Dear Gretl-Users Community,
I am a political science student, who is working on a Master Thesis in
'What factors hinder economic growth of oil-producing countries?' through
the case of Venezuela from 2001 to 2015. I have to admit that I have poor
background in statistics but comfortable with using computer software,
hence I am writing to seek for guidance in model correction and
interpretation of dataset. I would like to determine the positive/negative
correlations between Venezuela's GDP Growth and varying factors such as Oil
Production, OPEC Spare Capacity, Economic Freedom etc. from 2001 to 2015
(annual basis). Could you guys please comment on my model, whether it fits
for the aim of determining relationship and correlations between varying
factors? Thank you
Using the time series ARIMA model, the Dependent var. = Venezuela's GDP
Growth Rate
Here's the ADF Test of GDP Growth with lag of 2 from 14 data (annual).
*Augmented Dickey-Fuller test for GDP_Growth*
including 0 lags of (1-L)GDP_Growth
(max was 2, criterion AIC)
sample size 14
unit-root null hypothesis: a = 1
test with constant
model: (1-L)y = b0 + (a-1)*y(-1) + e
estimated value of (a - 1): -0.657506
test statistic: tau_c(1) = -2.29321
p-value 0.1868
1st-order autocorrelation coeff. for e: 0.131
with constant and trend
model: (1-L)y = b0 + b1*t + (a-1)*y(-1) + ... + e
estimated value of (a - 1): -0.935225
test statistic: tau_ct(1) = -3.03246
asymptotic p-value 0.1232
1st-order autocorrelation coeff. for e: -0.517
*ARIMA Model (0,0,0)*Model 2: ARMAX, using observations 2001-2015 (T = 15)
Estimated using least squares (= MLE)
Dependent variable: GDP_Growth
coefficient std.
error z p-value
----------------------------------------------------------------
const −26.9659 24.2697
−1.111 0.2665
ProvenCrude_Rese~ −0.0559316 0.0214914 −2.603
0.0093 ***
CrudeOil_Product~ 23.8026 8.44659
2.818 0.0048 ***
BalanceofTrade_U~ 0.000221970 0.000111334 1.994
0.0462 **
OPEC_SpareCapaci~ −3.80518 1.11847 −3.402
0.0007 ***
Economic_Freedom −0.570406 0.314276 −1.815
0.0695 *
Mean dependent var 2.371052 S.D. dependent var 7.682116
Mean of innovations 1.05e-14 S.D. of innovations 4.646369
Log-likelihood −40.49418 Akaike criterion 92.98835
Schwarz criterion 97.23665 Hannan-Quinn 92.94310