A Bayesian approach to analyze regional elasticities



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



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