INFORMATIVE PRIOR
DIFFUSE PRIOR
|
NORTH |
CENTER |
SOUTH |
PRIOR |
NORTH |
CENTER |
SOUTH | |
|
αL |
0.67734 |
0.65924 |
0.64535 |
0.71751 |
0.68055 |
0.65611 |
0.63901 |
|
st. err. |
0.00278 |
0.00234 |
0.00293 |
0.01158 |
0.00271 |
0.00346 |
0.00380 |
|
Yll |
0.19238 |
0.20726 |
0.21909 |
0.18793 |
0.25187 |
0.27113 |
0.30052 |
|
st. err. |
0.02470 |
0.01908 |
0.02447 |
0.01283 |
0.07895 |
0.06619 |
0.05407 |
|
Y###YL |
-0.07710 |
-0.06902 |
-0.05222 |
-0.18234 |
-0.09755 |
-0.06843 |
-0.06003 |
|
st. err. |
0.01820 |
0.01531 |
0.01742 |
0.02417 |
0.05927 |
0.06806 |
0.05447 |
|
6###TL |
-0.00582 |
-0.00826 |
-0.01077 |
0.00364 |
-0.00968 |
-0.01222 |
-0.01350 |
|
st. err. |
0.00131 |
0.00123 |
0.00135 |
0.00241 |
0.00439 |
0.00463 |
0.00322 |
|
σ^≡ |
0.00834 |
0.00660 |
0.00772 |
0.00351 |
0.00586 |
0.00620 |
0.00557 |
|
st. err. |
0.00159 |
0.00126 |
0.00147 |
0.00127 |
0.00213 |
0.00225 |
0.00202 |
with concavity imposed
|
δ###TL |
-0.00558 |
-0.00779 |
-0.01055 |
0.00522 |
-0.00599 |
-0.00791 | |
|
st. err. |
0.00125 |
0.00110 |
0.00119 |
0.00308 |
0.00400 |
0.00307 |
TABLE 1 - Parameter Characteristics
As I said above parameter distributions are nor very interesting per sè, perhaps
with the exception of the one relative to technical change. In Fig. B technical change
marginal densities in South are plotted using a diffuse or a proper prior with (solid line)
and without (dotted) concavity and monotonicity.
10
More intriguing information
1. On Social and Market Sanctions in Deterring non Compliance in Pollution Standards2. Distribution of aggregate income in Portugal from 1995 to 2000 within a SAM (Social Accounting Matrix) framework. Modeling the household sector
3. Importing Feminist Criticism
4. The name is absent
5. Conditions for learning: partnerships for engaging secondary pupils with contemporary art.
6. The name is absent
7. Co-ordinating European sectoral policies against the background of European Spatial Development
8. The name is absent
9. Giant intra-abdominal hydatid cysts with multivisceral locations
10. Neural Network Modelling of Constrained Spatial Interaction Flows