the estimates for the components of an additive (partial linear) model are calculated. If the local linear smoother is applied, the first derivatives are calculated as well, additionally the second derivatives if the local quadratic smoother is chosen.
estimates a dynamic semiparametric 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
estimates a dynamic semiparametric 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
fastint estimates the additive components and their derivatives of an additive model using a modification of the integration estimator plus a one step backfit, see Kim, Linton and Hengartner (1997) and Linton (1996)
defines a list with optional parameters in gam quantlets. The list is either created or new options are appended to an existing list. Note that gamopt does accept any values for the parameters without validation.
gintestpl fits an additive generalized partially linear model E[y|x,t] = G(x*b + m(t)). This quantlet offers a convenient interface for GPLM estimation. A preparation of data is performed (inclusive sorting).
interact estimates a model with interaction terms. It is using the marginal integration estimator with a local polynomial smoother. For details see Sperlich, Tjostheim, Yang (1997)
intertest1 is testing for interaction of x_1 and x_2 in an additive regression model. It is looking at the interation estimate and using wild bootstrap. For details see Sperlich, Tjostheim, Yang (1997)
intertest2 is testing for interaction of x_1 and x_2 in an additive regression model. It is looking at the estimate of the mixed derivative of the joint influence and using wild bootstrap. For details see Sperlich, Tjostheim, Yang (1997)
estimation of a bivariate joint influence function and its derivatives in a model with possible interaction. When loc.lin.smoother is chosen you get the function estimate and the first derivatives in the first and second direction, when loc.quadr.smoother is chosen you get the function and the mixe
Additive component analysis in additive separable models using wavelet estimation. An additive component can be tested against a given polynomial form with degree p, e.g. when p is set to zero we test for significant influence of that component. The procedure is presented in Haerdle, Sperlich,
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
Additive component analysis in additive separable models using wavelet estimation. The first (additive) component is tested against a given polynomial form of degree p, e.g., if p=1 is to test linearity, p=0 is to test for significant influence of the first component at all etc. For details see Hae