generally, time-series models are focused on modelling rather volatility than the spot prices. Structural models by definition include causal relations, but they usually have a good predictive power in certain periods and very poor in others. Also, the other methods, basing on wavelet decomposition, neural networks, etc. usually omit influence of other factor and focus on a single time-series [47, 48, 49, 50, 51]. These makes DMA an interesting method for a practitioner. The next aspect of DMA is that it allows for regression coefficients to be time-varying. Indeed, in the presence of both slow and rapid (structural breaks) changes in economy, such a property of the econometric model is very desirable. Of course, such an approach also exists in conventional methodology, for example, as recursive or rolling window regressions. But since the seminal paper by [PERSON] [62] more sophisticated methods are usually used. They allow to incorporate uncertainty of variables' estimates in the presence of noise in the measurement, etc. As a result more accurate estimates are obtained [63]. Another important feature of DMA is that the final forecast comes from forecast combination. As already mentioned in many economic problems the researcher is not sure which variables should be included in the model. In other words, narrowing for instance to linear regression models, the research has to cope with uncertainty about the model.