abs | Computes the absolute values of the elements of an array. |
acf | computes the autocorrelation function for time series |
acfplot | plots the autocorrelation function of a time series. |
acos | Returns the arccosine of the elements of an array. |
acosh | Returns the inverse hyperbolic cosine of the elements of an array. |
adap | performs an adaptive K-means cluster analysis |
adaptive | performs an adaptive K-means cluster analysis with appropriate (adaptive) multivariate graphic using the principal components |
addbutton |
Adds a button with some functionality to the plot menu.
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adddata |
Adds graphics to already plotted ones.
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addfnci | auxiliary macro for cointegration |
addlist | Adds components to an existing list or creates a new list with specific components. |
addr | creates one hidden layer network |
adedis | adedis computes estimates of the slope coefficients in a single index model. The coefficents of the continuous variables are estimated by (an average of) dwade (density-weighted average derivtive) estimates. The coefficients of the disrete explanatory variables are estimated by the method proposed in Horowitz and Haerdle, JASA 1996. |
adeind | indirect average derivative estimation using binning |
adeslp | slope estimation of average derivatives using binning |
aerlb | computes the Lachenbruch-Mickey unbiased estimate of the actual discrimination error rate |
agen | auxiliary macro for VAR models |
agglom | performs hierarchical cluster analysis. |
american | starting program to calculate option prices for american options |
andrews | andrews calculates the semiparametric estimator proposed by Andrews and Schafgans (1994) of the intercept coefficients of the outcome equation in a sample selection model. |
andrewscurv | shows andrews curves for the principal components of the selected variables. The number of gridpoints can be chosen be the user and one or more group variables (disctrete type) can be selected. |
andrewsrech | performs a pca on the selected variables and creates a composed object of the andrews curves of the principal . components |
ann | is a tool to run a feed-forward neural network |
annarchtest | This macro calculates either the Lagrange Multiplier (LM) form or the T-Rsquare (TR2) form of a test for conditional heteroskedasticity based on Artificial Neural Networks. The first argument of the function is the vector of residuals, the second optional argument is the order of the test, the third optional argument is the number of hidden units of the Neural Network. The second optional argument is either a vector or a scalar. If no second argument is provided, the default orders are 2, 3, 4, and 5. The third argument may be either a vector, or a scalar. If both second and third arguments are vectors, the test will be calculated for all combinations of orders and hidden units. If no third argument is provided, the number of hidden units by default is set to 3. The fourth argument is the form of the test. This argument is a string of characters, which can be either "LM" or "TR2". The default fourth argument is "LM", i.e., the Lagrange Multiplier form. The macro returns in the first column the order of the test, in the second column the number of hidden units, in the third column the value of the test, in the fourth column the 95% critical value of the null hypothesis for that order, and in the fifth column the P-value of the test. |
annlintest | This macro calculates the neural network test for neglected nonlinearity proposed by Lee, White and Granger (1993). This statistic is evaluated from uncentered squared multiple correlation of an auxiliary regression in which we regress the residuals of a linear regression on the regressors of this regression and the principal components of a nonlinear transformation of the regressors. The first argument of the macro is the series y. The second argument is either a set of regressors X if the series is regressed on X, or the number of lags if the series is regressed on its lagged realizations. The macro adds automatically a constant if the constant term is missing in X. If the series is regressed on its past realizations, then the second argument, i.e., the number of lags, may be a vector. In that case, the corresponding linear models and statistics for neglected nonlinearity are computed. The third optional argument is the number of hidden units of the neural network, which should be greater than or equal to 3. The fourth optional argument is the number of principal components used in the auxiliary regression. The number of principal components should be less than the number of corresponding hidden units. The default third argument is the vector (10,20), the default fourth argument is the vector (2,3). If the series is regressed on a set of exogeneous variables X, the macro returns the number of principal components used in the auxiliary regression, the value of the test, the 95% critical value for the null hypothesis of the test, and the P value of the test. If the series is regressed on its past realizations, the number of lagged explanatory variables is also displayed. |
aorBgen | auxiliary macro for full VAR model analysis |
append | Appends an object to the specified list. If the appended object is temporary, the component gets the name el<position>. If the first argument is not a list, it will be changed to a list with itself as first component. |
arbitrage | calculates an arbitrage table considering put and calls with the same strike price |
archest | estimates a GARCH process with mean zero by QMLE |
archtest | This macro calculates either the Lagrange Multiplier (LM) form or the R squared (TR2) form of Engle's ARCH test. The first argument of the function is the vector of residuals, the second optional argument is the lag order of the test. This ; second argument may be either a scalar, or a vector. In the later case, the test is evaluated for all the order components of the vector. If this second optional argument is not provided, the default lag orders are 2, 3, 4, and 5. The third optional argument is the form of the test. This argument is a string, which can be either "LM" or "TR2". The default third argument is "LM", i,e., the LM form. The macro returns in the first column the order of the test, in the second column the value of the test, in the third column the 95% critical value of the null hypothesis for that order, and in the fourth column the P-value of the test. |
arcsin | performs a arcus sinus 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 automatically set continuous. |
armacls | estimates an autoregressive moving average process with mean zero by conditional least squares |
armalik | estimates an ARMA(1,1) process with mean zero by maximum likelihood using the innovation algorithm |
arofva | auxiliary macro for full VAR model analysis |
aseq | Computes an additive sequence. |
asin | Returns the arcsine of the elements of an array. |
asinh | Returns the inverse hyperbolic sine of the elements of an array. |
asset | help program to calculate option prices for bitree |
atan | Returns the arctangent in radian of the elements of an array. |
atan2 | Returns the angle in radian between the positive part of the x-axis and the line with origin in (0,0) which contains the point (x, y). |
atanh | Returns the inverse hyperbolic tangent of the elements of an array. |
atof | Converts strings to numbers. |
axesoff |
All plots created afterwards have invisible axes.
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axeson |
All plots created afterwards have visible axes.
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