Abstract
The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance.
Highlights
The third section contains the brief note about the theory of Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model
The seventh section describes the implementation of the Diebold-Mariano test in such a way that the user can quickly compare forecasts from different models obtained with fDMA
The very first motivation behind the package described in this paper is to provide an easy and efficient tool for practitioners and/or researchers dealing with DMA
Summary
FDMA still allows the user to use modifications of DMA which eDMA lacks, like for example Occam’s window, Google data, other method of updating variance in the state space equation than the recursive moment estimator, etc. FDMA allows for information-theoretic averaging (a non-Bayesian method) to be performed like DMA, and some other methods In this way, the user can compute DMA, DMS and Median Probability Model, and compare the outcomes with some alternative methods. The user can compute DMA, DMS and Median Probability Model, and compare the outcomes with some alternative methods Such a feature is important in practical applications. MHTrajectoryR [34] uses BMS in logistic regression for the detection of adverse drug reactions
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