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
andrews calculates the semiparametric estimator proposed by Andrews and Schafgans (1994) of the intercept coefficients of the outcome equation in a sample selection model.
2-step estimation of a regression equation in the presence of self-selection. Selection rule is of the probit type (hence, this is a Type 2 Tobit Model in chapter 10 of Amemiya's Advanced Econometrics).
hhmult calculates the H-H statistic to jointly test the specification of the link functions of a polychotomous response model (such as the conditional logit model or the multinomial logit model)
hhtest calculates the H-H statistic to test the specification of the link function of a generalized linear model (such as the logit or probit model), assuming the index is correctly specified.
Estimates a Multi Index model by finding the orthogonal transformation matrix B by the MAVE and the OPG method. The model has the following form: y = g(B*x) + error, where B*x represents the Multi Index.
auxiliary quantlet for adedis. It defines the Nadaraya-Watson estimate of the link as a function of the (estimated) index of continuous explanatory variables. In adedis, the quantlet simpsonint is used to integrate over the function.
powell calculates the semiparametric estimator proposed by Powell (1987) of the slope coefficients of the outcome equation in a sample selection model.
Computes the regression rankscore test of a linear hypothesis based on the dual quantile regression process. It tests the hypothesis that b1 = 0 in the quantile regression model y = x0'b0 + x1'b1 + u. Test statistic is asymptotically Chi-squared with rank(x1) degrees of freedom.
select calculates semiparametric estimates of the intercept and slope coefficients in the "outcome" or "level" equation of a self-selection model. It is the second step of the two-step estimator of these models. It combines the procedures in the quantlets powell (slope estimator) and andrews (inter