gammaToBeta {WALS} | R Documentation |
Transforms posterior means \hat{\gamma}_2
and variances corresponding
to transformed auxiliary regressors Z_2
back to regression coefficients
\hat{\beta}
of original regressors X_1
and X_2
.
gammaToBeta(
posterior,
y,
Z1,
Z2,
Delta1,
D2,
sigma,
Z1inv,
method = "original",
svdZ1
)
posterior |
Object returned from |
y |
Response |
Z1 |
Transformed focus regressors |
Z2 |
Transformed auxiliary regressors |
Delta1 |
|
D2 |
From |
sigma |
Prespecified or estimated standard deviation of the error term. |
Z1inv |
|
method |
Character. |
svdZ1 |
Optional, only needed if |
The same transformations also work for GLMs, where we replace X_1
,
X_2
, Z_1
and Z_2
with \bar{X}_1
, \bar{X}_2
,
\bar{Z}_1
and \bar{Z}_2
, respectively. Generally, we need to
replace all variables with their corresponding "bar" version. Further,
for GLMs sigma
is always 1.
See Magnus and De Luca (2016), De Luca et al. (2018) and Huynh (2024b) for the definitions of the variables.
De Luca G, Magnus JR, Peracchi F (2018).
“Weighted-average least squares estimation of generalized linear models.”
Journal of Econometrics, 204(1), 1–17.
doi:10.1016/j.jeconom.2017.12.007.
Huynh K (2024b).
“WALS: Weighted-Average Least Squares Model Averaging in R.”
University of Basel.
Mimeo.
Magnus JR, De Luca G (2016).
“Weighted-average least squares (WALS): A survey.”
Journal of Economic Surveys, 30(1), 117-148.
doi:10.1111/joes.12094.