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Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu

Year 2016, , 113 - 128, 08.11.2016

Canan Güneş , Mustafa Ünlü , Yasin Büyükkör Şenay Üçdoğruk Birecikli

https://doi.org/10.17233/se.2016.10.006
Cited By: 2

Abstract

Sayma verileri, sadece tam sayı değeri alır ve ekonometrik araştırmalarda sıkça kullanılan veri türlerinden biridir. Bu çalışmanın amacı, Türkiye’de sağlık hizmeti talebini etkileyen faktörleri Türkiye İstatistik Kurumu’nun 2012 yılı Sağlık Araştırmaları veri seti kullanılarak belirlemektir. Talebin göstergesi olarak seçilen doktora gitme sayısı, sayma verisi özelliği göstermektedir. Çalışmada bağımsız değişkenlerden biri olan sağlık durumunun içsel değişken olup olmadığı araştırılacaktır. Genelleştirilmiş Momentler Yöntemi, Alet Değişken Yöntemi ve Sıfır Değer Ağırlıklı Negatif Binom Modeli ile çalışılacak ve politik değerlendirilmelerde bulunulacaktır.

References

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  • Cameron, A.C. & P.K. Trivedi (1986), “Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests”, Journal of Applied Econometrics, 1, 29-54.
  • Cameron, A.C. & P.K. Trivedi & F. Milne & J. Piggott (1988), “A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia”, Review of Economic Studies, 55, 85-106.
  • Cameron, A.C. & P.K. Trivedi (1998), Regression Analysis of Count Data, New York: Cambridge University Press.
  • Cameron, A.C. & P.K. Trivedi (2005), Microeconometrics Methods and Applications, New York: Cambridge University Press.
  • Contoyannis, C. & A. Jones & N. Rice (2004), “The Dynamics of Health in the British Household Panel Survey”, Journal of Applied Econometrics, 19(4), 473-503.
  • Deb, P. & A.M. Holmes (2000), “Estimates of Use and Costs of Behavioural Health Care: A Comparison of Standard and Finite Mixture Models“, Health Economics, 9(6), 475-489.
  • Deb, P. & P.K. Trivedi (1997), “Demand for Medical Care by the Elderly: A Finite Mixture Approach”, Journal of Applied Econometrics, 12, 313-336.
  • Deb, P. & P.K. Trivedi (2006), “Specification and Simulated Likelihood Estimation of a Non- Normal Treatment-Outcome Model with Selection: Application to Health Care Utilization”, Econometrics Journal, 9(2), 307-331.
  • Desmaris, B.A. & J.J. Harden (2013), “Testing for Zero Inflation in Count Models: Bias Correction fort he Vuong Test”, The Stata Journal, 13(4), 810-835.
  • Geraci, A. & D. Fabbri & C. Monfardini (2014), “Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two Stage Residual Inclusion Work?”, Quaderni-Working Papers DSE, No. 921.
  • Greene, W. (2007), “Functional Forms for the Negative Binomial Model for Count Data”, Economic Letters, 99, 585-590.
  • Gurmu, S. (1997), “Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization“, Journal of Applied Econometrics, 12(3), 225-43.
  • Hansen, L.P. (1982), “Large Sample Properties of Generalized Method of Moments Estimators”, Econometrica, 50(4), 1029-1054.
  • Hausman, J. & B. Hall & Z. Griliches (1984), “Economic Models for Count Data with an Application to the Patents-R&D Relationship”, Econometrica, 52, 909-938.
  • Hidayat, B. & S. Pokhrel (2010), “The Selection of an Appropriate Count Data Model for Modelling Health Insurance and Health Care Demand: Case of Indonesia”, International Journal of Environmental Research and Public Health, 7, 9-27.
  • Hilbe, J.M. (2011), Negative Binomial Regression, Second Ed, New York: Cambridge University Press.
  • Jones, A. & O. O’Donnell (2002), Econometric Analysis of Health Data, New York: John Wiley&Sons Ltd.
  • Kozumi, H. (2002), “A Bayesian Analysis of Endogenous Switching Models for Count Data”, Journal of the Japanese Statistical Society, 32(3), 141-154.
  • Kung, C.C. (2012), “Relationship between Education and Hospital Visit”, International Journal of Statistics in Medical Research, 1, 51-54.
  • Long, J.S. (1997), Regression Models for Categorical and Limited Dependent Variables, California: Sage Publications.
  • Malonzo, E.M. & E.D. Prantilla (2007), “Count Model Estimates of Health Care Demand in Davao City”, 10th National Convention on Statistics, October 1-2, 2007, 02.pdf>, 20.02.2015.
  • Masuhara, H. (2008), “Semi-Nonparametric Count Data Estimation with an Endogenous Binary Variable”, Economics Bulletin, 42(3), 1-13.
  • Mullahy, J. (1997), “Instrumental Variable Estimation of Count Data Models: Applications to Models of Cigarette Smoking Behavior”, The Review of Economics and Statistics, 79(4), 586-593.
  • Munkin, M.K. & P.K. Trivedi (1999), “Simulated Maximum Likelihood Estimation of Multivariate Mixed-Poisson Regression Models with Application“, Econometrics Journal, 2(1), 29- 48.
  • Pagan, A.R. & D. Hall (1983), “Diagnostic Tests as Residual Analysis”, Econometric Reviews, 2(2), 159-218.
  • Pohlmeier, W. & V. Ulrich (1995), “An Econometric Model of the Two-Part Decision Making Process in the Demand for Health Care”, Journal of Human Resources, 30, 339-61.
  • Riphahn, R.T. & A. Wambach & A. Million (2003), “Incentive Effects in the Demand for Health Care: A Bivariate Panel Count Data Estimation”, Journal of Applied Econometrics, 18(4), 387-405.
  • Romeu, A. & M. Vera-Hernandez (2005), “Counts with an Endogenous Binary Regressor: A Series Expansion Approach”, Econometrics Journal, 8(1), 1-22.
  • Sargan, J.D. (1975), “Testing for Misspecification after Estimating Using Instrumental Variables”, Econometrica, 26(3), 393-415.
  • Staub, K.E. (2009), “Simple Tests for Exogeneity of a Binary Explanatory Variable in Count Data Regression Models”, Socioeconomics Institute University of Zurich Working Paper, No. 0904.
  • Terza, J.V. & A. Basu & P.J. Rathouz (2008) “Two-Stage Residual Inclusion Estimation: Addressing Endogeneity in Health Econometric Modeling”, Journal of Health Economics, 27(3), 531-543.
  • Wang, P. & I. Cockburn & L. Puterman (1998), “Analysis of Patent Data-A Mixed Poisson Regression Model Approach”, Journal of Business and Economic Statistics, 16(1), 27- 41.
  • Windmeijer, F.A.G. & J.M.C. Santos Silva (1997), “Endogeneity in Count Data Models: An Application to Demand for Health Care”, Journal of Applied Econometrics, 12(3), 281- 294.
  • Winkelmann, R. (2004), “Health Care Reform and the Number of Doctor Visits-An Econometric Analysis”, Journal of Applied Econometrics, 19, 455-472.
  • Wooldridge, J.M. (2014), “Quasi-Maxumum Likelihood Estimation and Testing for Nonlinear Models with Endogenous Explanatory Variables”, Journal of Econometrics, 182(1), 226- 234.
  • Yaylalı, M. & S. Kaynak & Z. Karaca (2012), “Sağlık Hizmetleri Talebi: Erzurum İlinde Bir Araştırma”, Ege Akademik Bakış, 12 (4), 563-573.
  • Zimmer D. (2010), “Health Insurance and Health Care Demand Among the Selfemployed” Journal of Labor Research, 31, 1-19.

Year 2016, , 113 - 128, 08.11.2016

Canan Güneş , Mustafa Ünlü , Yasin Büyükkör Şenay Üçdoğruk Birecikli

https://doi.org/10.17233/se.2016.10.006
Cited By: 2

Abstract

References

  • Asplund, M. & R. Sandin (1999), “The Number of Firms and Production Capacity in Relation to Market Size”, Journal of Industrial Economics, 47(1), 69-85.
  • Cameron, A.C. & P.K. Trivedi (1986), “Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests”, Journal of Applied Econometrics, 1, 29-54.
  • Cameron, A.C. & P.K. Trivedi & F. Milne & J. Piggott (1988), “A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia”, Review of Economic Studies, 55, 85-106.
  • Cameron, A.C. & P.K. Trivedi (1998), Regression Analysis of Count Data, New York: Cambridge University Press.
  • Cameron, A.C. & P.K. Trivedi (2005), Microeconometrics Methods and Applications, New York: Cambridge University Press.
  • Contoyannis, C. & A. Jones & N. Rice (2004), “The Dynamics of Health in the British Household Panel Survey”, Journal of Applied Econometrics, 19(4), 473-503.
  • Deb, P. & A.M. Holmes (2000), “Estimates of Use and Costs of Behavioural Health Care: A Comparison of Standard and Finite Mixture Models“, Health Economics, 9(6), 475-489.
  • Deb, P. & P.K. Trivedi (1997), “Demand for Medical Care by the Elderly: A Finite Mixture Approach”, Journal of Applied Econometrics, 12, 313-336.
  • Deb, P. & P.K. Trivedi (2006), “Specification and Simulated Likelihood Estimation of a Non- Normal Treatment-Outcome Model with Selection: Application to Health Care Utilization”, Econometrics Journal, 9(2), 307-331.
  • Desmaris, B.A. & J.J. Harden (2013), “Testing for Zero Inflation in Count Models: Bias Correction fort he Vuong Test”, The Stata Journal, 13(4), 810-835.
  • Geraci, A. & D. Fabbri & C. Monfardini (2014), “Testing Exogeneity of Multinomial Regressors in Count Data Models: Does Two Stage Residual Inclusion Work?”, Quaderni-Working Papers DSE, No. 921.
  • Greene, W. (2007), “Functional Forms for the Negative Binomial Model for Count Data”, Economic Letters, 99, 585-590.
  • Gurmu, S. (1997), “Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization“, Journal of Applied Econometrics, 12(3), 225-43.
  • Hansen, L.P. (1982), “Large Sample Properties of Generalized Method of Moments Estimators”, Econometrica, 50(4), 1029-1054.
  • Hausman, J. & B. Hall & Z. Griliches (1984), “Economic Models for Count Data with an Application to the Patents-R&D Relationship”, Econometrica, 52, 909-938.
  • Hidayat, B. & S. Pokhrel (2010), “The Selection of an Appropriate Count Data Model for Modelling Health Insurance and Health Care Demand: Case of Indonesia”, International Journal of Environmental Research and Public Health, 7, 9-27.
  • Hilbe, J.M. (2011), Negative Binomial Regression, Second Ed, New York: Cambridge University Press.
  • Jones, A. & O. O’Donnell (2002), Econometric Analysis of Health Data, New York: John Wiley&Sons Ltd.
  • Kozumi, H. (2002), “A Bayesian Analysis of Endogenous Switching Models for Count Data”, Journal of the Japanese Statistical Society, 32(3), 141-154.
  • Kung, C.C. (2012), “Relationship between Education and Hospital Visit”, International Journal of Statistics in Medical Research, 1, 51-54.
  • Long, J.S. (1997), Regression Models for Categorical and Limited Dependent Variables, California: Sage Publications.
  • Malonzo, E.M. & E.D. Prantilla (2007), “Count Model Estimates of Health Care Demand in Davao City”, 10th National Convention on Statistics, October 1-2, 2007, 02.pdf>, 20.02.2015.
  • Masuhara, H. (2008), “Semi-Nonparametric Count Data Estimation with an Endogenous Binary Variable”, Economics Bulletin, 42(3), 1-13.
  • Mullahy, J. (1997), “Instrumental Variable Estimation of Count Data Models: Applications to Models of Cigarette Smoking Behavior”, The Review of Economics and Statistics, 79(4), 586-593.
  • Munkin, M.K. & P.K. Trivedi (1999), “Simulated Maximum Likelihood Estimation of Multivariate Mixed-Poisson Regression Models with Application“, Econometrics Journal, 2(1), 29- 48.
  • Pagan, A.R. & D. Hall (1983), “Diagnostic Tests as Residual Analysis”, Econometric Reviews, 2(2), 159-218.
  • Pohlmeier, W. & V. Ulrich (1995), “An Econometric Model of the Two-Part Decision Making Process in the Demand for Health Care”, Journal of Human Resources, 30, 339-61.
  • Riphahn, R.T. & A. Wambach & A. Million (2003), “Incentive Effects in the Demand for Health Care: A Bivariate Panel Count Data Estimation”, Journal of Applied Econometrics, 18(4), 387-405.
  • Romeu, A. & M. Vera-Hernandez (2005), “Counts with an Endogenous Binary Regressor: A Series Expansion Approach”, Econometrics Journal, 8(1), 1-22.
  • Sargan, J.D. (1975), “Testing for Misspecification after Estimating Using Instrumental Variables”, Econometrica, 26(3), 393-415.
  • Staub, K.E. (2009), “Simple Tests for Exogeneity of a Binary Explanatory Variable in Count Data Regression Models”, Socioeconomics Institute University of Zurich Working Paper, No. 0904.
  • Terza, J.V. & A. Basu & P.J. Rathouz (2008) “Two-Stage Residual Inclusion Estimation: Addressing Endogeneity in Health Econometric Modeling”, Journal of Health Economics, 27(3), 531-543.
  • Wang, P. & I. Cockburn & L. Puterman (1998), “Analysis of Patent Data-A Mixed Poisson Regression Model Approach”, Journal of Business and Economic Statistics, 16(1), 27- 41.
  • Windmeijer, F.A.G. & J.M.C. Santos Silva (1997), “Endogeneity in Count Data Models: An Application to Demand for Health Care”, Journal of Applied Econometrics, 12(3), 281- 294.
  • Winkelmann, R. (2004), “Health Care Reform and the Number of Doctor Visits-An Econometric Analysis”, Journal of Applied Econometrics, 19, 455-472.
  • Wooldridge, J.M. (2014), “Quasi-Maxumum Likelihood Estimation and Testing for Nonlinear Models with Endogenous Explanatory Variables”, Journal of Econometrics, 182(1), 226- 234.
  • Yaylalı, M. & S. Kaynak & Z. Karaca (2012), “Sağlık Hizmetleri Talebi: Erzurum İlinde Bir Araştırma”, Ege Akademik Bakış, 12 (4), 563-573.
  • Zimmer D. (2010), “Health Insurance and Health Care Demand Among the Selfemployed” Journal of Labor Research, 31, 1-19.
There are 38 citations in total.

Details

Journal Section Articles
Authors

Canan Güneş

Mustafa Ünlü

Yasin Büyükkör This is me

Şenay Üçdoğruk Birecikli

Publication Date November 8, 2016
Submission Date October 20, 2016
Published in Issue Year 2016

Cite

APA Güneş, C., Ünlü, M., Büyükkör, Y., Üçdoğruk Birecikli, Ş. (2016). Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu. Sosyoekonomi, 24(30), 113-128. https://doi.org/10.17233/se.2016.10.006
AMA Güneş C, Ünlü M, Büyükkör Y, Üçdoğruk Birecikli Ş. Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu. Sosyoekonomi. October 2016;24(30):113-128. doi:10.17233/se.2016.10.006
Chicago Güneş, Canan, Mustafa Ünlü, Yasin Büyükkör, and Şenay Üçdoğruk Birecikli. “Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu”. Sosyoekonomi 24, no. 30 (October 2016): 113-28. https://doi.org/10.17233/se.2016.10.006.
EndNote Güneş C, Ünlü M, Büyükkör Y, Üçdoğruk Birecikli Ş (October 1, 2016) Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu. Sosyoekonomi 24 30 113–128.
IEEE C. Güneş, M. Ünlü, Y. Büyükkör, and Ş. Üçdoğruk Birecikli, “Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu”, Sosyoekonomi, vol. 24, no. 30, pp. 113–128, 2016, doi: 10.17233/se.2016.10.006.
ISNAD Güneş, Canan et al. “Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu”. Sosyoekonomi 24/30 (October 2016), 113-128. https://doi.org/10.17233/se.2016.10.006.
JAMA Güneş C, Ünlü M, Büyükkör Y, Üçdoğruk Birecikli Ş. Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu. Sosyoekonomi. 2016;24:113–128.
MLA Güneş, Canan et al. “Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu”. Sosyoekonomi, vol. 24, no. 30, 2016, pp. 113-28, doi:10.17233/se.2016.10.006.
Vancouver Güneş C, Ünlü M, Büyükkör Y, Üçdoğruk Birecikli Ş. Türkiye’de Sağlık Hizmetleri Talebinin Sayma Veri Modelleriyle İncelenmesi: İçsellik Sorunu. Sosyoekonomi. 2016;24(30):113-28.

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