Marconi, Gabriele (2014): European higher education policies and the problem of estimating a complex model with a small cross-section. Published in: ROA Dissertation Series No. 19 (21 January 2015)
Preview |
PDF
MPRA_paper_87600.pdf Download (1MB) | Preview |
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.
Item Type: | MPRA Paper |
---|---|
Original Title: | European higher education policies and the problem of estimating a complex model with a small cross-section |
Language: | English |
Keywords: | principal components regression – OLS – small sample – explorative research – higher education policies – Montecarlo simulation |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J24 - Human Capital ; Skills ; Occupational Choice ; Labor Productivity O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O38 - Government Policy |
Item ID: | 87600 |
Depositing User: | Gabriele Marconi |
Date Deposited: | 24 Jul 2018 10:43 |
Last Modified: | 26 Sep 2019 08:48 |
References: | Basilevsky, A. (1994). Statistical Factor Analysis and Related Methods. New York: Wiley. Belsley, D. A., Kuh, E., and Welsch, R. E. (2004). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity (2nd ed.). Hoboken (NJ): John Wiley & Sons. Bernanke, B. S., and Boivin, J. (2003). Monetary Policy in a Data-rich Environment. Journal of Monetary Economics, 50, 525–546. Braun, A., Müller, K., and Schmeiser, H. (2013). What Drives Insurers’ Demand for Cat Bond Investments? Evidence from a Pan-European Survey. The Geneva Papers on Risk and Insurance - Issues and Practice, 38, 580–611. Chang, X., and Yang, H. (2012). Combining Two-Parameter and Principal Component Regression Estimators. Statistical Papers, 53, 549–562. CHEPS. (2008). Progress in Higher Education Reform across Europe - Governance and Funding Reform - Volume 2: Methodology, Performance Data, Literature Survey, National System Analyses and Case Studies (Vol. 2). Brussels: European Commission. Corazzini, L., Grazzi, M., and Nicolini, M. (2011). Social Capital and Growth in Brazilian Municipalities. In P. Nijkamp & I. Siedschlag (Eds.), Innovation, Growth and Competitiveness - Dynamic Regions in the Knowledge-Based World Economy (pp. 195–217). Berlin: Springer. Davidson, R., and MacKinnon, J. G. (2006). Bootstrap Methods in Econometrics. In T. C. Mills & K. Patterson (Eds.), Palgrave Handbook of Econometrics (pp. 812–838). New York: Palgrave Macmillan. Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., … Lautenbach, S. (2013). Collinearity: a Review of Methods to Deal With It and a Simulation Study Evaluating Their Performance. Ecography, 36, 27–46. Efron, B., and Tibshirani, R. (1986). Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy. Statistical Science, 1, 54–75. Faber, K., and Kowalski, B. R. (1997). Propagation of Measurement Errors for the Validation of Predictions Obtained by Principal Component Regression and Partial Least Squares. Journal of Chemometrics, 11, 181–238. Greene, W. H. . (2003). Econometric Analysis. Prentice Hall (Vol. 97). Upper Saddle River (NJ): Prentice Hall. Guttman, L. (1954). Some Necessary Conditions for Common-factor Analysis. Psychometrika, 19, 149– 161. Hicks, R., and Tingley, D. (2011). Causal Mediation Analysis. The Stata Journal, 11, 1–15. Hoareau, C., Ritzen, J., and Marconi, G. (2012). The State of University Policy for Progress in Europe - Technical report. Maastricht: EEU. Hoareau, C., Ritzen, J., and Marconi, G. (2013). Higher Education and Economic Innovation, a Comparison of European Countries. IZA Journal of European Labor Studies, 2, 24. Hoerl, A. E., and Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12, 55–67. Hoerl, R. W., Schuenemeyer, J. H., and Hoerl, A. E. (1986). A Simulation of Biased Estimation and Subset Selection Regression Techniques. Technometrics, 28, 369–380. Hoyle, R. H. (1999). Statistical Strategies for Small Sample Research. (R. H. Hoyle, Ed.). Thousand Oaks (CA): Sage Publications. Jakobsen, T. G., De Soysa, I., and Jakobsen, J. (2013). Why Do Poor Countries Suffer Costly Conflict? Unpacking per Capita Income and the Onset of Civil War. Conflict Management and Peace Science, 30, 140–160. Jambu, M. (1991). Exploratory and Multivariate Data Analysis. London: Academic Press Limited. Jolliffe, I. T. (2002). Principal Component Analysis. New York: Springer. Kaiser, H. F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement, 20, 141–151. Kaplan, D. (2000). Structural Equations Modeling: Foundations and Extensions. Thousand Oaks (CA): Sage Publications. Kiers, H. A. L., and Smilde, A. K. (2007). A Comparison of Various Methods for Multivariate Regression with Highly Collinear Variables. Statistical Methods and Applications, 16, 193–228. Kosfeld, R., and Lauridsen, J. (2008). Factor Analysis Regression. Statistical Papers, 49, 653–667. MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis. New York: Erlbaum. Merola, G. M., and Abraham, B. (2001). Dimensionality Reduction Approach to Multivariate Prediction. The Canadian Journal of Statistics / La Revue Canadienne de Statistique, 29, 191–200. Mittelhammer, R. C., and Baritelle, J. L. (1977). On Two Strategies for Choosing Principal Components in Regression Analysis. American Journal of Agricultural Economics, 59, 336–343. Pagan, A. (1984). Econometric Issues in the Analysis of Regressions with Generated Regressors. International Economic Review, 25, 221–247. Perobelli, F. S., and Oliveira, C. C. C. De. (2013). Energy Development Potential: An Analysis of Brazil. Energy Policy, 59, 683–701. Stebbins, R. A. (2001). Exploratory Research in the Social Sciences. Thousand Oaks (CA): Sage Publications. Stone, R. (1947). On the Interdependence of Blocks of Transactions. Journal of the Royal Statistical Society, 9, 1–45. Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., and Lauro, C. (2005). PLS Path Modeling. Computational Statistics and Data Analysis, 48, 159–205. Tipping, M. E., and Bishop, C. M. (1999). Probabilistic Principal Component Analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61, 611–622. Vinzi, V. E., Chin, W. W., Henseler, J., and Wang, H. (2009). Handbook of Partial Least Squares: Concepts, Methods and Applications. Handbook of Partial Least Squares. Berlin: Springer. Westerlund, J., and Urbain, J. (2011). Cross Sectional Averages or Principal Components? (No. 11/053). Maastricht: METEOR. Williams, L. J., Edwards, J. R., and Vandenberg, R. J. (2003). Recent Advances in Causal Modeling Methods for Organizational and Management Research. Journal of Management, 29, 903–936. Wooldridge, J. D. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge (MA): MIT Press. Yung, Y.-F., and Chan, W. (1999). Statistical Analyses Using Bootstrapping: Concepts and Implementation. In R. H. Hoyle (Ed.), Statistical Strategies for Small Sample Research (pp. 81–105). Thousand Oaks (CA): Sage Publications. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/87600 |