Home  Journals  Financial Analytics: Science and Experience  13(247) - 2015 April

A study of dynamic properties of economic systems using weight functions

Vol. 8, Iss. 13, APRIL 2015

Article PDF Version

Available online: 5 April 2015

Subject Heading: MATHEMATICAL ANALYSIS AND MODELING IN ECONOMICS

JEL Classification: 

Pages: 56-64

Importance A correlation and regression analysis is a conventional and traditional method to model dynamic relation of economic parameters. The resulting autoregression models and distributed lag models are known to have a number of weaknesses arising from an error in assumptions of the method of least squares, and difficulties in choosing the lag length. As a result of distortion in the dynamic properties, such models provide unsatisfactory forecasts. Due to this, currently issues of devising reliable models reflecting dynamics of economic factors remain rather relevant.
     Objectives Dynamic relations of economic indicators, as illustrated with GDP and investment in the Russian economy, are modeled as ordinary linear differential equations. Afterwards I transform them into a discrete format using the known transformation methods. Hence I model autoregressive models and distributed lag models, with the issue of choosing the lag length no longer existing.
     Methods I apply weight functions as a tool to transform the linear model into a discrete format.
     Results The proposed algorithm for forming autoregressive and lag models through weight functions facilitates a choice of their structure and adequacy of estimates. Computational modeling proves high adequacy of the models, as well as their capabilities for forecasting.
     Application The proposed methods are rather effective and may be used to model economic processes, and economic indicators of industries and companies, in particular: for example, the dependence of profit on production investment, advertising expenses or market research.
     Conclusions and Relevance As shown in the research of GDP dependence on investment, the proposed modeling algorithm demonstrates the efficiency, thus allowing not only creating very precise forecasting models, but also analyzing to what extent the resulting factor depends on input variables.

Keywords: economic model, dynamics, lag model, autoregressive model, weight function

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