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FDI and Democratic Governance, Evidence from a Panel VAR Model
Unformatted Document Text:  which each variable is in turn explained by its own lagged values, plus past values of the remaining n-1 variables. It combines the traditional VAR approach, which treats all the variables in the system as endogenous, with the panel-data approach, which allows for unobserved individual heterogeneity (Love and Zicchino 2004). Several design issues about PVAR model are worthy noting. First of all, a critical issue to resolve when using PVAR model is the number of lags of the dependent and independent variables used in the equations. There is always a dilemma, if there are too few lags, the model will be inconsistent, but if there are too many lags, the model will be inefficient (Burkhart and Lewis-Beck 1994, Soysa 2003). For a PVAR model, it is even more difficult to decide. Here I will use a tradition method to decide the number of lag terms by comparing results using different lag terms. If the results using one lag term, two lag terms, three lag terms, and four lag terms are consistent, then I will prefer using one or two lag terms. So I specify a first-order VAR model with i lag terms as follows: Democracy t = a 1 + b 1 FDI from democratic countries t-i + b 2 Democracy t-i FDI from democratic countries t = a 2 +b 3 FDI from democratic countries t-i + Democracy t-i (i =1, 2, 3, 4, 5….n) The second issue is control variables. In my analysis I will not include any control variables in the PVAR model. The reason is that by controlling the lagged dependent variable, I capture the effects of variables contributing to the dependent variable. That is, the values of previous democracy levels contain all the information about the previous conditions promoting democratic features, including the economic and social situations. In this case, it is difficult for spurious effects to be reported. Furthermore, the inclusion of the lagged dependent variable might “soak up the variations in the dependent variable 13

Authors: Sun, Feng.
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which each variable is in turn explained by its own lagged values, plus past values of the
remaining n-1 variables. It combines the traditional VAR approach, which treats all the
variables in the system as endogenous, with the panel-data approach, which allows for
unobserved individual heterogeneity (Love and Zicchino 2004).
Several design issues about PVAR model are worthy noting. First of all, a critical
issue to resolve when using PVAR model is the number of lags of the dependent and
independent variables used in the equations. There is always a dilemma, if there are too
few lags, the model will be inconsistent, but if there are too many lags, the model will be
inefficient (Burkhart and Lewis-Beck 1994, Soysa 2003). For a PVAR model, it is even
more difficult to decide. Here I will use a tradition method to decide the number of lag
terms by comparing results using different lag terms. If the results using one lag term,
two lag terms, three lag terms, and four lag terms are consistent, then I will prefer using
one or two lag terms. So I specify a first-order VAR model with i lag terms as follows:
Democracy
t
= a
1
+ b
1
FDI from democratic countries
t-i
+ b
2
Democracy
t-i
FDI from democratic countries
t
= a
2
+b
3
FDI from democratic countries
t-i
+ Democracy
t-i
(i =1, 2, 3, 4, 5….n)
The second issue is control variables. In my analysis I will not include any control
variables in the PVAR model. The reason is that by controlling the lagged dependent
variable, I capture the effects of variables contributing to the dependent variable. That is,
the values of previous democracy levels contain all the information about the previous
conditions promoting democratic features, including the economic and social situations.
In this case, it is difficult for spurious effects to be reported. Furthermore, the inclusion of
the lagged dependent variable might “soak up the variations in the dependent variable
13


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