Hi Please assist on the correct coding for Bayesian Panel VAR Model
This is the codes am using to get the coefficients when i rerun the codes they generate new different coefficients for the prameters included as priors,
bayes, prior({bank_zscore:co2_emissions}, normal(0,10000)) nchains(3) ///
prior({bank_zscore:temp}, normal(0,10000)) ///
prior({bank_zscore:renewable}, normal(0,10000)): regress bank_zscore cpi gdp exchange_rate unemploy
esian linear regression Number of chains = 3
Random-walk Metropolis–Hastings sampling Per MCMC chain:
Iterations = 12,500
Burn-in = 2,500
Sample size = 10,000
Number of obs = 600
Avg acceptance rate = .3242
Avg efficiency: min = .03877
avg = .07815
max = .2258
Avg log marginal-likelihood = -528.09729 Max Gelman–Rubin Rc = 1.007
-------------------------------------------------------------------------------
| Equal-tailed
| Mean Std. dev. MCSE Median [95% cred. interval]
--------------+----------------------------------------------------------------
bank_zscore |
cpi | .1961285 .0510366 .001276 .1962957 .0965352 .2959153
gdp | -.0034678 .0052482 .000154 -.0035323 -.0137184 .006792
exchange_rate | -.0669912 .0087213 .000225 -.067051 -.0836946 -.0495717
unemploy | -.0754374 .0227272 .000651 -.0751659 -.1201616 -.0300091
_cons | 1.976156 .2388241 .00566 1.97357 1.515922 2.444527
--------------+----------------------------------------------------------------
sigma2 | .2909023 .0167394 .000203 .290457 .2596255 .3258565
--------------+----------------------------------------------------------------
bank_zscore |
co2_emissions | 1.30486 97.48091 1.9873 2.31148 -191.5195 195.7779
temp | -.554729 97.98788 2.04469 -.7729672 -191.6773 195.147
renewable | -.4374633 97.84869 2.01713 -1.14357 -189.8018 194.703
-------------------------------------------------------------------------------
Note: Default priors are used for model parameters.
Sec estimation when rerruning the model
Bayesian linear regression Number of chains = 3
Random-walk Metropolis–Hastings sampling Per MCMC chain:
Iterations = 12,500
Burn-in = 2,500
Sample size = 10,000
Number of obs = 600
Avg acceptance rate = .3027
Avg efficiency: min = .0383
avg = .07712
max = .2039
Avg log marginal-likelihood = -528.06644 Max Gelman–Rubin Rc = 1.003
-------------------------------------------------------------------------------
| Equal-tailed
| Mean Std. dev. MCSE Median [95% cred. interval]
--------------+----------------------------------------------------------------
bank_zscore |
cpi | .1993594 .0518113 .001439 .1997266 .0988088 .30021
gdp | -.0032263 .0052958 .000121 -.0032195 -.0136243 .0071494
exchange_rate | -.0670696 .00891 .000229 -.0672743 -.0844076 -.0495029
unemploy | -.0765205 .0231021 .000682 -.0768223 -.1213171 -.0308558
_cons | 1.963093 .2384466 .006567 1.963514 1.501535 2.434646
--------------+----------------------------------------------------------------
sigma2 | .2909751 .0167047 .000214 .2904782 .2600855 .3253069
--------------+----------------------------------------------------------------
bank_zscore |
co2_emissions | -4.229638 99.66109 2.0484 -2.611635 -203.937 191.8734
temp | -.9293789 100.1134 1.95715 -.6258597 -196.8552 192.7595
renewable | 1.208592 97.90552 1.95044 2.656565 -191.3281 187.3914
-------------------------------------------------------------------------------
This is the codes am using to get the coefficients when i rerun the codes they generate new different coefficients for the prameters included as priors,
bayes, prior({bank_zscore:co2_emissions}, normal(0,10000)) nchains(3) ///
prior({bank_zscore:temp}, normal(0,10000)) ///
prior({bank_zscore:renewable}, normal(0,10000)): regress bank_zscore cpi gdp exchange_rate unemploy
esian linear regression Number of chains = 3
Random-walk Metropolis–Hastings sampling Per MCMC chain:
Iterations = 12,500
Burn-in = 2,500
Sample size = 10,000
Number of obs = 600
Avg acceptance rate = .3242
Avg efficiency: min = .03877
avg = .07815
max = .2258
Avg log marginal-likelihood = -528.09729 Max Gelman–Rubin Rc = 1.007
-------------------------------------------------------------------------------
| Equal-tailed
| Mean Std. dev. MCSE Median [95% cred. interval]
--------------+----------------------------------------------------------------
bank_zscore |
cpi | .1961285 .0510366 .001276 .1962957 .0965352 .2959153
gdp | -.0034678 .0052482 .000154 -.0035323 -.0137184 .006792
exchange_rate | -.0669912 .0087213 .000225 -.067051 -.0836946 -.0495717
unemploy | -.0754374 .0227272 .000651 -.0751659 -.1201616 -.0300091
_cons | 1.976156 .2388241 .00566 1.97357 1.515922 2.444527
--------------+----------------------------------------------------------------
sigma2 | .2909023 .0167394 .000203 .290457 .2596255 .3258565
--------------+----------------------------------------------------------------
bank_zscore |
co2_emissions | 1.30486 97.48091 1.9873 2.31148 -191.5195 195.7779
temp | -.554729 97.98788 2.04469 -.7729672 -191.6773 195.147
renewable | -.4374633 97.84869 2.01713 -1.14357 -189.8018 194.703
-------------------------------------------------------------------------------
Note: Default priors are used for model parameters.
Sec estimation when rerruning the model
Bayesian linear regression Number of chains = 3
Random-walk Metropolis–Hastings sampling Per MCMC chain:
Iterations = 12,500
Burn-in = 2,500
Sample size = 10,000
Number of obs = 600
Avg acceptance rate = .3027
Avg efficiency: min = .0383
avg = .07712
max = .2039
Avg log marginal-likelihood = -528.06644 Max Gelman–Rubin Rc = 1.003
-------------------------------------------------------------------------------
| Equal-tailed
| Mean Std. dev. MCSE Median [95% cred. interval]
--------------+----------------------------------------------------------------
bank_zscore |
cpi | .1993594 .0518113 .001439 .1997266 .0988088 .30021
gdp | -.0032263 .0052958 .000121 -.0032195 -.0136243 .0071494
exchange_rate | -.0670696 .00891 .000229 -.0672743 -.0844076 -.0495029
unemploy | -.0765205 .0231021 .000682 -.0768223 -.1213171 -.0308558
_cons | 1.963093 .2384466 .006567 1.963514 1.501535 2.434646
--------------+----------------------------------------------------------------
sigma2 | .2909751 .0167047 .000214 .2904782 .2600855 .3253069
--------------+----------------------------------------------------------------
bank_zscore |
co2_emissions | -4.229638 99.66109 2.0484 -2.611635 -203.937 191.8734
temp | -.9293789 100.1134 1.95715 -.6258597 -196.8552 192.7595
renewable | 1.208592 97.90552 1.95044 2.656565 -191.3281 187.3914
-------------------------------------------------------------------------------
