readme

README.md

@@ -19,12 +19,12 @@ the current observations for any given parameter value. However, in many
 instances, this likelihood function is untractable.
 
 An alternative approach to estimate the unknown parameters is to minimize a (weighted) distance between
-the empirical [moments](https://en.wikipedia.org/wiki/Moment_(mathematics) (means, variances, etc.) and their theoretical counterparts (which depend on paramaters). If the model is correctly specified, it is natural
+the empirical [moments](https://en.wikipedia.org/wiki/Moment_(mathematics)) (means, variances, etc.) and their theoretical counterparts (which depend on paramaters). If the model is correctly specified, it is natural
 to expect that the "true" parameter values are the ones generating theoretical moments close to the empirical ones. When the function mapping
 the set of parameter values to the theoretical moments (the "expected response function") is known, this method is called
 the [Generalized Method of Moments](https://en.wikipedia.org/wiki/Generalized_method_of_moments).
 Yet, in many cases the expected response function is unknown. This issue may be circumvented by
-simulating the expected response function, which is often an easy task. In this case, the method is called the [Method of Simulated Moments](https://en.wikipedia.org/wiki/Method_of_simulated_moments) or equivalently the **Simulated Method of Moments (SMM)**. This package is designed to facilitate the estimation of economic models via the Simulated Method of Moments.
+simulating the expected response function, which is often an easy task. In this case, the method is called the [Method of Simulated Moments](https://en.wikipedia.org/wiki/Method_of_simulated_moments) or equivalently the **Simulated Method of Moments (SMM)**. This package is designed to facilitate the estimation of economic models via the SMM.
 
 ## Philosophy