Page 27 - Azerbaijan State University of Economics

THE JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.78, # 2, 2021, pp. 17-42

On the other hand, short sample period does not allow us to use non-linear or
Markov Switching models. Due to these constraints, we will conduct our estimations
by employing simple VAR methodology. VAR model allows us to eliminate
possible endogeneity problems of explanatory variables. In VAR specification we
propose the following Cholesky ordering scheme: X= (Δoil revenue, Δtp_cpi, Δneer,
Δcpi) ′.

∆ = −1 (∆ ) + (1)
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∆ = −1 (∆ )+ + (2)
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∆ = −1 (∆ ) + + + (3)
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∆ = −1 (∆ ) + + + + (4)

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∆ = (∆ ) + + + + (5)
−1 41 42 43 44

∆ − = −1 (∆ − ) + + + + − (6)
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∆ = −1 (∆ ) + + + + (7)
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where is real oil revenue, denotes consumer price level of trading partners.

−
shows nominal effective exchange rate. Finally, , , ,

represents aggregate headline CPI, food CPI, non-food CPI and services CPI.

, , , , , − and are shocks of oil revenue, trading

partners’ CPI, exchange rate, aggregate CPI, food CPI, non-food CPI and services
CPI, respectively. −1 is the expectation of a variable conditional on the
information set at the end of period − 1.

In our identification scheme, we assume that Oil revenue is the most exogenous
variable. As we already mentioned above, Oil revenue consists of two components:
oil prices and oil production. Since oil price is exogenously determined in
international markets and volume of oil production is determined based on long-term
contracts between oil producers and importers, we assume that oil production is also
exogenous variable. Therefore, we can treat oil revenue as an exogenous variable. It
implies that in our identification scheme structural shocks on the rest of the variables
do not have any effect on this variable.

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