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POVERTY RATE AND ITS DETERMINANTS FOR 12 STATISTICAL REGIONS OF TURKEY: GENERALIZED MAXIMUM ENTROPY APPROACH

Year 2015, Volume: 24 Issue: 2, 337 - 348, 31.10.2015

Abstract

In this study, poverty rate of Turkey on 12 statistical regions (NUTS – 1 level) and some determinants of this rate is modeled by a linear regression model. Average household size, unemployment rate, high school and university enrollment rates, median income and urbanization rate as determinants of poverty rate are used as explanatory variables of this model. It is observed that the ordinary least squares (OLS) produce unstable estimates since the design matrix X is subject to strong multicollinearity. In order to obtain stabilized parameter estimates, two biased estimation methods known in the literature, namely Ridge regression and generalized maximum entropy (GME), are used. Inequality and sign constraints that are required in the context of economic theory are used for the GME estimator. Estimators are compared by their efficiency with the estimated mean squared error values obtained by the bootstrap method.

References

  • Akdeniz, F., Çabuk, A., & Güler, H. (2011). Generalized maximum entropy estimators: applications to the Portland Cement dataset. The Open Statistics and Probability Journal, 3: 13-20. Akdeniz, F., & Kaçıranlar, S. (1995). On the almost unbiased generalized Liu estimator and unbiased estimation of the bias and mse. Communications in Statistics - Theory and Methods, 24(7): 1789-1797. Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics, Wiley, New York. Campbell, R. C., & Hill, C. R. (2001). Maximum entropy estimation in economic models with linear inequality restrictions. Working paper. Çabuk, A. & Akdeniz, F. (2007). İçilişki ve genelleştirilmiş maksimum entropi tahmin edicileri. Journal of Statistical Research, 5(2): 1-19. Efron, B. (1979). Bootstrap methods: another look at the jacknife. The Annals of Statistics, 7(1): 1-26. Golam, K. B. M. (2003). Performance of Some New Ridge Regression Estimators. Communications in Statistics: Simulation & Computation, 32(2): 419-435. Golan, A., Judge, G., & Miller, D. (1996). Maximum entropy econometrics: robust estimation with limited data, John Wiley & Sons, New York. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12(1): 55-67. Hoerl, A. E., Kennard, R.W., & Baldwin, K. F. (1975). Ridge regression: some simulation. Communication in Statistics, 4: 105-123. Jaynes, E. T. (1957). Information theory and statistical mechanics II. Physical Review, 108(2): 171-190. Judge, G., & Golan, A. (1992). Recovering information in the case of ill-posed inverse problems with noise. Working paper, University of California, Berkeley, Department of Agricultural and Resource Economics. Lawless, J. F., & Wang, P. (1976). A simulation study of ridge and other regression estimators. Communications in Statistics: Theory and Methods, 14: 1589-1604. Liu, K. (1993). A new class of biased estimate in linear regression. Communications in Statistics: Theory and Methods, 22(2): 393-402. Liu, K. (2003). Using Liu-type estimator to combat collinearity. Communications in Statistics: Theory and Methods, 32(5): 1009-1020. Pukelsheim, F. (1994). The three sigma rule. The American Statistician, 48(2): 88-91. Ramanathan, R. (2002). Introductory econometrics with applications, Harcourt College Publishers, Fort Worth. Sarkar, N. (1992). A new estimator combining the ridge regression and the restricted least squares methods of estimation, Communications in Statistics: Theory and Methods, 21(7): 1987-2000. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3): 379-423. Vazquez, J. M., Panudulkitti, P., & Timofeev, A. (2009). Urbanization and the poverty level. International Studies Program Working Paper 9-14 (updated), Georgia State University.
Year 2015, Volume: 24 Issue: 2, 337 - 348, 31.10.2015

Abstract

References

  • Akdeniz, F., Çabuk, A., & Güler, H. (2011). Generalized maximum entropy estimators: applications to the Portland Cement dataset. The Open Statistics and Probability Journal, 3: 13-20. Akdeniz, F., & Kaçıranlar, S. (1995). On the almost unbiased generalized Liu estimator and unbiased estimation of the bias and mse. Communications in Statistics - Theory and Methods, 24(7): 1789-1797. Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics, Wiley, New York. Campbell, R. C., & Hill, C. R. (2001). Maximum entropy estimation in economic models with linear inequality restrictions. Working paper. Çabuk, A. & Akdeniz, F. (2007). İçilişki ve genelleştirilmiş maksimum entropi tahmin edicileri. Journal of Statistical Research, 5(2): 1-19. Efron, B. (1979). Bootstrap methods: another look at the jacknife. The Annals of Statistics, 7(1): 1-26. Golam, K. B. M. (2003). Performance of Some New Ridge Regression Estimators. Communications in Statistics: Simulation & Computation, 32(2): 419-435. Golan, A., Judge, G., & Miller, D. (1996). Maximum entropy econometrics: robust estimation with limited data, John Wiley & Sons, New York. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12(1): 55-67. Hoerl, A. E., Kennard, R.W., & Baldwin, K. F. (1975). Ridge regression: some simulation. Communication in Statistics, 4: 105-123. Jaynes, E. T. (1957). Information theory and statistical mechanics II. Physical Review, 108(2): 171-190. Judge, G., & Golan, A. (1992). Recovering information in the case of ill-posed inverse problems with noise. Working paper, University of California, Berkeley, Department of Agricultural and Resource Economics. Lawless, J. F., & Wang, P. (1976). A simulation study of ridge and other regression estimators. Communications in Statistics: Theory and Methods, 14: 1589-1604. Liu, K. (1993). A new class of biased estimate in linear regression. Communications in Statistics: Theory and Methods, 22(2): 393-402. Liu, K. (2003). Using Liu-type estimator to combat collinearity. Communications in Statistics: Theory and Methods, 32(5): 1009-1020. Pukelsheim, F. (1994). The three sigma rule. The American Statistician, 48(2): 88-91. Ramanathan, R. (2002). Introductory econometrics with applications, Harcourt College Publishers, Fort Worth. Sarkar, N. (1992). A new estimator combining the ridge regression and the restricted least squares methods of estimation, Communications in Statistics: Theory and Methods, 21(7): 1987-2000. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3): 379-423. Vazquez, J. M., Panudulkitti, P., & Timofeev, A. (2009). Urbanization and the poverty level. International Studies Program Working Paper 9-14 (updated), Georgia State University.
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Details

Journal Section Makaleler
Authors

Hüseyin Güler

Fikri Akdeniz

Hasan Altan Çabuk

Sibel ÖRK Özel This is me

Publication Date October 31, 2015
Submission Date November 16, 2017
Published in Issue Year 2015 Volume: 24 Issue: 2

Cite

APA Güler, H., Akdeniz, F., Çabuk, H. A., Özel, S. Ö. (2015). POVERTY RATE AND ITS DETERMINANTS FOR 12 STATISTICAL REGIONS OF TURKEY: GENERALIZED MAXIMUM ENTROPY APPROACH. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 24(2), 337-348.