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RANKTEST: Stata module to test the rank of a matrix using the Kleibergen-Paap rk statistic

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Author Info
Frank Kleibergen () (Brown University)
Mark E Schaffer () (Heriot-Watt University)

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Abstract

ranktest implements the Kleibergen-Paap (2006) rk test for the rank of a matrix. Tests of the rank of a matrix have many practical applications. For example, in econometrics the requirement for identification is the rank condition, which states that a particular matrix must be of full column rank. Another example from econometrics concerns cointegration in vector autoregressive (VAR) models; the Johansen trace test is a test of a rank of a particular matrix. The traditional test of the rank of a matrix for the standard (stationary) case is the Anderson (1951) canonical correlations test. If we denote one list of variables as Y and a second as Z, and we calculate the squared canonical correlations between Y and Z, the LM form of the Anderson test, where the null hypothesis is that the matrix of correlations or regression parameters B between Y and Z has rank(B)=r, is N times the sum of the r+1 largest squared canonical correlations. A large test statistic and rejection of the null indicates that the matrix has rank at least r+1. The Cragg-Donald (1993) statistic is a closely related Wald test for the rank of a matrix. Both the Anderson and Cragg-Donald tests require the assumption that the covariance matrix has a Kronecker form; when this is not so, e.g., when disturbances are heteroskedastic or autocorrelated, the test statistics are no longer valid. The Kleibergen-Paap (2006) rk statistic is a generalization of the Anderson canonical correlation rank test to the case of a non-Kronecker covariance matrix. The implementation in ranktest will calculate rk statistics that are robust to various forms of heteroskedasticity, autocorrelation, and clustering.

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File URL: http://fmwww.bc.edu/repec/bocode/r/ranktest.ado
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File URL: http://fmwww.bc.edu/repec/bocode/r/ranktest.hlp
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Publisher Info
Software component provided by Boston College Department of Economics in its series Statistical Software Components with number S456865.

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Programming language: Stata
Requires: Stata version 9.2
Date of creation: 30 Aug 2007
Date of revision: 05 May 2008
Handle: RePEc:boc:bocode:s456865

Note: This module may be installed from within Stata by typing "ssc install ranktest". Windows users should not attempt to download these files with a web browser.
Contact details of provider:
Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA
Phone: 617-552-3670
Fax: +1-617-552-2308
Email:
Web page: http://fmwww.bc.edu/EC/
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For technical questions regarding this item, or to correct its listing, contact: (Christopher F Baum).

Related research
Keywords: matrix; rank; collinearity; cointegration;

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. C. Lucarelli & M. E. Bontempi & C. Mazzoli & A. G. Quaranta, 2009. "Pre-trade transparency on the Italian Stock Exchange: a trade size model on panel data," Working Papers 678, Dipartimento Scienze Economiche, Universita' di Bologna. [Downloadable!]
  2. Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2007. "Enhanced routines for instrumental variables/GMM estimation and testing," Boston College Working Papers in Economics 667, Boston College Department of Economics, revised 05 Sep 2007. [Downloadable!]
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This page was last updated on 2009-11-21.


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