Thank you, things seem to be clearer :-) > Hansen extended this to the linear regression model and proposed to either > compute one test statistic per parameter (which you can do with the "parm" > argument of gefp) or a joint statistic for all parameters. Hansen included > in "all" parameters also the variance,
The "parm" argument of gefp is a nice feature, but what is about the significance level in test statistic compuation (sctest)? Is there multiple testing correction applied or should I rather use for this case the double max statistic as recommended below? An excerpt from page 5 of the paper "A Unified Approach to Structural Change Tests Based obn F Statistics, OLS Residuals, and ML Scores" (Achim Zeileis): Hansen (1992) suggests to compute this statistic for the full process efp(t) to test all coefficients simultaneously and also for each component of the process (efp(t))j (denoting the j-th component of the process efp(t), j = 1, . . . , k) individually to assess which parameter causes the instability. *Note, that this approach leads to a violation of the significance level of the procedure if no multiple testing correction is applied.* This can be avoided if a functional is applied to the empirical fluctuation process which aggregates over time first yielding k independent test statistics (see Zeileis and Hornik 2003, for more details). -- View this message in context: http://r.789695.n4.nabble.com/strucchange-Nyblom-Hansen-Test-tp3887208p3940055.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
