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