BMS and the Fixed Effects Estimator - A Tutorial
Thursday 26 May 2011 at 12:51 pm
This tutorial illustrates how to use Bayesian Model Averaging (BMA) with panel data using the R package BMS.
Defining Custom Model Priors in BMS
Wednesday 11 May 2011 at 1:10 pm
Bayesian Model Averaging (BMA) allows for any kind of model prior distributions. While the R package BMS has built-in support for several types of commonly used priors, there may be the need for constructing a custom model prior in a particular exercise.
This tutorial is for users which already dispose of some experience in BMS. Beginners might find other tutorials vastly more helpful.
BMS 0.3.0 Released
Thursday 05 May 2011 at 10:35 pm
Version of 0.3.0 of the Bayesian Model Averaging package BMS has been released. Apart from numerous bugfixes, BMS 0.3.0 includes two main additions:
- The ability in
bmsto keep certain fixed regressors to be included in all sampled models - The option to calculate predictive densities with function
pred.density.
Moreover, the interrnal structure has been redesigned to accommodate user-defined priors and samplers.
Read MoreReplicating the Body Fat Example from "Bayesian Model Averaging: A Tutorial" (1999) with BMS in R
Monday 07 February 2011 at 3:19 pm
In their paper Bayesian Model Averaging: A Tutorial (Statistical Science 14(4), 1999, pp. 382-401), Hoeting, Madigan, Raftery and Volinsky (HMRV) do an exercise in Bayesian Model Averaging (BMA) at pp.394-397 in estimating body fat data from Johnson (1996): "Fitting Percentage of Body Fat to Simple Body Measurements"; Journal of Statistics Education 4(1).
This tutorial shows how to reproduce the results with the R-package BMS.
Reproducing the BMA exercise of Fernández, Ley and Steel (2001) in R
Monday 07 February 2011 at 2:17 pm
In their paper Model uncertainty in cross-country growth regressions (Journal of Applied Econometrics 2001), Fernández, Ley and Steel (FLS) demonstrate the use of Bayesian Model Averaging (BMA) for a cross-section economic growth data set.
This tutorial shows how to reproduce the results with the R-package BMS.
A video tutorial (09:56) summarizes the essential part of this web tutorial.
The bms Function Explained
Saturday 06 November 2010 at 12:17 am
This text 'pedagocially' explains how the bms function works code-wise and is intended for people who prefer to program customized adjustments of the bms package.
bms is the workhorse function to do the sampling part of Bayesian Model Averaging in the BMS package. The bms function code in the package includes many different options, usability checks, and is tweaked for computational speed. Although it can be difficult to understand, it basically relies on object-oriented subfunctions that can be easily re-shuffled. In order to demonstrate this, the following paragraphs introduce code that replicate a very basic BMA sampling operation but stripped of all options and speed features.
Bayesian Model Averaging (BMA) with uncertain Spatial Effects
Wednesday 27 October 2010 at 1:32 pm
This file illustrates the computer code to use spatial filtering in the context of Bayesian Model Averaging (BMA). For more details and in case you use the code please cite Crespo Cuaresma and Feldkircher (2010).
In addition, this tutorial exists as well in PDF form: web_tutorial_spatfilt.pdf
The BMS Add-ons Section
Monday 18 October 2010 at 4:06 pm
This section will be updated irregularly with pages on applications of BMS. It intends to concentrate on two things:
- Some add-on code to extend the functionality of BMS in a certain direction, such as e.g. BMA and spatial filtering
- Usage of BMS to replicate BMA articles, such as the Body-fat exercise by Hoeting, Madigan, Raftery and Volinsky (1999)
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