Generalized Reduced Rank Tests using the Singular Value Decomposition
We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson [Annals of Mathematical Statistics (1951), 22, 327–351] sensitivity to the ordering of the variables for the LDU rank statistic of Cragg and Donald [Journal of the American Statistical Association (1996), 91, 1301–1309] and Gill and Lewbel [Journal of the American Statistical Association (1992), 87, 766–776] a limiting distribution that is not a standard chi-squared distribution for the rank statistic of Robin and Smith [Econometric Theory (2000), 16, 151–175] usage of numerical optimization for the objective function statistic of Cragg and Donald [Journal of Econometrics (1997), 76, 223–250] and ignoring the non-negativity restriction on the singular values in Ratsimalahelo [2002, Rank test based on matrix perturbation theory. Unpublished working paper, U.F.R. Science Economique, University de Franche-Comté]. In the non-stationary cointegration case, the limiting distribution of the new rank statistic is identical to that of the Johansen trace statistic.
|Keywords||GMM, cointegration, stochastic discount factor model|
|Persistent URL||dx.doi.org/10.1016/j.jeconom.2005.02.011, hdl.handle.net/1765/13216|
Kleibergen, F.R., & Paap, R.. (2006). Generalized Reduced Rank Tests using the Singular Value Decomposition. Journal of Econometrics, 133(1), 97–126. doi:10.1016/j.jeconom.2005.02.011