# Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R
## 2 Economic Motivation
Of course, one approach is to use some methods to uncover the \"true\" model out of all considered. Actually, one of the assumptions of DMA is that such a \"true\" model exists, so with upcoming new data its weight should increase. But the forecast in DMA is formulated as a weighted average from the predictions of all possible models concerned. In this way DMA incorporates a model averaging technique. Since the seminal paper by [PERSON] and [PERSON] [64] it is know that forecast combination can be beneficial. [PERSON] and [PERSON] [64] provided an example when forecast combination with suitable setting the weights for two models can result in smaller Mean Square Error than those of two separate models. Yet, since then in many cases it was found that not only on a theoretical ground model averaging can be beneficial over model selection. Just in case of practical applications, i.e., seeking a method to produce smaller errors, to better fit the historical data, and/or to produce more accurate forecast, it was found that model averaging can be useful for practitioners [65, 66, 67, 68, 69, 70, 71]. Indeed, model averaging is a popular technique not only in the Bayesian setting. Various approach are developed, basing on different underlying theories [72, 73, 74, 75, 76, 77, 78, 79, 80]. Recently, a very detailed surveys in model averaging in economics has been written [81, 82, 83], to which the interested reader is referred. In economics, model averaging within the Bayesian framework was well adapted in studying the economic growth [84]. However, the examples for fDMA in this paper are taken from the oil market, i.e., the spot oil price is examined. Indeed, this topic was studied, for example, by [PERSON] [85] and [PERSON] [86]. Therefore, the reader interested in financial and economical perspective is referred to these papers. Herein, just fDMA package is described, and oil market data serve just as an illustrative examples how to use the package - not how to do a good _economic_ research. In particular, the data set is greatly reduced in order to execute the examples in a few seconds. Therefore, in Table 1 a collection of several potential oil price drivers is presented. These relationships, their strengths and direction are also found to be time-varying. It has to be stressed that variables in this table are only potential oil price drivers. Although in some cases direct causal relationship can be found from literature, it is enough just to have a tentative supposition that given variable might be somehow linked with oil price. In this way the list of potential drivers is reasonable, but still uncertainty about them exists. \begin{table}
\begin{tabular}{l l} \hline \hline
**Driver** & **Reason to Treat as a Potential Driver and Source** \\ \hline \multirow{3}{*}{Interest rate} & Higher rate results in higher price of a non-renewable resource (Hotelling’s rule). Higher rate results in lower price of a commodity because of the cost of holding inventory [87]. \\ \cline{2-3}  & Increase in demand results in price increase. Increase in supply – in price decrease. This is sometimes known as the law of supply and demand [88, 89, 90, 91, 92, 93, 94]. \\ \hline \multirow{3}{*}{Exchange rates} & The price of oil in the domestic currency changes as the exchange rate changes. This has different implications for oil-exporting and oil-importing countries [95, 96, 97, 98, 99, 100, 101, 102]. \\ \cline{1-1}  & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Possible drivers of spot oil price. As mentioned, for the most detailed economic analysis of the literature the reader is referred to the already mentioned papers. Moreover, it is worth to notice that there exist a few, both up-to-date and extensive, reviews of various forecasting techniques of oil price [42; 43; 44; 45; 46]. Also, it is interesting to consider how the Bayesian approach in general is used in forecasting oil price.