|
Parameter
|
Mean
|
SD
|
95% Interval
|
CD
|
Inefficiency
|
|---|
|
\({(}\sum \beta {)}_{{1}}\)
|
0.0023
|
0.0003
|
[0.0018, 0.0029]
|
0.366
|
5.20
|
|
\({(}\sum \beta {)}_{{2}}\)
|
0.0023
|
0.0003
|
[0.0018, 0.0029]
|
0.113
|
3.70
|
|
\({(}\sum a {)}_{{1}}\)
|
0.0056
|
0.0017
|
[0.0034, 0.0099]
|
0.796
|
15.53
|
|
\({(}\sum a {)}_{{2}}\)
|
0.0048
|
0.0024
|
[0.0031, 0.0079]
|
0.787
|
22.10
|
|
\({(}\sum h {)}_{{1}}\)
|
0.0054
|
0.0015
|
[0.0034, 0.0091]
|
0.516
|
13.45
|
|
\({(}\sum h {)}_{{2}}\)
|
0.0056
|
0.0016
|
[0.0034, 0.0097]
|
0.846
|
10.42
|
- Done on EViews 12
- In this study, we assume as a diagonal matrix for simplicity. Compared to the non-diagonal assumption, previous experiences of Primiceri [38] and Nakajima [34] indicate that this assumption is not sensitive to the results. The following priors are assumed for the i -th diagonals of the covariance matrices: \({(}\sum \beta {)}_{i}^{{ - 2}} \sim Gamma \, (40,0.02)\) \({(}\sum a {)}_{i}^{{ - 2}} \sim Gamma \, (40,0.02)\) \({(}\sum h {)}_{i}^{{ - 2}} \sim Gamma \, (40,0.02)\)
- For the initial state of the time-varying parameter, rather flat priors are set as \(\mu_{{\beta_{0} }} = \mu_{{a_{0} }} = \mu_{{h_{0} }} = 0\), and \(\sum {\beta_{0} } = \sum {a_{0} } = \sum {h_{0} } = 10 \times I\). To compute the posterior estimates, we draw M = 10,000 observations after the initial 1000 observations are discarded. The estimates of \(\sum \beta\) and \(\sum \alpha\) are multiplied by 100. SD = standard deviation; CD = convergence diagnostics statistics
