An Analysis of Competitiveness of the EU Countries Using the Dependent Mixture Model

• Autorzy: Ewa Genge
• Języki publikacji: EN

Abstrakty

EN

As European Union countries are facing particularly difficult economic management decisions with challenging political and social ramifications, we try to find groups of countries which face a similar challenge of improving their competitiveness indicators. We focus on the dependent mixture model which additionally allows us to investigate the dynamic pattern of the competitiveness index and pillars organized into three sub-indices time series. This methodology will provide an opportunity to investigate which countries feature a similar level of competitiveness stability (are able to sustain their level of competitiveness) and which have similar regime-switching propensities. These results may contribute to the current policy discussion on measures for achieving the sustainable competitiveness of the European Union economies, EU strategy and reform programmes in separate member states.(original abstract)

Słowa kluczowe

PL

Konkurencyjność; Modele Markowa

EN

Competitiveness; Markov models

• Czasopismo: Argumenta Oeconomica
• Rocznik: 2017
• Numer: nr 2 (39)
• Strony: 111-127

Twórcy

autor

Ewa Genge

• University of Economics in Katowice, Poland

Bibliografia

• Ahearn, M., Culver, D., Schoney, R., Usefulness and Limitations of COP Estimates for Evaluating International Competitiveness: A Comparison of Canadian and U.S. Wheat, American, "Journal of Agricultural Economics", Vol. 72, No. 5, pp. 1283-1291, 1990.
• Akaike, H., A New Look at Statistical Model Identification, "IEEE Transactions on Automatic Control", Vol. 19, pp. 716-723, 1974.
• Buzzigoli, L., Viviani A., Firm and System Competitiveness: Problems of Definition, Measurement and Analysis, [in:] Viviani, A., Firms and System Competitiveness. Firenze University Press, Italy, 2009.
• Cappe, O., Moulines, E., Ryden, T., Inference in Hidden Markov Models. Springer-Verlag, New York, 2005.
• Collins, L. M., Lanza, S. T., Latent Class and Latent Transition Analysis with Applications in the Social, Behavioral, and Health Sciences. John Wiley and Sons, 2011.
• Costa, M., De Angelis, L., A Dynamic Analysis of Stock Markets through Multivariate Latent Markov Models [in:] Ingrassia, S., Rocci, R., Vichi, M., New Perspectives in Statistical Modeling and Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization, pp. 311-318. Springer-Verlag, 2011.
• Dempster, A. P., Laird, N. P., Rubin, D. B., Maximum Likelihood for Incomplete Data via the EM Algorithm (with discussion), "Journal of the Royal Statistical Society", Vol. 39, pp. 1-38, 1977.
• Dicken, P., Global Shift: Transforming the World Economy. Paul Chapman, London, 1998.
• European Commission, European Competitiveness Report 2008, European Commission, 2009.
• Frühwirth-Schnatter, S., Finite Mixture and Markov Switching Model, Springer-Verlag, New York, 2006.
• Frühwirth-Schnatter, S., Kaufmann, S., Model-Based Clustering of Multiple Time Series, "Journal of Business & Economic Statistics, American Statistical Association", Vol. 26, pp. 78-89, 2008.
• Garelli, S., Competitiveness of Nations: The Fundamentals, IMD World Competitiveness Yearbook, 2006.
• Ghysels, E., On the Periodic Structure of the Business Cycle, "Journal of Business and Economic Statistics", Vol. 12 (3), pp. 289-298, 1994.
• Hatzichronoglou, T., Globalisation and Competitiveness: Relevant Indicators, "OECD Science, Technology and Industry Working Papers", Paris OECD Publishing, Organisation for Economic Co-operation and Development, 1996.
• Haque, I., Technology and Competitiveness, [in:] Haque, I., Trade, Technology and International Competitiveness. The World Bank, Washington, D. C., 1995.
• Kim, C. J., Dynamic Linear Models with Markov-switching, "Journal of Econometrics", Vol. 60, pp. 1-22, 1994.
• Krogh, A., An Introduction to Hidden Markov Models for Biological Sequences [in:] Salzberg, L. S., Searls, B. D., Kasif, S. (eds.), Computational Methods in Molecular Biology, pp. 45-63. Elsevier, Amsterdam, 1998.
• Lall, S., Competitiveness, Technology and Skills. Edward Elgar, Cheltenham, 2001.
• Langenheine, R., Van de Pol, F., Fitting Higher Order Markov Chains, "Methods of Psychological Research Online", Vol. 5, pp. 32-55, 2001.
• Lazarsfeld, P. F., The Logical and Mathematical Foundations of Latent Structure Analysis, [in:] Stouffer, A. S., Measurement and Prediction, the American Soldier: Studies in Social Psychology in World War II, pp. 362-412. Princeton University Press, Princeton,1950a.
• Lazarsfeld, P. F., The Interpretation and Computation of Some Latent Structures, [in:] Stouffer, A. S., Measurement and Prediction, the American Soldier: Studies in Social Psychology in World War II, pp. 413-472, Princeton University Press, Princeton, 1950b.
• Lazarsfeld, P. F., Henry, N. W., Latent Structure Analysis. Houghton Mifflin, Boston, 1968.
• Leroux, B. G., Puterman, M. L., Maximum-Penalized-Likelihood Estimation for Independent and Markov-Dependent Mixture Models, "Biometrics", Vol. 48, 1992.
• McCutcheon, A. L., Latent class analysis, Beverly Hills, Sage University Paper series on Quantitative Applications in the Social Sciences, series No. 07-064, 1987.
• Orszaghova, L., Savelin, L., Schudel, W., External Competitiveness of EU Candidate Countries, Occasional Paper Series, No 141, available at http://www.ecb.europa.eu, accessed in January 2013.
• Otranto, E., Clustering Heteroskedastic Time Series by Model-based Procedures, "Computational Statistics & Data Analysis", Vol. 52, pp. 4685-4698, 2008.
• Rabiner, L. R., A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of IEEE, Vol. 77, No 2, pp. 267-295, 1989.
• Schmittmann, V. D., Visser, I., Raijmakers, M. E. J., Multiple Learning Modes in the Development of Rule-based Category-learning Task Performance, "Neuropsychologia", Vol. 44, No 11, pp. 2079-2091, 2006.
• Schwarz, G., Estimating the Dimension of a Model, "Annals of Statistics", Vol. 6, pp. 461- -464, 1978.
• Sharples, J., Cost of Production and Productivity in Analyzing Trade and Competitiveness, "American Journal of Agricultural Economics", Vol. 72, No. 5, pp. 1278-1282, 1990.
• Visser, I., Speekenbrink, M., Package 'depmixS4', available at https://cran.r-project.org/ web/packages/ depmixS4/depmixS4.pdf.
• Visser, I., Speekenbrink, M. (2010), depmixS4: An R Package for Hidden Markov Models, "Journal of Statistical Software", Vol. 36, No 7, pp. 1-21, available at http://www. jstatsoft.org/v36/i07/.
• Wickens, T. D., Models for Behavior: Stochastic Processes in Psychology. W. H. Freeman and Company, San Francisco,1982.
• World Economic Forum (2012), "Global Competitiveness Report 2012-2013", available at http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2012-13.pdf.
• Zucchini, W., MacDonald, I., Hidden Markov Models for Time Series: An Introduction Using R. Monographs on Statistics and Applied Probability, Boca Raton, CRC Press, 2009.

Identyfikatory

DOI

10.15611/aoe.2017.2.05