Theoretical Background Many statisticians and data scientists use the correlation coefficient to study the relationship between 2 variables. For 2 random variables, and , the correlation coefficient between them is defined as their covariance scaled by the product of their standard deviations. Algebraically, this can be expressed as . In real life, you can never […]
I don’t understand why any researcher would choose not to use panel/multilevel methods on panel/hierarchical data. Let’s take the following linear regression as an example: , where is a random effect for the i-th group. A pooled OLS regression model for the above is unbiased and consistent. However, it will be inefficient, unless for all […]
Interesting election results in the UK over the weekend, where the Conservatives romped to victory. This was despite a widespread consensus that neither the Conservative or Labour party would get a majority. This was a triumph for uncertainty and random error over the deterministic, as none of the statistical forecasts appeared to deem such a […]
If history can tell us anything about the World Cup, it’s that the host nation has an advantage of all other teams. Evidence of this was presented last night as the referee in the Brazil-Croatia match unjustly ruled in Brazil’s favour on several occasions. But what it is the statistical evidence of a host advantage? […]
In April 2012, I wrote this blog post demonstrating an approach proposed in Lewbel (2012) that identifies endogenous regressor coefficients in a linear triangular system. Now I am happy to announce the release of the ivlewbel package, which contains a function through which Lewbel’s method can be applied in R. This package is now available […]
In April 2012, I wrote this blog post demonstrating an approach proposed in Lewbel (2012) that identifies endogenous regressor coefficients in a linear triangular system. Now I am happy to announce the release of the ivlewbel package, which contains a function through which Lewbel’s method can be applied in R. This package is now available […]
In econometrics, generalized method of moments (GMM) is one estimation methodology that can be used to calculate instrumental variable (IV) estimates. Performing this calculation in R, for a linear IV model, is trivial. One simply uses the gmm() function in the excellent gmm package like an lm() or ivreg() function. The gmm() function will estimate […]
In econometrics, generalized method of moments (GMM) is one estimation methodology that can be used to calculate instrumental variable (IV) estimates. Performing this calculation in R, for a linear IV model, is trivial. One simply uses the gmm() function in the excellent gmm package like an lm() or ivreg() function. The gmm() function will estimate […]
Any instrumental variables (IV) estimator relies on two key assumptions in order to identify causal effects: That the excluded instrument or instruments only effect the dependent variable through their effect on the endogenous explanatory variable or variables (the exclusion restriction), That the correlation between the excluded instruments and the endogenous explanatory variables is strong enough […]
Any instrumental variables (IV) estimator relies on two key assumptions in order to identify causal effects: That the excluded instrument or instruments only effect the dependent variable through their effect on the endogenous explanatory variable or variables (the exclusion restriction), That the correlation between the excluded instruments and the endogenous explanatory variables is strong enough […]
