Posts Tagged ‘ Regression Modelling ’

Standard, Robust, and Clustered Standard Errors Computed in R

June 15, 2012
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Standard, Robust, and Clustered Standard Errors Computed in R

Where do these come from? Since most statistical packages calculate these estimates automatically, it is not unreasonable to think that many researchers using applied econometrics are unfamiliar with the exact details of their computation. For the purposes of illustration, I am going to estimate different standard errors from a basic linear regression model: , using the

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Let’s Party!

June 6, 2012
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Let’s Party!

Exploring whether regression coefficients differ between groups is an important part of applied econometric research, and particularly for research with a policy based objective. For example, a government in a developing country may decide to introduce free school lunches in an effort to improve childhood health. However, if this treatment is known to only improve

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Optim, you’re doing it wrong?

May 28, 2012
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Optim, you’re doing it wrong?

Call me uncouth, but I like my TV loud, my beer cold and my optimization functions as simple as possible. Therefore, what I write in this blog post is very much from a layman’s perspective, and I am happy to be corrected on any fundamental errors. I have recently become interested in writing my own

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Time-Series Policy Evaluation in R

May 21, 2012
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Time-Series Policy Evaluation in R

Quantifying the success of government policies is clearly important. Randomized control trials, like those conducted by drug companies, are often described as the ‘gold-standard’ for policy evaluation. Under these, a policy is implemented in/to one area/group (treatment), but not in/to another (control). The difference in outcomes between the two areas or groups represents the effectiveness

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An ivreg2 function for R

May 3, 2012
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An ivreg2 function for R

The ivreg2 command is one of the most popular routines in Stata. The reason for this popularity is its simplicity. A one-line ivreg2 command generates not only the instrumental variable regression coefficients and their standard errors, but also a number of other statistics of interest. I have come across a number of functions in R

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Probit/Logit Marginal Effects in R

April 23, 2012
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Probit/Logit Marginal Effects in R

The common approach to estimating a binary dependent variable regression model is to use either the logit or probit model. Both are forms of generalized linear models (GLMs), which can be seen as modified linear regressions that allow the dependent variable to originate from non-normal distributions. The coefficients in a linear regression model are marginal

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