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Econometric Modelling For Simulating The Economic Impact Of Structural Reforms In Romania: A Pilot Project

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Author Info

  • Ruxandra Savonea

    (Ph.D students at the Academy of Economy Studies)

  • Mihaela Ştefănescu

    (Ph.D students at the Academy of Economy Studies)

Abstract

The aim of this research to set up a system which helps to estimate the impact of public funding from projects. The impact depends on all factors affecting demand for the good or service provided under the structural intervention at stake. Thus, the present research provides a pilot econometric model for simulating the main mechanisms that lead to public investment impact from the perspective of 42 counties for the year 2006. The importance of this time span is due to the fact that it represents the end of the second programming period for European Union funds and it reveals the impact of economic structural measures at microeconomic level. Data observed to estimate the linear regression model contain at least 50 observations and the t test, the F test and the coefficients of determination are used to analyse the worthiness of the model after prior estimation of the parameters. Findings showed that the independent element of the model is unbiased, whilst the regression coefficient is biased.

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File URL: http://www.rebe.rau.ro/RePEc/rau/journl/WI09/REBE-WI09-A12.pdf
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Bibliographic Info

Article provided by Romanian-American University in its journal Romanian Economic and Business Review.

Volume (Year): 4 (2009)
Issue (Month): 4 (Winter)
Pages: 103-110

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Handle: RePEc:rau:journl:v:4:y:2009:i:4:p:103-110

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Related research

Keywords: econometric modeling; public spending; evaluation; monitoring; pilot project; micoreconomics;

References

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  1. Juan Mora & Daniel Miles, 2002. "On The Performance Of Nonparametric Specification Tests In Regression Models," Working Papers. Serie AD 2002-13, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  2. Kevin Hoover & Harris Dellas, 2003. "Truth and Robustness in Cross-country Growth Regressions," Working Papers 11, University of California, Davis, Department of Economics.
  3. Amendola, Alessandra & Francq, Christian & Koopman, Siem Jan, 2006. "Special Issue on Nonlinear Modelling and Financial Econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2115-2117, December.
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