# Blog Archives

## Let’s Party!

June 6, 2012
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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

## Optim, you’re doing it wrong?

May 28, 2012
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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

## Time-Series Policy Evaluation in R

May 21, 2012
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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

## Simple Spatial Correlograms for Cross-Country Analysis in R

May 9, 2012
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Accounting for temporal dependence in econometric analysis is important, as the presence of temporal dependence violates the assumption that observations are independent units. Historically, much less attention has been paid to correcting for spatial dependence, which, if present, also violates this independence assumption. The comparability of temporal and spatial dependence is useful for illustrating why

## An ivreg2 function for R

May 3, 2012
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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

## Probit/Logit Marginal Effects in R

April 23, 2012
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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

## Dummies for Dummies

April 19, 2012
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Most R functions used in econometrics convert factor variables into a set of dummy/binary variables automatically. This is useful when estimating a linear model, saving the user from the laborious activity of manually including the dummy variables as regressors. However, what if you want to reshape your dataframe so that it contains such dummy variables?

## Instrumental Variables without Traditional Instruments

April 14, 2012
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Typically, regression models in empirical economic research suffer from at least one form of endogeneity bias. The classic example is economic returns to schooling, where researchers want to know how much increased levels of education affect income. Estimation using a simple linear model, regressing income on schooling, alongside a bunch of control variables, will typically

## Temperature Change in Ireland

April 7, 2012
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Has Ireland gotten any warmer? Ask any punter on the street and they will happily inform you of wild swings, trends and dips. “Back when I was a child”, “when I was younger”, or “years ago” are the usual refrains. What’s the evidence? To answer this, I will use the temperature data from my previous

## Web-Scraping in R

April 2, 2012
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Web-scraping, or web-crawling, sounds like a seedy activity worthy of an Interpol investigative department. The reality, however, is far less nefarious. Web-scraping is any procedure by which someone extracts data from the internet. Given that it’s possible to get the internet on computers these days; web-scrapping opens an array of interesting possibilities to social-science researchers