Endogeneity example sentences

"Endogeneity" Example Sentences

1. The endogeneity of the variables was analyzed in the regression model.
2. The endogeneity problem arises when the explanatory variables are correlated with the error term.
3. The identification problem in the econometric model is often caused by endogeneity.
4. The endogeneity bias can lead to invalid inference in the causal relationship.
5. To overcome endogeneity, instrumental variables can be used in the regression analysis.
6. The simultaneous equations model is often used to deal with endogeneity in econometric analysis.
7. The endogeneity issue can be resolved by including more control variables in the model.
8. The Granger causality test can help to determine the endogeneity problem in the time-series data.
9. The Hausman test is commonly used to check for endogeneity in the regression model.
10. The endogeneity assumption is crucial for the validity of the instrumental variable estimator.
11. The endogeneity of the variables can cause spurious correlation in the data.
12. The panel data analysis can help to address the endogeneity problem in the cross-sectional data.
13. The endogeneity problem can influence the estimation and interpretation of the marginal effects in the regression model.
14. The difference-in-differences estimator is robust to endogeneity in the treatment and control group.
15. To address the endogeneity issue, some researchers use natural experiments that provide exogenous variation in the explanatory variables.
16. The presence of omitted variables can also cause endogeneity bias in the regression model.
17. The endogeneity of the variables can also affect the forecasting accuracy of the time-series model.
18. The selection bias can arise due to the endogeneity caused by the self-selection of the treatment and control group.
19. The endogeneity of the variables can lead to biased estimates of the sensitivity analysis.
20. The spatial econometric models can account for the endogeneity caused by the spatial autocorrelation in the data.
21. The use of fixed effects models can help to control for the endogeneity caused by the unobserved time-invariant factors.
22. The endogeneity bias can be reduced by using dynamic panel data models that capture the lagged effects of the explanatory variables.
23. The endogeneity problem can also affect the estimation of the heteroskedasticity-consistent standard errors.
24. The nonlinear models can be used to overcome the endogeneity bias caused by the nonlinearity of the underlying relationship.
25. The endogeneity of the variables can cause the omitted variable bias in the regression model.
26. The instrumental variable technique can provide unbiased estimates of the causal effects in the presence of endogeneity.
27. The use of robust regression techniques can also help to mitigate the endogeneity problem in the data.
28. The endogeneity problem can lead to overestimation or underestimation of the true effect size in the regression model.
29. The endogeneity assumption can also affect the optimal design of the randomized controlled trials.
30. The endogeneity problem can also be caused by the measurement error in the explanatory variables.

Common Phases

1. Endogeneity is a problem in this analysis;
2. The endogeneity of the independent variable must be accounted for;
3. We need to address the issue of endogeneity in our model;
4. Endogeneity can lead to biased estimates;
5. The endogeneity of certain variables can complicate our analysis;
6. Dealing with endogeneity requires careful consideration of the sources of correlation.