REVISTA ACADÉMICA ECO
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This paper maintains that it is necessary to incorporate
these sorts of factors in economic modelling in order
to formulate strong policies of minimal vulnerability
possible. However, it must be assumed that all
these factors maintain a constant quantitative and
qualitative transformation(s) in different historical
periods of the society concerned (Ruiz Estrada,
2011). Moreover, this paper makes a deep analysis
about economic modelling evolution. We are taking
in account a careful study of a total of 1775 papers.
It presents the percentages of papers published in
individual categories of economic modelling identified
by Ruiz Estrada. Additionally, we are going to study
the common modelling approaches used in different
papers at the last twenty seven years in the journal of
economic modelling (JEM) journal. At the same time,
this paper recommends multidisciplinary approach to
economic modelling. It suggests the incorporation of
multidisciplinary, non-economic variables in economic
modelling to formulate strong policies. Secondly, the
evolution of the journal of economic modelling (JEM)
journal is possible to observe through different volumes
from year 1984 until year 2012 that the application of
different research approaches into economic modelling
keeps a constant quantitative transformation (volume
of research output) and a qualitative transformation
(content and form). Especially, these quantitative
and qualitative transformations can be observed in
different manuscripts in this specific journal by the
application of different quantitative and qualitative
methods, innovative policies and recommendations.
3. The Economic Modeling Trend
Among the 1775 papers published in the journal of
economic modelling (JEM) journal in the past twenty
eight years (1984-2012), the following research
orientation was common: benefit/cost, probabilistic
or forecasting analysis through the application of
econometric methods and use of microeconomic and
macroeconomic levels secondary data. Therefore, we
are using forty (40) variables to evaluate all papers
were published by the economic modelling (EM)
journal until today. The following forty (40) variables
are (1.) predicting economic modelling; (2.) monitoring
economic modelling; (3.) simulation economic
modelling; (4.) empirical economic modelling; (5.)
theoretical economic modelling; (6.) primary data
economic modelling; (7.) secondary data economic
modelling; (8.) long run economic modelling; (9.)
short run economic modelling; (10.) linear regression
analysis; (11.) multiple regression analysis; (12.)
times series data analysis; (13.) cross-sectional data
analysis; (14.) panel data analysis; (15.) 2-Dimensional
graphical modelling; (16.) 3-Dimensional graphical
modelling; (17.) economics policy modelling approach;
(18.) technological policy modelling; (19.) environment
policy modelling; (20.) original theoretical framework;
(21.) traditional theoretical framework; (22.) extension
theoretical framework; (23.) private sector modelling;
(24.) public sector modelling; (25.) macroeconomics
modelling; (26.) microeconomics modelling; (27.)
partial equilibrium modelling; (28.) general equilibrium
modelling; (29.) dynamic economic modelling; (30.)
static economic modelling; (31.) perfect competition
modelling; (32.) imperfect competition modelling; (33.)
national level modelling; (34.) regional level modelling;
(35.) global level modelling; (36.) Keynesian modelling
approach; (37.) monetary modelling approach; (38.)
classic economic modelling approach; (39.) neo-
classic economic modelling approach; (40.) planning
economic modelling approach. (see Table 3). Based on
the same study and the same classification of variables
above, the percentages of papers in the individual
modelling approaches in the journal of economic
modelling (JEM) was found to be as follows: (1.)
predicting economic modelling (1456 papers = 82%);
(2.) monitoring economic modelling (142 papers =
8%); (3.) simulation economic modelling (178 papers =
10%); (4.) empirical economic modelling (1331 papers =
75%); (5.) theoretical economic modelling (444 papers
= 25%); (6.) primary data economic modelling (18
papers = 1%); (7.) secondary data economic modelling
(1757 papers = 99%); (8.) long run economic modelling
(1686 papers = 95%); (9.) short run economic modelling
(89 papers = 5%) ; (10.) linear regression analysis (89
papers = 5%); (11.) multiple regression analysis (302
papers = 17%); (12.) times series data analysis (408
papers = 23%); (13.) cross-sectional data analysis (568
papers = 32%); (14.) panel data analysis (408 papers
= 23%); (15.) 2-Dimensional graphical modelling
(1757 papers = 99%); (16.) 3-Dimensional graphical