Marcin Wolski

April 2019

There are many packages dealing with density estimation in R. They offer several advantages over manually coded algorithms, including bandwidth-selection procedures or involving some more complex features of density estimation, like derivative estimation or higher order kernels. Some of them are also coded in native C language, which should speed up the calculations and enhance memory management. Nevertheless, many of these extra features may be often unused in simple applications of density estimations. That leaves the question open: which algorithm is the fastest?

I try to look at the broadest possible set of R packages dealing with density estimation, including

np

kdensity

sm

and

GenKern

. There are some other packages which I skip here, as I wanted to make sure I estimate the density at a given point, and not across the domain, to make the numbers comparable. (As a s...

R programming

March 2019

The R way to explore APIs

Data access is often a nightmare. Especially with irregular data shapes or multiple data types. APIs, or application programming interfaces, offer a simple access gates to the information resources in their native structures, and therefore they offer a powerful tool to quickly boost many research projects.

In a nutshell, an API is a gate through which a user may access the resources or data located on a server in a quick and friendly way. APIs have a generic address, typically in the form of http address, and endpoints. Endpoints direct the user to specific parts of of the database (like tables), the user may need to access. APIs require an authentication key, called a token, which offers the server the access control mechanism. Sometimes you need to pay for a token, but oftentimes some limited functionality is offered for free.

To demonstrate the performance of the API, I will access the trading database of

R programming

February 2019

Testing conditional expectations

I recently came across a problem of testing if the expectations of one variable, call it $Y$ , vary alongside the distribution of another variable, say $X$ . The problem can be approached through several angles, including parametric quantile approach, however, it was decided to use one of the most flexible methods, and actually one of my favorites, i.e. the bootstrap.

The idea is quite simple. Imagine two random variables $Y$ and $X$ . (For more information about the exact definitions of what a random variable is, the Wikipedia page has a lot of useful information.) Given their observed realisations $\{(Y_i,X_i):i=1,...,n\}$ , the goal is to test if the conditional average of $Y$ is statistically different from its unconditional average. We can approximate the former by estimating the mean of $Y$ for different parts of $X$ distribution. For instance, we can test if expectations of $Y$ d...

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Marcin Wolski, PhD
Climate Economist
European Investment Bank
E-mail: M.Wolski (at) eib.org
Phone: +352 43 79 88708

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