Determining seasonal unit roots with bridge estimator: Monte Carlo evidence and an application to convergence hypothesis

References

• Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19 (6):716–23. doi: 10.1109/TAC.1974.1100705.
   Web of Science ®Google Scholar
• Akıncı, M., and Ö. Yılmaz, 2012. Türkiye ile AB arasındaki kişi başına gelir yakınsaması: Farklardaki fark analizi [Per capita income convergence between Turkey and EU: Difference-in-differences analysis]. Finans Politik ve Ekonomik Yorumlar Dergisi, 49, 15– 26.
   Google Scholar
• Bozkurt, H. 2007. Zaman serileri analizi [Time Series Analysis]. Ankara: Ekin Yayınevi.
   Google Scholar
• Breiman, B. L. 1996. Heuristics of instability and stabilization in model selection. The Annals of Statistic 24 (6):2350–83.
   Web of Science ®Google Scholar
• Burridge, P, and A. M. R. Taylor. 2001. On the Properties of Regression-Based Tests for Seasonal Unit Roots in the Presence of Higher-Order Serial Correlation. Journal of Business & Economic Statistics 19 (3):374–9. doi: 10.1198/073500101681019918.
   Web of Science ®Google Scholar
• Caner, M, and K. Knight. 2013. An alternative to unit root tests: Bridge estimators differentiate between nonstationary versus stationary models and select optimal lag. Journal of Statistical Planning and Inference 143 (4):691–715. doi: 10.1016/j.jspi.2012.10.009.
   Web of Science ®Google Scholar
• Chand, S. 2012. On tuning parameter selection of LASSO-type methods- a Monte Carlo study, IBCAST 2012-Proceedings of the 9th International Bhurban Conference on Applied Sciences & Technology, ıslamabad, Pakistan: IEEE.
   Google Scholar
• Chatterjee, A, and S. N. Lahiri. 2011. Bootstrapping Lasso Estimators. Journal of the American Statistical Association 106 (494):608–25. doi: 10.1198/jasa.2011.tm10159.
   Web of Science ®Google Scholar
• Del Barrio Castro, T., D. R. Osborn, and A. R. Taylor. 2016. The Performance of Lag Selection and Detrending Methods for HEGY Seasonal Unit Root Tests. Econometric Reviews 35 (1):122–68. doi: 10.1080/07474938.2013.807710.
   Web of Science ®Google Scholar
• Dickey, D. A., D. P. Hasza, and W. A. Fuller. 1984. Testing for Unit Roots in Seasonal Time Series. Journal of the American Statistical Association 79 (386):355–67. doi: 10.1080/01621459.1984.10478057.
   Web of Science ®Google Scholar
• Efron, B. B., T. Hastie, I. Johnstone, and R. Tibshirani. 2004. Least angle regression. The Annals of Statistics 32 (2):407–99. doi: 10.1214/009053604000000067.
   Web of Science ®Google Scholar
• Enders, W. 2004. Applied econometric time series, 2nd ed., USA (NJ): John Wiley & Sons.
   Google Scholar
• Fan, J, and R. Li. 2001. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties. Journal of the American Statistical Association 96 (456):1348–60. doi: 10.1198/016214501753382273.
   Web of Science ®Google Scholar
• Frank, E.L, and J. H. Friedman. 1993. A Statistical View of Some Chemometrics Regression Tools. Technometrics 35 (2):109–35. doi: 10.1080/00401706.1993.10485033.
   Web of Science ®Google Scholar
• Fu, W. J. 1998. Penalized Regressions: The Bridge versus the Lasso. Journal of Computational and Graphical Statistics 7 (3):397–416. doi: 10.1080/10618600.1998.10474784.
   Web of Science ®Google Scholar
• R. Harris, and R. Sollis, 2003. Applied time series modelling and forecasting, NJ: John Wiley & Sons.
   Google Scholar
• Hoerl, A. E, and R. W. Kennard. 1970. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics 12 (1):55–67. doi: 10.1080/00401706.1970.10488634.
   Web of Science ®Google Scholar
• Huang, B. J., J. L. Horowitz, and S. Ma. 2008. Asymptotic properties of Bridge estimators in sparse high-dimensional regression models. The Annals of Statistics 36 (2):587–613. doi: 10.1214/009053607000000875.
   Web of Science ®Google Scholar
• Hylleberg, S., R. F. Engle, C. Granger, and B. S. Yoo. 1990. Seasonal integration and cointegration. Journal of Econometrics 44 (1-2):215–38. doi: 10.1016/0304-4076(90)90080-D.
   Web of Science ®Google Scholar
• Kirkland, L.-A., F. Kanfer, and S. Millard, 2015. LASSO tuning parameter selection. Proceedings of the 57th Annual Conference of South African Statistical Association, 49– 56. Pretoria, South Africa, SASA.
   Google Scholar
• Knight, K, and W. Fu. 2000. Asymptotics for LASSO-type estimators. The Annals of Statistics 28 (5):1356–78.
   Web of Science ®Google Scholar
• Konat, G., M. Gökçe, and F. Kızılkaya. 2009. AB ülkelerinin yakınsaması: SURADF ve SURKSS birim kök testi [Convergence of EU countries: SURADF and SURKSS unit root test]. Ekoist: Journal of Econometrics and Statistics 31:63–75.
   Google Scholar
• Öztürk, L., S. Ay, and H. Yildirir Keser, 2013. Turkey’s convergence to the EU: Catching up or falling behind?. In Economic and Social Development, Proceedings of 2nd International Scientific Conference, D. Filipovic, A. G. Urnaut, 650–7. Paris, Croatia: Varazdin Development and Entrepreneurship Agency.
   Google Scholar
• Park, C, and Y. J. Yoon. 2011. Bridge regression: Adaptivity and group selection. Journal of Statistical Planning and Inference 141 (11):3506–19. doi: 10.1016/j.jspi.2011.05.004.
   Web of Science ®Google Scholar
• Savacı, S., and B. Karşıyakalı. 2016. Ülkeler arası gelir yakınsamasıanalizi: AB ülkeleri ve Türkiye [Cross-country convergence analysis: EU countries and Turkey]. Akdeniz ııBF Dergisi 33, 237–57.
   Google Scholar
• Schwarz, G. 1978. Estimating the dimension of a model. Annals of Statistics 6 (2):461–4.
   Web of Science ®Google Scholar
• Taylor, A. 1997. On the practical problems of computing seasonal unit root tests. International Journal of Forecasting 13 (3):307–18. doi: 10.1016/S0169-2070(97)00019-8.
   Web of Science ®Google Scholar
• Tibshirani, R. 1996. Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58 (1):267–88. doi: 10.1111/j.2517-6161.1996.tb02080.x.
   Web of Science ®Google Scholar
• Uçar, N, and H. Güler. 2010. Testing stochastic income convergence in seasonal heterogeneous panels. Economic Modelling 27:422–53.
   Web of Science ®Google Scholar
• Wang, H, and C. Leng. 2007. Unified LASSO Estimation by Least Squares Approximation. Journal of the American Statistical Association 102 (479):1039–48. doi: 10.1198/016214507000000509.
   Web of Science ®Google Scholar
• Wang, H., B. Li, and C. Leng. 2009. Shrinkage Tuning Parameter Selection with a Diverging number of Parameters. Journal of the Royal Statistical Society Series B: Statistical Methodology 71 (3):671–83. doi: 10.1111/j.1467-9868.2008.00693.x.
   Web of Science ®Google Scholar
• Wang, H., G. Li, and C.-L. Tsai. 2007. Regression coefficient and autoregressive order shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology 69 (1):63–78. doi: 10.1111/j.1467-9868.2007.00577.x.
   Google Scholar
• Wang, H., R. Li, and C.-L. Tsai. 2007. Tuning parameter selectors for the smoothly clipped absolute deviation method. Biometrika 94 (3):553–68. 19343105. doi: 10.1093/biomet/asm053.
   PubMed Web of Science ®Google Scholar
• Yılmaz, Ö., M. N. Saifi, and M. Akıncı. 2019. G20 ülkeleri ve Türkiye arasında yakınsama hipotezinin testi [Testing of the convergence hypothesis between G20 countries and Turkey]. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 23 (4):1639–56.
   Google Scholar
• Zou, H, and T. Hastie. 2005. Regularization and Variable Selection Via the Elastic Net. Journal of the Royal Statistical Society Series B: Statistical Methodology 67 (2):301–20. doi: 10.1111/j.1467-9868.2005.00503.x.
   Web of Science ®Google Scholar
• Zou, H., T. Hastie, and R. Tibshirani. 2007. On the “degrees of freedom” of the LASSO. The Annals of Statistics 35 (5):2173–92. doi: 10.1214/009053607000000127.
   Web of Science ®Google Scholar