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Energy, Economics & Replication

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  • Jeffrey Racine

Abstract

This article outlines recent developments in Markdown scripting languages that facilitate the production of replicable, publication quality, research. The approach is similar to that achieved by using, say, Sweave, R and LaTeX, but is written instead in simple Markdown syntax and not tied to any particular output format (e.g., MS Word) nor computational language (e.g., Python). The computational component can be written in C++, Python, SQL, Stan, Bash, or R by way of example. The Markdown script is seamlessly converted to any one of a number of output formats. The output format is essentially an afterthought, and could be rendered as a PDF (LaTeX or Beamer presentation), MS Word, HTML, EPUB, or gitbook document, by way of illustration. Conversion of the Markdown script to the desired output format is performed by pandoc (a universal document converter). These tools can dramatically reduce the amount of time required to complete a research project that can be trivially replicated. Recent enhancements to RStudio streamline the entire process of output format generation via a simple click of an icon or keystroke shortcut (the minimum requirement is R). Replicability is guaranteed by using the checkpoint package in R. This article was written using Markdown.

Suggested Citation

  • Jeffrey Racine, 2017. "Energy, Economics & Replication," Department of Economics Working Papers 2017-02, McMaster University.
  • Handle: RePEc:mcm:deptwp:2017-02
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    File URL: http://socserv.mcmaster.ca/econ/rsrch/papers/archive/McMasterEconWP2017-02.pdf
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    Cited by:

    1. Orozco, Valérie & Bontemps, Christophe & Maigné, Elise & Piguet, V. & Hofstetter, A. & Lacroix, Anne & Levert, F. & Rousselle, J.M, 2018. "How To Make A Pie: Reproducible Research for Empirical Economics & Econometrics," TSE Working Papers 18-933, Toulouse School of Economics (TSE).

    More about this item

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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