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An Analysis for New Institutionality in Science, Technology and Innovation in Colombia Using a Structural Vector Autoregression Model

Author

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  • Clara Inés Pardo Martínez
  • Alexander Cotte Poveda
  • Nicolas Ronderos

Abstract

Purpose: The purpose of this article is to analyze the strengths and the institutionality of the Ministry of Science Technology and Innovation (MSTI) in increasing investments in research and development as well as promoting the generation of knowledge. Design/Methodology/Approach: We use structural vector autoregression (SVAR) and structural vector error correction (SVEC) to examine the effects of institutionality in science, technology and innovation in the Ministry of Science, Technology and Innovation (MSTI) using three variables (i.e., investments in activities of science, technology and innovation (STIA), investments in research and development (R&D) and independence index). Findings: The results indicate that increasing the independence and transparency of the MSTI leads to higher investments in STIA and R&D over time. SVAR and SVEC models were used to assess the robustness and reliability of the results. Practical Implications: The results are important for assessing the effective governance and functionality of the new MSTI and its mission to adopt new policies and instruments that may strengthen science, technology and innovation in Colombia as the country migrates to a knowledge-based society. Originality/Value: In this context, Colombia opted to implement this model; using law 1951 of 2019, the country created this ministry. It is important to analyse the implications and key elements that allow the ministry to operate and achieve better investments to promote research, innovation, and the application of new technologies.

Suggested Citation

  • Clara Inés Pardo Martínez & Alexander Cotte Poveda & Nicolas Ronderos, 2019. "An Analysis for New Institutionality in Science, Technology and Innovation in Colombia Using a Structural Vector Autoregression Model," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 218-228.
    • Handle: RePEc:ers:journl:v:xxii:y:2019:i:2:p:218-228
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    References listed on IDEAS

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    1. Vespignani, Joaquin L. & Ratti, Ronald A., 2016. "Not all international monetary shocks are alike for the Japanese economy," Economic Modelling, Elsevier, vol. 52(PB), pages 822-837.
    2. Eleftherios J. Thalassinos & Evagelos D. Politis, 2011. "International Stock Markets: A Co-integration Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 113-130.
    3. Arias, Jonas E. & Caldara, Dario & Rubio-Ramírez, Juan F., 2019. "The systematic component of monetary policy in SVARs: An agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 1-13.
    4. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    5. Eleftherios J. Thalassinos & Evagelos D. Politis, 2012. "The Evaluation of the USD Currency and the Oil Prices: A Var Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 137-146.
    6. Johansen, Soren, 1992. "Cointegration in partial systems and the efficiency of single-equation analysis," Journal of Econometrics, Elsevier, vol. 52(3), pages 389-402, June.
    7. Galariotis, Emilios C. & Makrichoriti, Panagiota & Spyrou, Spyros, 2016. "Sovereign CDS spread determinants and spill-over effects during financial crisis: A panel VAR approach," Journal of Financial Stability, Elsevier, vol. 26(C), pages 62-77.
    8. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    9. van Aarle, Bas & Garretsen, Harry & Gobbin, Niko, 2003. "Monetary and fiscal policy transmission in the Euro-area: evidence from a structural VAR analysis," Journal of Economics and Business, Elsevier, vol. 55(5-6), pages 609-638.
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    Cited by:

    1. Clara Inés Pardo Martínez & Alexander Cotte Poveda, 2021. "Science, technology, innovation, theory and evidence: the new institutionality in Colombia," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 845-876, June.
    2. Anna Bialek-Jaworska & Robert Faff & Damian Zieba, 2020. "A Liquidity Redistribution Effect in Intercorporate Lending: Evidence from Private Firms in Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 151-175.

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    More about this item

    Keywords

    Science; technology; innovation; institutionality; structural vector autoregression model; Colombia.;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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