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A Survey about Smooth Transition Panel Data Analysis

Author

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  • Tolga Omay

    (Cankaya University, Department of Banking and Finance)

Abstract

In this study we are introducing a literature survey about the panel smooth transition regression models. This type of modeling has been emerged from two different strand of literature where the first one is nonlinear time series the other is the panel data analysis. Both of these fields have tackled with different type of biases in estimation process. Therefore, combining these two fields constitutes different problems in estimation. Hence, instead of giving the studies in chronological order, we preferred to explain papers with respect to problems which they have solved. In this order, first we analyze the categories of different models. For example, there are several categorizations in the panel data estimation with respect to time and cross-section dimension. Therefore every category has its own biases depending on the time and cross-section dimension. On the other hand the dynamic structure of the panel data is another important determinant in which we can classify the biases. Hence, the static and dynamic panel smooth transition models are also discussed separately in this study. Finally, smooth transition models has its’ own categories, hence we are giving these categories under the panel categorization as well.

Suggested Citation

  • Tolga Omay, 2014. "A Survey about Smooth Transition Panel Data Analysis," Econometrics Letters, Bilimsel Mektuplar Organizasyonu (Scientific letters), vol. 1(1), pages 18-29.
  • Handle: RePEc:bmo:bmoart:v:1:y:2014:i:1:p:18-29
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    References listed on IDEAS

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    1. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    2. Fouquau, Julien & Hurlin, Christophe & Rabaud, Isabelle, 2008. "The Feldstein-Horioka puzzle: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 25(2), pages 284-299, March.
    3. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    4. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    5. Fouquau, Julien & Hurlin, Christophe & Rabaud, Isabelle, 2008. "The Feldstein-Horioka puzzle: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 25(2), pages 284-299, March.
    6. Vasilis Sarafidis & Donald Robertson, 2009. "On the impact of error cross-sectional dependence in short dynamic panel estimation," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 62-81, March.
    7. Fouquau, Julien & Hurlin, Christophe & Rabaud, Isabelle, 2008. "The Feldstein-Horioka puzzle: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 25(2), pages 284-299, March.
    8. Jerry Coakley & Ana-Maria Fuertes & Ron Smith, 2002. "A Principal Components Approach to Cross-Section Dependence in Panels," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B5-3, International Conferences on Panel Data.
    9. Fouquau, Julien & Hurlin, Christophe & Rabaud, Isabelle, 2008. "The Feldstein-Horioka puzzle: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 25(2), pages 284-299, March.
    10. Fouquau, Julien & Hurlin, Christophe & Rabaud, Isabelle, 2008. "The Feldstein-Horioka puzzle: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 25(2), pages 284-299, March.
    11. Omay, Tolga & Öznur Kan, Elif, 2010. "Re-examining the threshold effects in the inflation-growth nexus with cross-sectionally dependent non-linear panel: Evidence from six industrialized economies," Economic Modelling, Elsevier, vol. 27(5), pages 996-1005, September.
    12. Pesaran, M. Hashem, 2004. "General Diagnostic Tests for Cross Section Dependence in Panels," IZA Discussion Papers 1240, Institute for the Study of Labor (IZA).
    13. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    14. Arellano, Manuel, 1989. "A note on the Anderson-Hsiao estimator for panel data," Economics Letters, Elsevier, vol. 31(4), pages 337-341, December.
    15. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    16. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    17. Sarafidis, Vasilis & Yamagata, Takashi, 2010. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors under Cross-sectional Dependence," MPRA Paper 25182, University Library of Munich, Germany.
    18. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    19. Fouquau, Julien & Hurlin, Christophe & Rabaud, Isabelle, 2008. "The Feldstein-Horioka puzzle: A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 25(2), pages 284-299, March.
    20. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    21. Ucar, Nuri & Omay, Tolga, 2009. "Testing for unit root in nonlinear heterogeneous panels," Economics Letters, Elsevier, vol. 104(1), pages 5-8, July.
    22. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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    Cited by:

    1. repec:bmo:bmoart:v:4:y:2017:i:1:p:1-17 is not listed on IDEAS
    2. repec:spr:empeco:v:54:y:2018:i:3:d:10.1007_s00181-017-1237-2 is not listed on IDEAS
    3. Reneé van Eyden & Tolga Omay & Rangan Gupta, 2015. "Inflation-Growth Nexus in Africa: Evidence from a Pooled CCE Multiple Regime Panel Smooth Transition Model," Working Papers 201504, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Panel smooth transition data; Bias; Large-moderate-small panel data; static panel data; dynamic panel data; Logistic; exponential; time varying;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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