Auxiliary routine for rICfil: solves - if possible - by explicit integration and Newton-Algorithm the following equation: E [|AX|^2 \min{1,b/|AX|}]=1, E [|AX|^2 \min{1,b^2/|AX|^2}]=(1+e)p for X ~ N_p (0,unit(p))
Auxiliary routine for rICfil: solves - if possible - by explicit integration and Newton-Algorithm the following equations: (separate clipping in 1 dimension of normal scores X=X1+X2, X1,X2 indep.) E [A (X1 \min{1,b/|AX1|} +X2) (X1+X2) ]=1, E [A^2 (X1 \min{1,b/|AX1|} +X2)^2]=(1+e) /(S1+S2) for
computes the (sample) autocorrelation function for the time series x. The output vector starts with the autocorrelation r_0 at lag length 0 (and thus is equal to 1). The next entries are r_1, r_2 ... r_k.
plots the (sample) autocorrelation function of the time series x. The plot starts with the autocorrelation r_0 at lag length 0 (and is thus equal to 1).
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 derivative) estimates. The coefficients of the discrete explanatory variables are estimated by the method proposed in Ho
calculates the test statistic 'tau' for a unit root in a time series according to the Augmented Dickey-Fuller test. Nonstandard critical values are given according to MacKinnon's response surface. t-values and corresponding probability-values are given for the coefficients of the lagged differences
andrews calculates the semiparametric estimator proposed by Andrews and Schafgans (1994) of the intercept coefficients of the outcome equation in a sample selection model.
This quantlet 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 t
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 the residuals are regressed on the original regressors and the principal components o
Appends an object to the specified list. If the appended object is temporary, the component gets the name el. If the first argument is not a list, it will be changed to a list with itself as first component.
This quantlet 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 sca
estimates the ARIMA(1,d,1) process by Maximum Likelihood and computes diagnostics. Residuals diagnostics include their time plot with two-standard error bounds, correlograms and Ljung-Box statistics with p-values. Parameter diagnostics include their t-statistics with p-values and variance-covarianc
Estimation of ARIMA(p,d,q) models by conditional least squares, where residuals diagnostics and model selection criteria are given. Residuals diagnostics include their timeplot with two-standard error bounds, correlograms and the Ljung-Box statistic with p-values. Computed model selection criteria
Estimation of the ARI(p,d) process by OLS and diagnostics. Residuals diagnostics include their timeplot with two-standard error bounds, correlograms and Ljung-Box statistics with p-values. Parameter diagnostics include their t-statistics with p-values and variance-covariance matrix. Computed model