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Dergiler / AYBU Business Journal / 2022 / Cilt: 2 - Sayı: 1

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

Volkan SEZGİN
Sayfa
10–22
Yayın Tarihi
2022-06-30
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In summary, we aim to explain basic steps for a straightforward empirical panel data research process for new researchers in social sciences.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Accurate and rigorous applications of the econometric analysis is crucial when writing a high-quality analytical research paper in social sciences.
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Temel Bulgular & Veri:This article provides the basic framework on how to construct econometric analysis for heterogenous non-stationary dynamic panel datasets.
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Sonuç & Katkı:In summary, we aim to explain basic steps for a straightforward empirical panel data research process for new researchers in social sciences.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:: Econometric Analysis · Panel Data · Unit Root

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.

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Bu Makaleye Atıf Yap

SEZGİN, V. (2022). Exploring a Scientific Research Methodology in Social Sciences: Steps for Analyzing Non-Stationary Heterogeneous Panel Data. AYBU Business Journal, Cilt: 2 - Sayı: 1, 10–22. https://ajindex.com/articles/725753

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Kaynakça (14)

  1. Anderson, T.W., & Hsiao C. (1982). Formulation and estimation of dynamic models using panel data. Journal of Econometrics, 18, 47-82.
  2. 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.
  3. 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.
  4. Baltagi, B., & Kao, C. (2000). “Nonstationary panels, cointegration in panels and dynamic panels: A survey. Advances in Econometrics, 15, 7–51.
  5. Baltagi, B., Song, S., & Koh, W. (2003). Testing panel data regression models with spatial error correlation. Journal of Econometrics, 117, 123-150.
  6. 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.
  7. Breitung, J., & Meyer, W. (1994). Testing for unit roots in panel data: Are wages on different bargaining levels cointegrated? Applied Economics, 26, 353–361.
  8. 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.
  9. Elhorst J. P. (2014).
  10. Spatial Econometrics, Springer Briefs in Regional Science. Engle, R. & Granger, C. (1987). Cointegration and error correction: Representation, estimation and testing. Econometrica, 55, 251-276.
  11. 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.
  12. Hadri K. (2000). Testing for stationarity in heterogeneous panel data. The Econometrics Journal, 148-161.
  13. 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.
  14. Heckman, J.J. (1981). Heterogeneity and state dependence. National Bureau of Economic