A Bayesian approach to analyze regional elasticities



INFORMATIVE PRIOR DIFFUSE PRIOR

NORTH

CENTER

SOUTH

NORTH

CENTER

SOUTH

σ###KL

0.13841

0.05088

0.03848

-0.11992

-0.24101

-0.31822

st. err.

0.11212

0.08993

0.10634

0.35575

0.31708

0.23709

^##KK

-0.27205

-0.10575

-0.07020

0.23032

0.50949

0.58942

st. err.

0.22055

0.18890

0.19623

0.67920

0.67211

0.44091

σ≡LΓ-

-0.07054

-0.02450

-0.02112

0.06250

0.11416

0.17195

st. err.

0.05737

0.04297

0.05783

0.18763

0.15064

0.12808

ε≡KΓ^

0.09196

0.03438

0.02472

-0.07884

-0.16357

-0.20661

st. err.

0.07438

0.06050

0.06929

0.23317

0.21520

0.15408

ε≡LK-

0.04689

0.01655

0.01356

-0.04108

-0.07744

-0.11161

st. err.

0.03798

0.02889

0.03764

0.12271

0.10201

0.08310

£###KK

-0.09196

-0.03438

-0.02472

0.07884

0.16356

0.20661

st. err.

0.07438

0.06050

0.06929

0.23317

0.21520

0.15408

ε≡LΓ-

-0.04689

-0.01655

-0.01356

0.04107

0.07744

0.11161

st. err.

0.03798

0.02889

0.03764

0.12271

0.10201

0.08310

TABLE 3 - Elasticities conditional on 1989 (Y,t,w/r) without inequality constraints

A few comments are worthwhile. As shown in Table 2 and 3, expected values
conditional on labor share and on
(Y,t,w/r) without imposing concavity are very similar.
Therefore we can conclude that the standard translog model predict quite well in terms
of elasticities. Very different is the situation when we impose concavity. Results for
expected values and standard deviations are tabulated in Table 4.

14



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