(all implemented in ddml): Interactive model yi = g (di , xi ) + ui zi = m(xi ) + vi E [ui |xi , di ] = 0 E [ui |xi ] = 0 As in the Partial Linear Model, we are interested in the ATE, but do not assume that di (a binary treatment variable) and xi are separable...
13 / 40 DDML models The DDML framework can be applied to other models (all implemented in ddml): Interactive model Y = g0(D, X) + U (2) where D is a scalar binary variable and that D is not required to be additively separable from the controls X. In this setting, the parameters...
Double lassoPost selection inferenceWe extend the Heckman (1979) sample selection model by allowing for a large number of controls that are selected using lasso under a sparsity scenario. The standard lasso estimation is known to under-select causing an omitted variable bias in addition to the ...
The group variable, representing the difference in XC ski-specific training experience between groups, was significantly associated with VO2 (b = 39.548, SE = 8.067, t = 4.9, p = 0.003). This result indicates that experienced skiers have a higher SE than novice skiers. ...
HDRS was the dependent variable. Our primary hypothesis was that the interaction of time with group would be significant, with active dTMS being superior to sham at week 4. After that, pairwise comparisons were performed at each time point (contrast command in Stata). Similar analyses were ...
Models 1, 2 and 3 were repeated for neighborhood double disadvantage as the independent variable, with neighborhood double disadvantage replacing urbanicity in the models. Models were fitted using Stata v17 and MLwiN v3.06 statistical software, using the runmlwin command to execute MLwiN within Stata...
A score test of linear trend was conducted for each SNP using a three-level ordinal variable. To minimize false positive results gener- ated from the multiple statistical tests used in our analysis, we applied a false discovery rate (FDR) method to the P values for trend [14]. To ...
Independent variable In this study, we included maternal socio-demographic factors (mother’s age, age at first birth, ethnicity, place of residence, province, education level, occupation, household wealth status, height, iron/folate intake, antenatal care (ANC) visits, parity, delivery by cesarean...
To adjust for potential confounding climatic differences between clusters, we further included a cluster-level climate variable (reflecting mean annual precipitation and evapotranspiration) with the use of data from a high-resolution global raster climate database—the CGIAR Global Aridity Index dataset ...
Moreover, when I try to issue the following command: generate double x = 9223372034619058931 I get the variable *x* with all values being equal to 9223372034619059200, again. I have found a workaround by forcing Stata to read these numbers in as strings. But I would like to try to avoid...