calculates the d'th difference of the time series x. Furthermore, it allows to calculate the s'th seasonal difference. In terms of the backshift (or lag) operator B, the series y_t = (1-B^s)^d x_t is generated. The default values are s=1 and d=1, which generate the first differences of the series x
Generates a display that shows the time series x in multiple windows with user-specified maximum length per window. It is possible to label the abscissa in a yearly format. However, you can not specify the periodicity of the labels (in that case, use timeplotlabel).
Generates a display that shows the time series x. The abscissa is labelled automatically, when the labels have the format year:1. One may specify the periodicity of the labels.
estimates a dynamic factor model from the form: yt = m0(u) + bt1*m1(u) + bt2*m2(u)... btL*mL(u), where m0 to mL are 2-dimensional invariant basis functions on the grid u and bt0=1. bt1 to btL are scalar weights depending on time T. After estimation, the functions m are orthogonalized under the empi
The teachware quantlet tw1d shows a histogram of the user-defined data and offers an interactive visual analysis of this data by means of box plots (for mean and median) and QQ-plots. Transformations may be applied to the data in order to study the change in distribution and box plots.
teachware quantlet twclt shows a discrete four point distribution and simulates repeated sampling from this apparently non normal distribution. The variation of the observed mean values around the true mean value (standardized by scale) is shown in a plot. The user may interactively change the numb
Shows the functions approximation by wavelets. You can choose between different wavelet base, different number of father wavelet coefficients, different functions and different views to the mother wavelet coefficients.
Compares the data compression of wavelets with fourier basis. You can choose between different wavelet base, different number of father wavelet coefficients, different functions and different views to the mother wavelet coefficients.
Compares the approximation of sines with different frequencies by wavelets. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
Shows the approximation of a sine function which changes its frequency. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
Shows how a hard threshold behaves on the true function and the true function plus noise. You can choose between different wavelet base, different number of father wavelet coefficients, different functions different views to the mother wavelet coefficients, hard threshold by hand and automatically.
Shows how a soft threshold behaves on the true function and the true function plus noise. You can choose between different wavelet base, different number of father wavelet coefficients, different functions different views to the mother wavelet coefficients, soft threshold by hand and automatically.
Shows how a hard threshold behaves on an image and an image plus noise. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
teachware quantlet twlinreg gives visual insight into how least squares simple linear regression works, and the relationship between the regression of Y on X, X on Y, and total regression.
teachware quantlet twnormalize shows the distribution of binomials B(n1, p), B(n2, p) and B(n3, p) with increasing n1, n2, n3. One may shift the distribution by the mean value and divide by the standard deviation in order to study the normalizing effect. In addition a normal density may be graphica
the teachware quantlet twpearson gives a visual demonstration of the form of the Pearson correlation coefficient. In particular, it shows why the product moment gives a measure of "dependence", and why it is essential to "normalize", i.e. to subtract means, and divide by standard deviations, to pre
the teachware quantlet twrandomsample asks for a distribution of the numbers {1, 2, 3, 4}, displays a bar chart of the entered values and calculates a test for H0: p{2,3} = 0.5, the hypothesis of uniform distribution.