Abstract
| Original language | English |
|---|---|
| Place of Publication | Tilburg |
| Publisher | Econometrics |
| Number of pages | 39 |
| Volume | 2012-017 |
| Publication status | Published - 2012 |
Publication series
| Name | CentER Discussion Paper |
|---|---|
| Volume | 2012-017 |
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Keywords
- Hierarchical model averaging
- Growth determinants
- Measurement problem
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Concept-Based Bayesian Model Averaging and Growth Empirics. / Magnus, J.R.; Wang, W.
Tilburg : Econometrics, 2012. (CentER Discussion Paper; Vol. 2012-017).Research output: Working paper › Discussion paper › Other research output
TY - UNPB
T1 - Concept-Based Bayesian Model Averaging and Growth Empirics
AU - Magnus, J.R.
AU - Wang, W.
N1 - Pagination: 39
PY - 2012
Y1 - 2012
N2 - Abstract: In specifying a regression equation, we need to determine which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth theories taking into account the measurement problem in the growth regression. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques when the number of variables is large or when computing time is limited, and we propose possible strategies for sensitivity analysis.
AB - Abstract: In specifying a regression equation, we need to determine which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth theories taking into account the measurement problem in the growth regression. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques when the number of variables is large or when computing time is limited, and we propose possible strategies for sensitivity analysis.
KW - Hierarchical model averaging
KW - Growth determinants
KW - Measurement problem
M3 - Discussion paper
VL - 2012-017
T3 - CentER Discussion Paper
BT - Concept-Based Bayesian Model Averaging and Growth Empirics
PB - Econometrics
CY - Tilburg
ER -