How to examine country by country in PVAR model

[Badiah Eljahimi]

Hi all, I'm examining the causality relationship between innovation, financial development, and growth for 24 countries, over a period of 17 years. employing the PVAR model proposed by Love and
Zicchino (2006)

Here are the results of the whole sample:

. pvar gdppcg IHS_pcdmb IHS_produc IHS_gfcf IHS_tr IHS_inf IHS_fdi, lag(1)

Panel vector autoregresssion



GMM Estimation

Final GMM Criterion Q(b) = 1.53e-31
Initial weight matrix: Identity
GMM weight matrix: Robust
No. of obs = 360
No. of panels = 24
Ave. no. of T = 15.000


------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gdppcg |
gdppcg |
L1. | .2765602 .1342178 2.06 0.039 .0134981 .5396222
|
IHS_pcdmb |
L1. | -1.724529 2.503537 -0.69 0.491 -6.631371 3.182313
|
IHS_produc |
L1. | -4.625741 8.366947 -0.55 0.580 -21.02466 11.77317
|
IHS_gfcf |
L1. | -4.225041 4.676805 -0.90 0.366 -13.39141 4.941328
|
IHS_tr |
L1. | -1.241037 3.722666 -0.33 0.739 -8.537328 6.055255
|
IHS_inf |
L1. | -.943753 .3293977 -2.87 0.004 -1.589361 -.2981455
|
IHS_fdi |
L1. | -.0283012 .1386068 -0.20 0.838 -.2999657 .2433632
-------------+----------------------------------------------------------------
IHS_pcdmb |
gdppcg |
L1. | .0054662 .002598 2.10 0.035 .0003742 .0105581
|
IHS_pcdmb |
L1. | 1.026977 .0548302 18.73 0.000 .9195121 1.134442
|
IHS_produc |
L1. | -.4790975 .2793335 -1.72 0.086 -1.026581 .0683861
|
IHS_gfcf |
L1. | .1067362 .1078064 0.99 0.322 -.1045603 .3180328
|
IHS_tr |
L1. | .1204885 .1079679 1.12 0.264 -.0911247 .3321018
|
IHS_inf |
L1. | .0156875 .0115818 1.35 0.176 -.0070124 .0383875
|
IHS_fdi |
L1. | -.0039915 .0051529 -0.77 0.439 -.014091 .006108
-------------+----------------------------------------------------------------
IHS_produc |
gdppcg |
L1. | .0000195 .0009747 0.02 0.984 -.0018908 .0019299
|
IHS_pcdmb |
L1. | -.0117249 .0212992 -0.55 0.582 -.0534706 .0300207
|
IHS_produc |
L1. | .9685544 .0875681 11.06 0.000 .796924 1.140185
|
IHS_gfcf |
L1. | -.0051895 .0378729 -0.14 0.891 -.0794191 .0690401
|
IHS_tr |
L1. | -.0042789 .0388676 -0.11 0.912 -.0804581 .0719002
|
IHS_inf |
L1. | -.0001209 .0038576 -0.03 0.975 -.0076817 .0074399
|
IHS_fdi |
L1. | .0015152 .001466 1.03 0.301 -.001358 .0043884
-------------+----------------------------------------------------------------
IHS_gfcf |
gdppcg |
L1. | .0109155 .00223 4.89 0.000 .0065447 .0152862
|
IHS_pcdmb |
L1. | -.0519499 .0430884 -1.21 0.228 -.1364016 .0325018
|
IHS_produc |
L1. | .0992881 .1804791 0.55 0.582 -.2544445 .4530206
|
IHS_gfcf |
L1. | .8770643 .0908029 9.66 0.000 .699094 1.055035
|
IHS_tr |
L1. | -.0324826 .0825636 -0.39 0.694 -.1943043 .1293391
|
IHS_inf |
L1. | .0014183 .008252 0.17 0.864 -.0147553 .017592
|
IHS_fdi |
L1. | -.0016733 .0039715 -0.42 0.674 -.0094572 .0061106
-------------+----------------------------------------------------------------
IHS_tr |
gdppcg |
L1. | -.0004768 .0015733 -0.30 0.762 -.0035605 .0026069
|
IHS_pcdmb |
L1. | -.0001921 .0367628 -0.01 0.996 -.072246 .0718617
|
IHS_produc |
L1. | .1605265 .2055623 0.78 0.435 -.2423682 .5634212
|
IHS_gfcf |
L1. | -.0680593 .1011352 -0.67 0.501 -.2662808 .1301621
|
IHS_tr |
L1. | .7555747 .0868285 8.70 0.000 .5853939 .9257554
|
IHS_inf |
L1. | -.0201886 .0083944 -2.41 0.016 -.0366413 -.0037359
|
IHS_fdi |
L1. | -.0002274 .0035065 -0.06 0.948 -.0070999 .0066452
-------------+----------------------------------------------------------------
IHS_inf |
gdppcg |
L1. | -.0083513 .0245095 -0.34 0.733 -.0563891 .0396864
|
IHS_pcdmb |
L1. | -.2865985 .5251933 -0.55 0.585 -1.315959 .7427615
|
IHS_produc |
L1. | -3.025331 2.777078 -1.09 0.276 -8.468304 2.417642
|
IHS_gfcf |
L1. | 1.457915 1.245401 1.17 0.242 -.9830261 3.898856
|
IHS_tr |
L1. | 2.56168 1.382939 1.85 0.064 -.14883 5.272191
|
IHS_inf |
L1. | .5171584 .1119532 4.62 0.000 .2977342 .7365826
|
IHS_fdi |
L1. | .0533884 .0517517 1.03 0.302 -.0480431 .1548199
-------------+----------------------------------------------------------------
IHS_fdi |
gdppcg |
L1. | .0621273 .0588894 1.05 0.291 -.0532939 .1775484
|
IHS_pcdmb |
L1. | 2.734762 1.648998 1.66 0.097 -.4972149 5.966739
|
IHS_produc |
L1. | -9.394742 6.466876 -1.45 0.146 -22.06959 3.280102
|
IHS_gfcf |
L1. | -7.767025 3.608453 -2.15 0.031 -14.83946 -.6945857
|
IHS_tr |
L1. | -1.837303 2.555886 -0.72 0.472 -6.846746 3.172141
|
IHS_inf |
L1. | .0141353 .2692019 0.05 0.958 -.5134906 .5417613
|
IHS_fdi |
L1. | -.0237097 .1451742 -0.16 0.870 -.3082459 .2608265
------------------------------------------------------------------------------
Instruments : l(1/1).(gdppcg IHS_pcdmb IHS_produc IHS_gfcf IHS_tr IHS_inf IHS_fdi)

Now I'm trying to examine countries individually. i.e. I'm trying to examine the causal relationship between innovation, financial
development, and economic growth for individual countries. could you please guide me if there is a code for examining countries individually using VAR model for time series? Thanks

[George Ford]

You could loop through the countries, using preserve/restore to tsset your data within the loop and executing var. But, you've only got 17 observations per country. Pretty thin.