References
- Bai, J. (2003). Inferential theory for factor models of large dimensions. Econometrica 71:135–171.
- Bai, Z. (1999). Methodologies in spectral analysis of large dimensional random matrices, a review. Statistica Sinica 9: 611–677.
- Bai, Z., Jiang, D., Yao, J. F., Zheng, S. (2009). Corrections to LRT on large-dimensional covariance matrix by RMT. Annals of Statistics 37:3822–3840.
- Baltagi, B. H., Feng, Q., Kao, C. (2011). Testing for sphericity in a fixed effects panel data model. Econometrics Journal 14:25–47.
- Baltagi, B. H., Feng, Q., Kao, C. (2012). A Lagrange multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics 170:164–177.
- Baltagi, B. H., Maasoumi, E. (2013). An overview of dependence in cross-section, time-series and panel data. Econometric Reviews 32:543–546.
- Breusch, T. S., Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies 47:239–253.
- Cai, T. T., Ma, Z. (2013). Optimal hypothesis testing for high dimensional covariance matrices. Bernoulli 19:2359–2388.
- Chen, S. X., Zhang, L. X., Zhong, P. S. (2010). Tests for high-dimensional covariance matrices. Journal of the American Statistical Association 105:810–819.
- Chudik, A., Pesaran, M. H. (2015). Large panel data models with cross-sectional dependence: A survey. In: Baltagi, B. H., ed. Chapter 1 in the Oxford Handbook of Panel Data. Oxford: Oxford University Press, pp. 3–45.
- Dufour, J. M., Khalaf, L. (2002). Exact tests for contemporaneous correlation of disturbances in seemingly unrelated regressions. Journal of Econometrics 106:143–170.
- Frees, E. W. (1995). Assessing cross-sectional correlation in panel data. Journal of Econometrics 69:393–414.
- Geman, S. (1980). A limit theorem for the norm of random matrices. Annals of Probability 8:252–261.
- John, S. (1971). Some optimal multivariate tests. Biometrika 58:123–127.
- John, S. (1972). The distribution of a statistic used for testing sphericity of normal distributions. Biometrika 59:169–173.
- Johnstone, I. M. (2001). On the distribution of the largest principal component. Annals of Statistics 29:295–327.
- Johnstone, I. M., Lu, A. Y. (2009). On consistency and sparsity for principal components analysis in high dimensions. Journal of the American Statistical Association 104:682–693.
- Ledoit, O., Wolf, M. (2002). Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Annals of Statistics 30:1081–1102.
- Onatski, A. (2012). Asymptotics of the principal components estimator of large factor models with weakly influential factors. Journal of Econometrics 168:244–258.
- Onatski, A., Moreira, M. J., Hallin, M. (2013). Asymptotic power of sphericity tests for high-dimensional data. Annals of Statistics, 41:1204–1231.
- Onatski, A., Moreira, M. J., Hallin, M. (2014). Signal detection in high dimension: The multispiked case. Annals of Statistics 42:225–254.
- Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Working Paper, University of Cambridge&USC.
- Pesaran, M. H. (2012). Testing weak cross-sectional dependence in large panels, CESifo working paper 3800.
- Pesaran, M. H., Ullah, A., Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. Econometrics Journal 11:105–127.
- Phillips, P. C. B., Moon, H. R. (1999). Linear regression limit theory for nonstationary panel data. Econometrica 67: 1057–1111.
- Srivastava, M. S. (2005). Some tests concerning the covariance matrix in high dimensional data. Journal of the Japan Statistical Society 35:251–272.
- Staiger, D., Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica 65: 557–586.
- Wang, C. (2014). Asymptotic power of likelihood ratio tests for high dimensional data. Statistics & Probability Letters 88:184–189.