Description Usage Arguments Details Value Author(s) References See Also Examples
This function estimates an Instrumental Variable (IV) system while incorporating model uncertainty and performing model averaging using an MC3-within-Gibbs Sampler.
1 2 |
Y |
n x 1 matrix. Response variable |
X |
n x r matrix. Endogenous variables |
W |
n x p matrix. Further explanatory variables. You are responsible for including an intercept. |
Z |
n x q matrix. Instrumental variables |
s |
integer. Number of iterations |
b |
integer. Number of iterations to discard as burn-in. |
full |
If full is TRUE then model selection is not performed |
odens |
Output density. How many samples from the posterior should be returned? Note that posterior expectations are taken over every sample after burn-in |
print.every |
After how many iterations should the progress be printed? |
run.diagnostics |
If TRUE, this will compute experimental diagnostics to assess the validity of the instruments in use. Note that this adds a non-negligible amount of computing time. |
The function estimates the parameters based on the model
Y = [X W] * ρ + ε
X = [Z W] * λ + η
with
(ε_i, η_i)^T \sim N_2 ( 0,Σ)
and its extension to multiple endogenous variables.
If full
is set to FALSE
model uncertainty is included
using conditional Bayes factors.
rho |
An odens x (r + p) matrix with sampled values for the outcome stage. Endogenous variables come first. |
rho.bar |
Posterior expectation of the outcome stage taken over all iterations |
lambda |
A (p + q) x r x odens array with sampled values for the parameters of the first stage regressions. Instruments come first. |
lambda.bar |
Posterior expectation of each first stage taken over all iterations |
Sigma |
odens sampled realizations of Sigma |
Sigma.bar |
Posterior expectation of Sigma taken over all iterations |
M |
Sampled first stage models |
M.bar |
Posterior first stage inclusion probabilities |
L |
Sampled second stage models |
L.bar |
Posterior second stage inclusion probabilities |
If run.diagnostics was set to TRUE then you also receive
Sargan |
Model averaged Sargan p-values. Lower values indicate lack of instrument validity |
Bayesian.Sargan |
An _Experimental_ Bayesian Sargan diagnostic based on Conditional Bayes Factors. Same direction as above |
Alex Lenkoski (alex.lenkoski@uni-heidelberg.de)
Anna Karl and Alex Lenkoski (2012). "Instrumental Variable Bayesian Model Averaging via Conditional Bayes Factors" http://arxiv.org/abs/1202.5846
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | set.seed(1)
data(growth)
attach(growth)
## To replicate KL, set s to 1e5
a <- ivbma(Y, X, Z, W, s = 1e2)
summary(a, nms.U = c(names(Z), names(W)),nms.V = c(names(X), names(W)))
detach(growth)
set.seed(1)
data(margarine)
attach(margarine)
## To replicate KL, set s to 2.5e5
a <- ivbma(Y, X, Z, W, s=1e2)
summary(a, nms.U = c(names(Z), names(W)),nms.V = c(names(X), names(W)))
detach(margarine)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.