Blog Archives

Brazil’s Host Advantage

June 13, 2014
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Brazil’s Host Advantage

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? […]

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The ivlewbel Package. A new way to Tackle Endogenous Regressor Models.

May 15, 2014
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The ivlewbel Package. A new way to Tackle Endogenous Regressor Models.

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 […]

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The ivlewbel Package. A new way to Tackle Endogenous Regressor Models.

May 15, 2014
By
The ivlewbel Package. A new way to Tackle Endogenous Regressor Models.

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 […]

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IV Estimates via GMM with Clustering in R

April 1, 2014
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IV Estimates via GMM with Clustering in R

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 […]

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IV Estimates via GMM with Clustering in R

April 1, 2014
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IV Estimates via GMM with Clustering in R

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 […]

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Within Group Index in R

February 4, 2014
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Within Group Index in R

There are many occasions in my research when I want to create a within group index for a data frame. For example, with demographic data for siblings one might want to create a birth order index. The below illustrates a simple example of how one can create such an index in R.

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Within Group Index in R

February 4, 2014
By
Within Group Index in R

There are many occasions in my research when I want to create a within group index for a data frame. For example, with demographic data for siblings one might want to create a birth order index. The below illustrates a simple example of how one can create such an index in R.

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Detecting Weak Instruments in R

September 23, 2013
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Detecting Weak Instruments in R

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 […]

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Detecting Weak Instruments in R

September 23, 2013
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Detecting Weak Instruments in R

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 […]

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Endogenous Spatial Lags for the Linear Regression Model

August 18, 2013
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Endogenous Spatial Lags for the Linear Regression Model

Over the past number of years, I have noted that spatial econometric methods have been gaining popularity. This is a welcome trend in my opinion, as the spatial structure of data is something that should be explicitly included in the empirical modelling procedure. Omitting spatial effects assumes that the location co-ordinates for observations are unrelated […]

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