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Econometric Methods to Model Growth
Economists use econometric instruments in order to complete the quantitative analysis of the available economic data and determine possible relationships. Traditional econometric methods are statistical instruments associated with discussing the linear relationships and changes (Magnus, Powell, & Prufer, 2010, p. 140). The notion of the economic evolution is closely associated with the idea of the economic growth. Thus, economists focus on modelling growth in order to project the further changes (Morgan & Foster, 2000, p. 185).
However, the economic evolution and the idea of growth are often connected with the observed structural change. In this context, it is important to answer the question whether econometric methods can be used to model growth in the presence of structural change as a result of the economic evolution. Although traditional econometric methods are linear and they cannot be used for modelling growth in the context of structural change, combined and improved econometric methods can be applied to model growth while analysing concrete periods.
It is important to note that the economic growth affected by the structural change is not linear in its nature. This growth is characterised by the irregular change and movement of different growth patterns which constitute the structural change in this context. For instance, the economic growth of such countries as Romania is characterised by the presence of growth cycles, and the whole economic evolution is non-linear (Ionescu & Oana, 2014, p. 192). As a result, it is almost impossible to model the general economic growth for this country with the help of linear econometric methods because changes in the economic evolutions paths associated with the structural change cannot be reflected in such a model.
From this point, the main challenge which prevents the active use of traditional econometric methods to model growth when structural change is observed is the focus on linear methods. Economists traditionally use the linear econometrics to analyse only separate factors associated with forecasting the economic growth such as, for instance, consumption rate, tax ratio, equilibrium, and public debt (Teo, 2011, p. 50). Therefore, the linear econometrics is not appropriate to predict or model growth because of the absence of focus on dynamic changes.
Still, it is possible to apply econometric methods while modelling the growth in the context of structural change when specific econometric models are used to analyse the growth only at the concrete phase, without discussing the whole growth rate at all possible paths.
The improved approaches also include the focus on linear dynamic models and data techniques, the use of fuzzy regression approaches, and the use of logistic diffusion models (Morgan & Foster, 2000, p. 186). These models and methods can be effective to predict the growth of the market share, but they are not appropriate to model the general growth in the context of the economic evolution.
Therefore, econometric methods cannot be discussed as good instruments to model growth in those cases when structural change is observed. From this perspective, economists try to provide improved variants of econometric instruments to use them in the presence of structural change, but econometric methods are linear, and they can be applied only to separate phases of the growth. As a result, the other methods which differ from traditional linear econometric models are necessary to analyse non-linear relationships and model growth in order to provide more accurate results.
References
Ionescu, A., & Oana, G. (2014). Signalling economic growth through econometric analysis: Romanias case. Economics, Management and Financial Markets, 9(1), 191-205.
Magnus, J., Powell, O., & Prufer, P. (2010). A comparison of two model averaging techniques with an application to growth empirics. Journal of Econometrics, 154(1), 139-153.
Morgan, B., & Foster, J. (2000). Modelling growth in economic systems as the outcome of a process of self-organisational change: A fuzzy regression approach. In U. Cantner, H. Hanusch, & S. Klepper (Eds.), Economic evolution, learning, and complexity (pp. 185-207). Heidelberg, NY: Physica-Verlag.
Teo, T. (2011). Using structural equation modeling (SEM) in educational research: Practices and issues. International Journal of Applied Educational Studies, 10(1), 49-60.
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