| efp {strucchange} | R Documentation |
Computes an empirical fluctuation process according to a specified method from the generalized fluctuation test framework
efp(formula, data, type = <<see below>>, h = 0.15, dynamic = FALSE, rescale = TRUE, tol = 1e-7)
formula |
a symbolic describtion for the model to be tested. |
data |
an optional data frame containing the variables in the model. By
default the variables are taken from the environment which efp is
called from. |
type |
specifies which type of fluctuation process will be computed. For details see below. |
h |
a numeric from interval (0,1) sepcifying the bandwidth. determins the size of the data window relative to sample size (for MOSUM and ME processes only). |
dynamic |
logical. If TRUE the lagged observations are included as
a regressor. |
rescale |
logical. If TRUE the estimates will be standardized by
the regressor matrix of the corresponding subsample according to Kuan & Chen
(1994); if FALSE the whole regressor matrix will be used.
(only if type is either "fluctuation" or "ME") |
tol |
tolerance when solve is used |
If type is one of "Rec-CUSUM", "OLS-CUSUM",
"Rec-MOSUM" or "OLS-MOSUM" the function efp will return a
one-dimensional empiricial process of sums of residuals. Either it will be based
on recursive residuals or on OLS residuals and the process will contain
CUmulative SUMs or MOving SUMs of residuals in a certain data window.
For the MOSUM and ME processes all estimations are done for the
observations in a moving data window, whose size is determined by h and
which is shifted over the whole sample.
If there is a single structural change point t^*, the standard CUSUM path starts to depart from its mean 0 at t^*. The OLS-based CUSUM path will have its peak around t^*. The MOSUM path should have a strong change at t^*.
If type is either "fluctuation" or "ME" a
k-dimensional process will be returned, if k is the number of
regressors in the model, as it is based on recursive OLS estimates of the
regression coefficients or moving OLS estimates respectively.
Both paths should have a peak around t^* if there is a single structural shift.
Achim Zeileis zeileis@ci.tuwien.ac.at
Brown R.L., Durbin J., Evans J.M. (1975), Techniques for testing constancy of regression relationships over time, Journal of the Royal Statistal Society, B, 37, 149-163.
Chu C.-S., Hornik K., Kuan C.-M. (1995), MOSUM tests for parameter constancy, Biometrika, 82, 603-617.
Chu C.-S., Hornik K., Kuan C.-M. (1995), The moving-estimates test for parameter stability, Econometric Theory, 11, 669-720.
Kraemer W., Ploberger W., Alt R. (1988), Testing for structural change in dynamic models, Econometrica, 56, 1355-1369.
Kuan C.-M., Hornik K. (1995), The generalized fluctuation test: A unifying view, Econometric Reviews, 14, 135 - 161.
Kuan C.-M., Chen (1994), Implementing the fluctuation and moving estimates tests in dynamic econometric models, Economics Letters, 44, 235-239.
Ploberger W., Kraemer W. (1992), The CUSUM test with OLS residuals, Econometrica, 60, 271-285.
plot.efp, print.efp,
sctest.efp, boundary.efp
## Load dataset "nhtemp" with average yearly temperatures in New Haven data(nhtemp) ## plot the data plot(nhtemp) ## test the model null hypothesis that the average temperature remains constant ## over the years ## compute OLS-CUSUM fluctuation process temp.cus <- efp(nhtemp ~ 1, type = "OLS-CUSUM") ## plot the process with alternative boundaries plot(temp.cus, alpha = 0.01, alt.boundary = TRUE) ## and calculate the test statistic sctest(temp.cus) ## Load dataset "USIncExp" with income and expenditure in the US ## and choose a suitable subset data(USIncExp) USIncExp2 <- window(USIncExp, start=c(1970,1), end=c(1989,12)) ## test the null hypothesis that the way the income is spent in expenditure ## does not change over time ## compute moving estimates fluctuation process me <- efp(expenditure~income, type="ME", data=USIncExp2, h=0.2) ## plot the two dimensional fluctuation process with boundaries plot(me, functional=NULL) ## and perform the corresponding test sctest(me)