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Main aspects on the nature of dynamic models

Author

Listed:
  • Constantin ANGHELACHE

    (Bucharest University of Economic Studies, Romania)

  • Dana Luiza GRIGORESCU

    (Bucharest University of Economic Studies, Romania)

  • Oana BÎRSAN

    (Bucharest University of Economic Studies, Romania)

Abstract

The dynamic models used in the micro or macro-economic analyzes appeared as a necessity to correlate the statistical variables divided into the resultant variables and the factorial variables, in order to be able to estimate, by the parameters calculated using statistical-econometric methods and to be able to deduce the correlative influence between those two categories of variables. Macroeconomic models are static or equilibrium, meaning that the static model refers to the data up to the moment, and the equilibrium model is a special case, especially of a dynamic system. The situation of tomorrow is established by investigating how the result variables considered under the influence of factors (statistical variables considered) evolved. Based on the agreed static or dynamic models, the differential equations are established (this starts from the term of the use of derivatives), so as to justify the way in which the relations between the two variables are expressed and their evolution over time. Dynamic models are used at the macroeconomic level to determine the National Income, as the net result indicator that is distributed, redistributed and used to meet the needs of those interested. Dynamic models must be considered as a system in which differential equations refer to two successive periods that can be represented graphically. After all, the dynamic models used in the analyzes must start from the fact that a study was carried out previously based on the statistical data series and then on their graphical representation. A number of elements are considered important in agreeing on dynamic macroeconomic models, such as the evolution of the anticipated price cycle that changes the value, but not the content, most of the times, the structural content of the macroeconomic aggregates.

Suggested Citation

  • Constantin ANGHELACHE & Dana Luiza GRIGORESCU & Oana BÎRSAN, 2019. "Main aspects on the nature of dynamic models," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(621), W), pages 129-138, Winter.
  • Handle: RePEc:agr:journl:v:xxvi:y:2019:i:4(621):p:129-138
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    References listed on IDEAS

    as
    1. O. Scaillet, 2004. "Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall," Mathematical Finance, Wiley Blackwell, vol. 14(1), pages 115-129, January.
    2. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
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