Review Article
BibTex RIS Cite

Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data

Year 2022, Volume: 2 Issue: 1, 10 - 22, 30.06.2022

Volkan Sezgin

Abstract

Accurate and rigorous applications of the econometric analysis is crucial when writing a high-quality analytical research paper in social sciences. This article provides the basic framework on how to construct econometric analysis for heterogenous non-stationary dynamic panel datasets. Panel data econometrics is a very broad field, naturally it will not be possible to include all methods in this study. Thus, we present details of the specific selected highly used standard panel data tests and estimations (Im-Pesaran-Shin unit root testing, Pedroni’s cointegration test, panel data ordinary least squares) in this paper and explain why and under which conditions these methods are applied. We employ theoretical formulations of mentioned tests and estimations along with Engle and Granger’s error correction mechanism in order to determine the order of integration and long-run relationship between the panel variables. In summary, we aim to explain basic steps for a straightforward empirical panel data research process for new researchers in social sciences.

Keywords

: Econometric Analysis Panel Data Unit Root Cointegration Panel Data Ordinary Least Squares (PDOLS)

References

  • Anderson, T.W., & Hsiao C. (1982). Formulation and estimation of dynamic models using panel data. Journal of Econometrics, 18, 47-82.
  • Arellano, M., & Bond S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58: 277-297.
  • Balestra, P., & Nerlove M. (1996). Pooling cross-section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34, 585–612.
  • Baltagi, B., & Kao, C. (2000). “Nonstationary panels, cointegration in panels and dynamic panels: A survey. Advances in Econometrics, 15, 7–51.
  • Baltagi, B., Song, S., & Koh, W. (2003). Testing panel data regression models with spatial error correlation. Journal of Econometrics, 117, 123-150.
  • Berrington, A., Smith, P., & Sturgis, P. (2006). An overview of methods for the analysis of panel data. ESRC National Centre for Research Methods (NCRM Methods) Review Papers, 007.
  • Bond, S.R. (2002). Dynamic panel data models: A guide to micro data methods and practice. Portuguese Economic Journal, 1, 141–162.
  • Breitung, J., & Meyer, W. (1994). Testing for unit roots in panel data: Are wages on different bargaining levels cointegrated? Applied Economics, 26, 353–361.
  • Corrado, L., & Fingleton, B. (2011). Where is the economics in spatial econometrics? SIRE Discussion Papers, 2011-02, Scottish Institute for Research in Economics (SIRE).
  • Dickey, D.A., & Fuller, W.A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072. Elhorst J. P. (2014). Spatial Econometrics, Springer Briefs in Regional Science.
  • Engle, R. & Granger, C. (1987). Cointegration and error correction: Representation, estimation and testing. Econometrica, 55, 251-276.
  • Groen, J. J., & Kleibergen, F. (2003). Likelihood-based cointegration analysis in panels of vector error correction models. Journal of Business and Economic Statistics, 21(2), 295–318.
  • Hadri K. (2000). Testing for stationarity in heterogeneous panel data. The Econometrics Journal, 148-161.
  • Harris, R., & Tzavalis, E. (1996). Inference for unit root in dynamic panels. Unpublished manuscript.
  • Harris, D., Harvey, D. I., Leybourne, S. J., & Sakkas, N. D. (2010). Local asymptotic power of the Im-Pesaran-Shin panel unit root test and the impact of initial observations. Econometric Theory, Cambridge University Press, 26(1), 311-324.
  • Heckman, J.J. (1981). Heterogeneity and state dependence. National Bureau of Economic Research (NBER) Chapters, in: Studies in Labor Markets, 91-140.
  • Honoré B. E., & Kyriazidou E. (2000). Panel data discrete choice models with lagged dependent variables. Econometrica, Econometric Society, vol. 68(4), 839-874.
  • Hsiao, C. (2005). Why panel data?” The Singapore Economic Review, 50 (2), 143–154.
  • Im, K., Pesaran, H., & Shin, Y. (1995). Testing for unit roots in heterogeneous panels. Manuscript, University of Cambridge.
  • Im, K., Pesaran, H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.
  • Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90, 1–44.
  • Kao, C., & Chiang M-H. (1997). On the estimation and inference of a cointegrated regression in panel data. Syracuse University manuscript.
  • Kao, C., & Chiang, M.-H. (2001). On the estimation and inference of a cointegrated regression in panel data", Baltagi, B.H., Fomby, T.B. and Carter Hill, R. (Ed.) Nonstationary Panels, Panel Cointegration, and Dynamic Panels (Advances in Econometrics, Vol. 15), Emerald Group Publishing Limited, Bingley, 179-222.
  • Larsson, R., & Lyhagen, J. (1999). Likelihood-based inference in multivariate panel cointegration models. Stockholm School of Economics Working Paper Series in Economics & Finance, No.331.
  • Larsson, R., Lyhagen, J., & Løthgren, M. (2001). Likelihood-based cointegration tests in heterogeneous panels. Econometrics Journal, 4, 109–142.
  • Levin, A., & Lin, C. F. (1993). Unit root tests in panel data: New Results. San Diego, CA: University of California.
  • Levin, A., Lin, C. F., & Chu, C.S. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108, 1–24.
  • Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test: Evidence from simulations and the bootstrap. Oxford Bulletin of Economics and Statistics, 61, 631–652.
  • McCoskey, S., & Kao, C. (1998). A residual-based test of the null of cointegration in panel data. Econometric Reviews, 17, 57–84.
  • Moundigbaye, M., Rea W., & Robert R. W. (2018). Which panel data estimator should I use?: A corrigendum and extension.” Economics Discussion Papers, No. 2017-58, Kiel Institute for the World Economy.
  • Örsal, D. D. K. (2007). Comparison of panel cointegration tests. SFB 649 Discussion Paper, No. 2007,029, Humboldt University of Berlin, Collaborative Research Center 64.
  • Quah, D. (1994). Exploiting cross section variation for unit root inference in dynamic data. Economics Letters, 44, 9–19.
  • Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels.” In B. H. Baltagi, T. B. Fomby, & R. Carter Hill (Eds.), Nonstationary Panels, Panel Cointegration, and Dynamic Panels (Advances in Econometrics, Vol. 15), Bingley: Emerald Group Publishing Limited, 93-130.
  • Pedroni, P. (1995). Panel cointegration; asymptotic and finite sample properties of pooled time series tests, with an application to the PPP Hypothesis. Indiana University Working Papers in Economics, No.95-013.
  • Pedroni, P. (1996). Fully modified OLS for heterogeneous cointegrated panels and the case of purchasing power parity. Working Paper, No. 96–20, Indiana University.
  • Pedroni, P. (1997). On the role of cross-Sectional dependency in panel unit root and panel cointegration exchange rate studies.” Working Paper, Indiana University.
  • Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics & Statistics, 61, 653–670.
  • Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Review of Economics and Statistics, 83(4), 727–731.
  • Pesaran, M. H., Shin Y., & Smith R. P. (1997). Estimating long-run relationships in dynamic heterogeneous panels. DAE Working Papers, Amalgamated Series No. 9721.
  • Pesaran, M. H., Shin Y., & Smith R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94: 621-634.
  • Phillips, P. C. B. & Moon, H. R. (1999). Linear regression limit theory for nonstationary panel data. Econometrica, 67, 1057–1111.
  • Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: Journal of the Econometric Society, 61, 783– 820.

Year 2022, Volume: 2 Issue: 1, 10 - 22, 30.06.2022

Volkan Sezgin

Abstract

References

  • Anderson, T.W., & Hsiao C. (1982). Formulation and estimation of dynamic models using panel data. Journal of Econometrics, 18, 47-82.
  • Arellano, M., & Bond S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58: 277-297.
  • Balestra, P., & Nerlove M. (1996). Pooling cross-section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34, 585–612.
  • Baltagi, B., & Kao, C. (2000). “Nonstationary panels, cointegration in panels and dynamic panels: A survey. Advances in Econometrics, 15, 7–51.
  • Baltagi, B., Song, S., & Koh, W. (2003). Testing panel data regression models with spatial error correlation. Journal of Econometrics, 117, 123-150.
  • Berrington, A., Smith, P., & Sturgis, P. (2006). An overview of methods for the analysis of panel data. ESRC National Centre for Research Methods (NCRM Methods) Review Papers, 007.
  • Bond, S.R. (2002). Dynamic panel data models: A guide to micro data methods and practice. Portuguese Economic Journal, 1, 141–162.
  • Breitung, J., & Meyer, W. (1994). Testing for unit roots in panel data: Are wages on different bargaining levels cointegrated? Applied Economics, 26, 353–361.
  • Corrado, L., & Fingleton, B. (2011). Where is the economics in spatial econometrics? SIRE Discussion Papers, 2011-02, Scottish Institute for Research in Economics (SIRE).
  • Dickey, D.A., & Fuller, W.A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072. Elhorst J. P. (2014). Spatial Econometrics, Springer Briefs in Regional Science.
  • Engle, R. & Granger, C. (1987). Cointegration and error correction: Representation, estimation and testing. Econometrica, 55, 251-276.
  • Groen, J. J., & Kleibergen, F. (2003). Likelihood-based cointegration analysis in panels of vector error correction models. Journal of Business and Economic Statistics, 21(2), 295–318.
  • Hadri K. (2000). Testing for stationarity in heterogeneous panel data. The Econometrics Journal, 148-161.
  • Harris, R., & Tzavalis, E. (1996). Inference for unit root in dynamic panels. Unpublished manuscript.
  • Harris, D., Harvey, D. I., Leybourne, S. J., & Sakkas, N. D. (2010). Local asymptotic power of the Im-Pesaran-Shin panel unit root test and the impact of initial observations. Econometric Theory, Cambridge University Press, 26(1), 311-324.
  • Heckman, J.J. (1981). Heterogeneity and state dependence. National Bureau of Economic Research (NBER) Chapters, in: Studies in Labor Markets, 91-140.
  • Honoré B. E., & Kyriazidou E. (2000). Panel data discrete choice models with lagged dependent variables. Econometrica, Econometric Society, vol. 68(4), 839-874.
  • Hsiao, C. (2005). Why panel data?” The Singapore Economic Review, 50 (2), 143–154.
  • Im, K., Pesaran, H., & Shin, Y. (1995). Testing for unit roots in heterogeneous panels. Manuscript, University of Cambridge.
  • Im, K., Pesaran, H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.
  • Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90, 1–44.
  • Kao, C., & Chiang M-H. (1997). On the estimation and inference of a cointegrated regression in panel data. Syracuse University manuscript.
  • Kao, C., & Chiang, M.-H. (2001). On the estimation and inference of a cointegrated regression in panel data", Baltagi, B.H., Fomby, T.B. and Carter Hill, R. (Ed.) Nonstationary Panels, Panel Cointegration, and Dynamic Panels (Advances in Econometrics, Vol. 15), Emerald Group Publishing Limited, Bingley, 179-222.
  • Larsson, R., & Lyhagen, J. (1999). Likelihood-based inference in multivariate panel cointegration models. Stockholm School of Economics Working Paper Series in Economics & Finance, No.331.
  • Larsson, R., Lyhagen, J., & Løthgren, M. (2001). Likelihood-based cointegration tests in heterogeneous panels. Econometrics Journal, 4, 109–142.
  • Levin, A., & Lin, C. F. (1993). Unit root tests in panel data: New Results. San Diego, CA: University of California.
  • Levin, A., Lin, C. F., & Chu, C.S. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108, 1–24.
  • Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test: Evidence from simulations and the bootstrap. Oxford Bulletin of Economics and Statistics, 61, 631–652.
  • McCoskey, S., & Kao, C. (1998). A residual-based test of the null of cointegration in panel data. Econometric Reviews, 17, 57–84.
  • Moundigbaye, M., Rea W., & Robert R. W. (2018). Which panel data estimator should I use?: A corrigendum and extension.” Economics Discussion Papers, No. 2017-58, Kiel Institute for the World Economy.
  • Örsal, D. D. K. (2007). Comparison of panel cointegration tests. SFB 649 Discussion Paper, No. 2007,029, Humboldt University of Berlin, Collaborative Research Center 64.
  • Quah, D. (1994). Exploiting cross section variation for unit root inference in dynamic data. Economics Letters, 44, 9–19.
  • Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels.” In B. H. Baltagi, T. B. Fomby, & R. Carter Hill (Eds.), Nonstationary Panels, Panel Cointegration, and Dynamic Panels (Advances in Econometrics, Vol. 15), Bingley: Emerald Group Publishing Limited, 93-130.
  • Pedroni, P. (1995). Panel cointegration; asymptotic and finite sample properties of pooled time series tests, with an application to the PPP Hypothesis. Indiana University Working Papers in Economics, No.95-013.
  • Pedroni, P. (1996). Fully modified OLS for heterogeneous cointegrated panels and the case of purchasing power parity. Working Paper, No. 96–20, Indiana University.
  • Pedroni, P. (1997). On the role of cross-Sectional dependency in panel unit root and panel cointegration exchange rate studies.” Working Paper, Indiana University.
  • Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics & Statistics, 61, 653–670.
  • Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Review of Economics and Statistics, 83(4), 727–731.
  • Pesaran, M. H., Shin Y., & Smith R. P. (1997). Estimating long-run relationships in dynamic heterogeneous panels. DAE Working Papers, Amalgamated Series No. 9721.
  • Pesaran, M. H., Shin Y., & Smith R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94: 621-634.
  • Phillips, P. C. B. & Moon, H. R. (1999). Linear regression limit theory for nonstationary panel data. Econometrica, 67, 1057–1111.
  • Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: Journal of the Econometric Society, 61, 783– 820.
There are 42 citations in total.

Details

Primary Language English
Journal Section Reviews
Authors

Volkan Sezgin 0000-0001-7642-7674

Publication Date June 30, 2022
Published in Issue Year 2022 Volume: 2 Issue: 1

Cite

APA Sezgin, V. (2022). Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data. AYBU Business Journal, 2(1), 10-22.
AMA Sezgin V. Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data. AYBU Business Journal. June 2022;2(1):10-22.
Chicago Sezgin, Volkan. “Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data”. AYBU Business Journal 2, no. 1 (June 2022): 10-22.
EndNote Sezgin V (June 1, 2022) Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data. AYBU Business Journal 2 1 10–22.
IEEE V. Sezgin, “Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data”, AYBU Business Journal, vol. 2, no. 1, pp. 10–22, 2022.
ISNAD Sezgin, Volkan. “Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data”. AYBU Business Journal 2/1 (June 2022), 10-22.
JAMA Sezgin V. Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data. AYBU Business Journal. 2022;2:10–22.
MLA Sezgin, Volkan. “Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data”. AYBU Business Journal, vol. 2, no. 1, 2022, pp. 10-22.
Vancouver Sezgin V. Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data. AYBU Business Journal. 2022;2(1):10-22.
 Download Cover Image