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Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Faculty of Economic Sciences, University of Warsaw, 00-241 Warsaw, Poland
Signals 2020, 1(1), 47-99; https://doi.org/10.3390/signals1010004
Received: 10 May 2020 / Revised: 25 June 2020 / Accepted: 29 June 2020 / Published: 6 July 2020
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. View Full-Text
Keywords: Bayesian Model Averaging; Dynamic Model Averaging; Dynamic Model Selection; forecast combination; R Bayesian Model Averaging; Dynamic Model Averaging; Dynamic Model Selection; forecast combination; R
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Drachal, K. Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R. Signals 2020, 1, 47-99.

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