l1line | l1line computes the least absolute deviation line from scatterplot data. It gives the estimate b0 and b1 that minimizes sum_i=1,n |y_i - b0 - b1 x_i |. |
leafnum | Gives the number of leaves (terminal nodes) in a regression tree. |
lgamma |
lgamma computes logarithm of the gamma function.
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lgenci | auxiliary quantlet for cointegration |
library |
library loads an xplore library
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lik | |
line | Convenient function for plotting results. Similar to plot but uses lines instead. |
linreg | linreg computes the Generalized Least Squares estimate for the coefficients of a linear model. |
linregbs | linregbs computes a backward elimination of a multiple linear regression model. |
linregfs | linregfs computes a simple forward selection for a multiple linear regression model. |
linregfs2 | linregfs2 computes a forward selection for a multiple linear regression model. |
linregopt | sets optional parameters for linregbs, linregfs2 and linregstep |
linregres | linregres computes some residual analysis for a linear regression. |
linregstep | linregstep computes a stepwise regression for a multiple linear regression model. |
list | list generates lists from given objects. If an object is temporary the name of the component is el<position>, otherwise the name of the object at this position. |
lo |
Calculation of the Lo statistic for long-range dependence.
The first argument of the quantlet is the series, the second
optional argument is the vector of truncation lags of the
autocorrelation consistent variance estimator. If the second
optional argument is missing, the vector of truncation lags
is set to m = 5, 10, 25, 50. The quantlet returns the estimated
statistic with its corresponding order. If the estimated statistic
is outside the interval (0.809, 1.862), which is the 95 percent
confidence interval for no long-memory,
a star symbol * is displayed in the third column. The other
critical values are in Lo's paper.
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lobrob | Semiparametric test for I(0) of a time series against fractional alternatives, i.e., long-memory and antipersistence. The test is semiparametric in the sense that it does not depend on a specific parametric form of the spectrum in the neighborhood of the zero frequency. The first argument of the function is the series. The second optional argument is the vector of bandwidth, i.e., the parameter specifying the number of harmonic frequencies around zero to be considered. By default, the macro uses the automatic bandwidth given in Lobato and Robinson. If the user provides his own vector of bandwidths, then the function returns the value of the test for each component of the bandwidth vector. If the value of the test is in the lower tail of the standard normal distribution, the null hypothesis of I(0) is rejected against the alternative that the series displays long-memory. If the value of the test is in the upper tail of the standard normal distribution, the null hypothesis I(0) is rejected against the alternative that the series is antipersistent. |
locpol |
locpol computes the local polynomial estimator.
It is using the quartic kernel.
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locpoldis |
locpoldis computes the local polynomial estimator without mixed terms
but allows for including a linear part in the regression model.
It is using the quartic kernel.
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log | log returns the natural logarithm of the elements of an array. |
log10 | log10 returns the logarithm base 10 of the elements of an array. |
log1p | log1p computes the natural logarithm of (1+x) accurately even for tiny x. |
logarithmic | performs a logarithmic transformation of the selected variables in ISTA. The transformed variables can replace the original ones or can be appended on the end of data.x. The type is set automatically to continuous. |
logfile | If a command was typed in the command line of the input window and <Enter> was pressed then the command is written to the logfile. |
logit | performs a logit (log((x/(1-x))) transformation of the selected variables in ISTA. The transformed variables can replace the original ones or can be appended on the end of data.x. The type is set automatically to continuous. |
looreg | computes the Nadaraya-Watson leave-one-out estimator without binning using the quartic kernel. Prior to estimation, looreg sorts the data. The sorted data, along with the sorted leave-one-out regression estimates, are returned as an output. |
lorenz | calculates the measures of concentration of Gini and Herfindahl |
lowess | lowess computes the robust locally weighted regression. Fitted values are computed at each of the given values x. |
lpderest | estimates the q-th derivative of a regression function using local polynomial kernel regression. The computation uses WARPing. |
lpderrot | determines a rule-of-thumb bandwidth for univariate local polynomial derivatives estimation using the Quartic kernel. |
lpderxest | estimates the q-th derivative of a regression function using local polynomial kernel regression with Quartic kernel. |
lpdist | computes the so-called Lp-distances between the rows of a data matrix. In the case p=1 (absolute metric) or p=2 (euclidean metric) one should favour the function DISTANCE. |
lpregest | estimates a regression function using local polynomial kernel regression. The computation uses WARPing. |
lpregrot | determines a rule-of-thumb bandwidth for univariate local polynomial kernel regression using the Quartic kernel. |
lpregxest | estimates a univariate regression function using local polynomial kernel regression with Quartic kernel. |
lprotint | lprotint computes the integral of the (p+1)st derivative of a polynomial of order (p+3), this function is used to find rule-of-thumb bandwidth for local polynomial regression and derivative estimation |
lregestp | estimates a multivariate regression function using local polynomial kernel regression. The computation uses WARPing. |
lregxestp | estimates a multivariate regression function using local polynomial kernel regression with Quartic kernel. |
lrseev | LRS estimator for EV model |
lts | Computes the least trimmed squares estimate for the coefficients of a linear model. |
ludecomp | ludecomp computes the lu decomposition of a matrix. |
lvtest | This quantlet tests for significance of a subset or of the whole set of continuous regresssors in a nonparametric regression. |