Eberhardt, Markus and Bond, Stephen (2009): Cross-section dependence in nonstationary panel models: a novel estimator.
This is the latest version of this item.
Download (319kB) | Preview
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.
|Item Type:||MPRA Paper|
|Original Title:||Cross-section dependence in nonstationary panel models: a novel estimator|
|Keywords:||Nonstationary Panel Econometrics, Common Factor Models, Empirical Analysis of Economic Development|
|Subjects:||O - Economic Development, Technological Change, and Growth > O1 - Economic Development > O11 - Macroeconomic Analyses of Economic Development
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C33 - Models with Panel Data; Longitudinal Data; Spatial Time Series
|Depositing User:||Markus Eberhardt|
|Date Deposited:||16. Oct 2009 07:11|
|Last Modified:||18. Feb 2013 18:03|
Bai, J. (2009). Panel Data Models with Interactive Fixed Effects. Econometrica, 77(4), 1229-1279.
Bai, J., & Kao, C. (2006). On the estimation and inference of a panel cointegration model with cross-sectional dependence. In B. H. Baltagi (Ed.), Panel data econometrics: Theoretical contributions and empirical applications. Amsterdam: Elsevier Science.
Bai, J., Kao, C., & Ng, S. (2009). Panel cointegration with global stochastic trends. Journal of Econometrics, 149(1), 82-99.
Bai, J., & Ng, S. (2008). Large Dimensional Factor Analysis. Foundations and Trends in Econometrics, 3(2), 89-163.
Chudik, A., Pesaran, M. H., & Tosetti, E. (2009). Weak and Strong Cross Section Dependence and Estimation of Large Panels (Cambridge Working Papers in Economics (CWPE) No. 0924). University of Cambridge. (June 2009)
Coakley, J., Fuertes, A. M., & Smith, R. (2006). Unobserved heterogeneity in panel time series models. Computational Statistics & Data Analysis, 50(9), 2361-2380.
Eberhardt, M., & Teal, F. (2008). Modeling Technology and Technological Change in Manufacturing: How do Countries Differ? (CSAE Working Paper, WPS/2008-12). Centre for the Study of African Economies, Department of Economics, University of Oxford.
Eberhardt, M., & Teal, F. (2009). A Common Factor Approach to Spatial Heterogeneity in Agricultural Productivity Analysis (CSAE Working Paper, WPS/2009-05). Centre for the Study of African Economies, Department of Economics, University of Oxford.
FAO. (2007). FAOSTAT (Online database, Rome: FAO). United Nations Food and Agriculture Organisation. (Accessed July 2007)
Hendry, D., & Nielsen, B. (2007). Econometric Modeling: A Likelihood Approach. Princeton University Press.
Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 65(1), 9-15.
Kapetanios, G., Pesaran, M. H., & Yamagata, T. (2009). Panels with Nonstationary Multifactor Error Structures (Tech. Rep.). (unpublished working paper, July 2009, updated version of IZA Discussion Paper #2243)
Pedroni, P. (2007). Social capital, barriers to production and capital shares: implications for the importance of parameter heterogeneity from a nonstationary panel approach. Journal of Applied Econometrics, 22(2), 429-451.
Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967-1012.
Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113.
Phillips, P. C. B., & Moon, H. R. (1999). Linear regression limit theory for nonstationary panel data. Econometrica, 67(5), 1057-1112.
Smith, R. P., & Fuertes, A.-M. (2007). Panel Time Series. (Centre for Microdata Methods and Practice (cemmap) mimeo, April 2007.)
UNIDO. (2004). UNIDO Industrial Statistics 2004 (Online database, Vienna: UNIDO). United Nations Industrial Development Organisation.
Available Versions of this Item
Cross-section dependence in nonstationary panel models: a novel estimator. (deposited 07. Oct 2009 18:50)
- Cross-section dependence in nonstationary panel models: a novel estimator. (deposited 16. Oct 2009 07:11) [Currently Displayed]