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 |
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. |
dwade | dwade estimation of the density weighted average derivatives |
heckman | 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 | 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 | hhtest calculates the H-H statistic to test the specifi- cation of the link function of a generalized linear model (such as the logit or probit model), assuming the index is correctly specified. |
metricsmain | loads the libraries needed by the macros in the metrics library |
metricstest | executes some tests for the macros defined in metrics.lib Is invoked by vertest(). |
ndw | ndw is an auxiliary macro of adedis. It defines the nadaraya watson estimate of the link as a function of the (estimated) index of continuous explanatory variables. In adedis the simpsonint routine is used to integrate over this function. |
pandyn | 2nd Step of the 2-stage dynamic GMM |
pandyn | Estimaties dynamic panel data model with GMM |
panfix | Estimation of a static Fixed-Effects-Modell |
panhaus | Hausman Test for a correlation between the regressors and the individual effects |
panrand | Estimation of a static Random-Effects-Modell |
pansort | |
panstats | Computes summary statistics of the variables |
pantime | Subtracts the mean of period t from the model variables. Purpose: Accounting for fixed time effects |
panunit | Computes unit root statistics for panel data |
powell | powell calculates the semiparametric estimator proposed by Powell (1987) of the slope coefficients of the outcome equation in a sample selection model. |
select | select calculates semiparametric estimators of the intercept and slope coefficients in the "outcome" or "level" equation of a self-selection model. select is therefore the second stage of the two-stage procedure used to estimate these models. select does not estimate the coefficients of the "selection" or "decision" equation but requires that the estimated first-step "index" be given as an input. The procedure to estimate the slope coefficients is desribed in Powell (1987). The procedure to estimate the intercept coefficient is desribed in Andrews and Schafgans (1994). select combines the powell and andrews macros of the metrics library. |
sir | Calculates the effective dimension-reduction (edr) directions by Sliced Inverse Regression (Li, 1991) |
sir1 | This macro is the taylored Sliced Inverse Regression method for the "sssm" macro. Here, the macro "sir1" builds a special slice which contains the cases such that y=0 (the value "0" for y symbolically indicates a missing value). Then, nbslices (by default, nbslices=5) other slices are made by splitting the range of the non missing y's into slices of nearly equal weight (i.e. such that they contain nearly the same number of cases). The classical algorithm of S.I.R. is then used with this special slicing. |
sir2 | Calculates the effective dimension-reduction (edr) directions by Sliced Inverse Regression II (Li, 1991) |
sssm | computes the estimates of the slope vectors in the outcome equation and in the selection equation for a semiparametric sample selection model (SSSM). This is a two-stages estimation method: the first one is a taylored Sliced Inverse Regression (S.I.R.) analysis, and, in the second one, two Canonical analyses are conducted in order to convert the estimated EDR directions into estimates of the slopes. The identifiability conditions have to be verified: there exists a component of the explanatory variable X which affects the selection and does not affect the outcome, and there also exists another component of X which affects the outcome and does not affect the selection. If these identifiability conditions are not verified, the program stops. Moreover, the number of explanatory variables for the selection (resp. outcome) equation must be greater or equal than 2. |
tobit | 2-step estimation of a Tobit model |
trimper | trims a given percentage of a (binned) data matrix |
wtsder | computes the weights for derivative estimation for the use with the quatric kernel in the context of binning |