slm package enables to fit linear models on datasets considering the dependence between the observations.
Most of the functions are based on the functions and methods of
lm, with the same arguments and the same format for the outputs.
slmfunction, in "slm-main.R"
slm function is the main function of this package. Its architecture is the same as the
but it takes into account the possible correlation between the observations. To estimate the asymptotic covariance matrix of
the least squares estimator, several approaches are available: "fitAR" calls the
cov_AR function, "spectralproj" the
cov_spectralproj function, "kernel" the
cov_efromovich function and "select" the
cov_select function. The "hac" method uses the
and more precisely, the method described by Andrews (1991) and Zeileis (2004).
slm, in "slm-method.R"
slm function has several associated methods, which are the same as for the
The available methods are:
The package has some auxiliary functions, in particular some predefined kernels for the kernel method of
slm function: the
trapeze kernel, the triangle kernel and the rectangular kernel. The user can also define his own kernel and put it in the argument
kernel_fonc in the
generative_process function generates some stationary processes.
generative_model function generates some designs.
The package contains a dataset "shan". This dataset comes from a study about fine particle pollution in the city of Shanghai. The data are available on the following website https://archive.ics.uci.edu/ml/datasets/PM2.5+Data+of+Five+Chinese+Cities#.
D. Andrews (1991). Heteroskedasticity and autocorrelation consistent covariant matrix estimation. Econometrica, 59(3), 817-858.
E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. arXiv preprint arXiv:1906.06583. https://arxiv.org/abs/1906.06583.
A. Zeileis (2004). Econometric computing with HC and HAC covariance matrix estimators.
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