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Cross-section dependence in nonstationary panel models: a novel estimator

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  • Eberhardt, Markus
  • Bond, Stephen

Abstract

This paper uses Monte Carlo simulations to investigate the impact of nonstationarity, parameter heterogeneity and cross-section dependence on estimation and inference in macro panel data. We compare the performance of standard panel estimators with that of our own two-step method (the AMG) and the Pesaran (2006) Common Correlated Effects (CCE) estimators in time-series panels with arguably similar characteristics to those encountered in empirical applications using cross-country macro data. The empirical model adopted leads to an identification problem in standard estimation approaches in the case where the same unobserved common factors drive the evolution of both dependent and independent variables. We replicate the design of two recent Monte Carlo studies on the topic (Coakley et al, 2006; Kapetanios et al, 2009), with results confirming that the Pesaran (2006) CCE approach as well as our own AMG estimator solve this identification problem by accounting for the unobserved common factors in the regression equation. Our investigation however also indicates that simple augmentation with year dummies can do away with most of the bias in standard pooled estimators reported --- a finding which is in stark contrast to the results from earlier empirical work we carried out using cross-country panel data for agriculture and manufacturing (Eberhardt & Teal, 2008; Eberhardt & Teal, 2009). We therefore introduce a number of additional Monte Carlo setups which lead to greater discrepancy in the results between standard (micro-)panel estimators and the novel approaches incorporating cross-section dependence. We further highlight the performance of the pooled OLS estimator with variables in first differences and speculate about the reasons for its favourable results.

Suggested Citation

  • Eberhardt, Markus & Bond, Stephen, 2009. "Cross-section dependence in nonstationary panel models: a novel estimator," MPRA Paper 17692, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:17692
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    References listed on IDEAS

    as
    1. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    2. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
    3. Eberhardt, Markus & Teal, Francis, 2009. "A Common Factor Approach to Spatial Heterogeneity in Agricultural Productivity Analysis," MPRA Paper 15810, University Library of Munich, Germany.
    4. 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.
    5. Bai, Jushan & Kao, Chihwa & Ng, Serena, 2009. "Panel cointegration with global stochastic trends," Journal of Econometrics, Elsevier, vol. 149(1), pages 82-99, April.
    6. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    7. Eberhardt, Markus & Teal, Francis, 2008. "Modeling technology and technological change in manufacturing: how do countries differ?," MPRA Paper 10690, University Library of Munich, Germany.
    8. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    9. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Nonstationary Panel Econometrics; Common Factor Models; Empirical Analysis of Economic Development;

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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