You can check for homoscedasticity in Stata by plotting the studentized residuals against the unstandardized predicted values. Assumption #6: Your data must not show multicollinearity, which occurs when you have two or more independent variables that are highly correlated with each other. You can ...
Multicollinearity is a statistical situation that occurs in a regression model when two or more independent variables are highly correlated
Learn about factor analysis - a simple way to condense the data in many variables into a just a few variables.
Assumption #4: Your data must not show multicollinearity, which occurs when you have two or more independent variables that are highly correlated with each other. Assumption #5: There needs to be a linear relationship between any continuous independent variables and the logit transformation of the ...
In an additional robustness check (see Section 4.3.3), we analyze the impact of the sample reduction due to missing advertising and R&D expenses by imputing the missing values and find no substantive changes to the variables of interest. Lastly, after inspecting the value ranges for all ...
There are lots of robustness tests out there to apply to any given analysis. You can test for heteroskedasticity, serial correlation, linearity, multicollinearity, any number of additional controls, different specifications for your model, and so on and so on. In most cases there are actually mul...
The VIF test detects multicollinearity among the independent variables in a multiple regression model. Multicollinearity reduces the statistical significance of the independent variables, and lessens the explanatory power of the model, so avoiding it is crucial. The results of the VIF shown in Table 3...
To ensure that multicollinearity does not bias our calculation models, following Kalnins (2018), we examined each pairwise correlation value above |0.3| in two steps. First, we checked whether the two variables had regression coefficients (cf. regression result tables) of opposite signs if correlat...
We also discuss the relevance of party characteristics such as government status, age or performance in the polls, but due to multicollinearity concerns we exclude them in multivariate analyses. Clearly, there are limits to the generalizability of results relating to one country and one election. ...
To implement these analyses, we need a flexible estimator for multi-equation econometric models that can accommodate non-linearity if need be (i.e., if researchers choose to use a control function approach). MLE is suitable for this purpose and can be implemented in thecmpmodule for Stata, ...