IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/87600.html
   My bibliography  Save this paper

European higher education policies and the problem of estimating a complex model with a small cross-section

Author

Listed:
  • Marconi, Gabriele

Abstract

This paper discusses the components on components regression, a statistical technique suitable for explorative analyses of small datasets containing multiple independent, mediating and dependent variables. This method is compared to ordinary least squares and principal component regression by means of discussion of their properties and the assumptions underlying these estimators, a simulation and an empirical application to European higher education policy, and economic innovativeness in 32 countries. In the datasets used in this paper, the components on components regression yields more precise estimates of the coefficients of association between independent, mediating and dependent variables, compared to ordinary least squares. Compared to the principal components regression, it leads to a more parsimonious empirical model. The simulation also shows that the standard errors of the coefficients estimated with the components on components regression can be obtained by bootstrapping.

Suggested Citation

  • Marconi, Gabriele, 2014. "European higher education policies and the problem of estimating a complex model with a small cross-section," MPRA Paper 87600, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:87600
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/87600/8/MPRA_paper_87600.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    2. Cecile Hoareau & Jo Ritzen & Gabriele Marconi, 2013. "Higher education and economic innovation, a comparison of European countries," IZA Journal of European Labor Studies, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 2(1), pages 1-24, December.
    3. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    4. Reinhold Kosfeld & Jørgen Lauridsen, 2008. "Factor analysis regression," Statistical Papers, Springer, vol. 49(4), pages 653-667, October.
    5. Hoareau, Cécile & Ritzen, Jo & Marconi, Gabriele, 2012. "The State of University Policy for Progress in Europe," IZA Policy Papers 51, Institute of Labor Economics (IZA).
    6. Henk Kiers & Age Smilde, 2007. "A comparison of various methods for multivariate regression with highly collinear variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 16(2), pages 193-228, August.
    7. Pagan, Adrian, 1984. "Econometric Issues in the Analysis of Regressions with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(1), pages 221-247, February.
    8. Xinfeng Chang & Hu Yang, 2012. "Combining two-parameter and principal component regression estimators," Statistical Papers, Springer, vol. 53(3), pages 549-562, August.
    9. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages 2-18, September.
    10. Louis Guttman, 1954. "Some necessary conditions for common-factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 19(2), pages 149-161, June.
    11. Raymond Hicks & Dustin Tingley, 2011. "Causal mediation analysis," Stata Journal, StataCorp LP, vol. 11(4), pages 605-619, December.
    12. Perobelli, Fernando Salgueiro & Oliveira, Caio Cézar Calheiros de, 2013. "Energy development potential: An analysis of Brazil," Energy Policy, Elsevier, vol. 59(C), pages 683-701.
    13. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    14. Westerlund, J. & Urbain, J.R.Y.J., 2011. "Cross sectional averages or principal components?," Research Memorandum 053, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    15. Ron C. Mittelhammer & John L. Baritelle, 1977. "On Two Strategies for Choosing Principal Components in Regression Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 59(2), pages 336-343.
    16. Tor Georg Jakobsen & Indra De Soysa & Jo Jakobsen, 2013. "Why do poor countries suffer costly conflict? Unpacking per capita income and the onset of civil war," Conflict Management and Peace Science, Peace Science Society (International), vol. 30(2), pages 140-160, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    2. Paola Zuccolotto, 2012. "Principal component analysis with interval imputed missing values," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 1-23, January.
    3. Jinho Bae & Chang-Jin Kim & Dong Kim, 2012. "The evolution of the monetary policy regimes in the U.S," Empirical Economics, Springer, vol. 43(2), pages 617-649, October.
    4. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    5. Asongu, Simplice A. & Andrés, Antonio R., 2020. "Trajectories of knowledge economy in SSA and MENA countries," Technology in Society, Elsevier, vol. 63(C).
    6. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Sep 2023.
    7. Simplice A. Asongu & Nicholas M. Odhiambo, 2019. "Governance, capital flight and industrialisation in Africa," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-22, December.
    8. John C. Bluedorn & Christopher Bowdler, 2010. "The Empirics of International Monetary Transmission: Identification and the Impossible Trinity," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(4), pages 679-713, June.
    9. Smets, Frank & Beyer, Robert C. M., 2015. "Labour market adjustments in Europe and the US: How different?," Working Paper Series 1767, European Central Bank.
    10. Domenico Giannone & Michele Lenza, 2010. "The Feldstein-Horioka Fact," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 6(1), pages 103-117.
    11. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
    12. Amavilah, Voxi & Asongu, Simplice A. & Andrés, Antonio R., 2017. "Effects of globalization on peace and stability: Implications for governance and the knowledge economy of African countries," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 91-103.
    13. Giuseppe D'Onza & Alessandra Rigolini & Francesco Brotini, 2016. "Esiste una strategia di Internal Auditing che crea valore? Un?analisi empirica del contesto italiano," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2016(1), pages 125-148.
    14. Francesco Curreri & Giacomo Fiumara & Maria Gabriella Xibilia, 2020. "Input Selection Methods for Soft Sensor Design: A Survey," Future Internet, MDPI, vol. 12(6), pages 1-24, June.
    15. Massimo Guidolin & Manuela Pedio, 2020. "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers 20140, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    16. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
    17. Gaofeng Jia & Alexandros Taflanidis & Norberto Nadal-Caraballo & Jeffrey Melby & Andrew Kennedy & Jane Smith, 2016. "Surrogate modeling for peak or time-dependent storm surge prediction over an extended coastal region using an existing database of synthetic storms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 909-938, March.
    18. Dorota Toczydlowska & Gareth W. Peters & Man Chung Fung & Pavel V. Shevchenko, 2017. "Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principal Components," Risks, MDPI, vol. 5(3), pages 1-77, July.
    19. Chen, Tao & Martin, Elaine & Montague, Gary, 2009. "Robust probabilistic PCA with missing data and contribution analysis for outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3706-3716, August.
    20. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.

    More about this item

    Keywords

    principal components regression – OLS – small sample – explorative research – higher education policies – Montecarlo simulation;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:87600. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.