Econometrics vs hard science

Econometrics vs hard science

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The difference between econometrics and hard science in terms of experimental conditions and causal relationships.

Science Experiment vs. Econometrics

  • In science, experiments aim to establish causal relationships by creating controlled conditions with an experiment and a control group.
  • Econometrics lacks the ability to conduct such controlled experiments due to ethical and practical constraints.
  • An example in econometrics involves studying the effect of military participation on lifetime income, highlighting challenges in establishing causality without experimental conditions.
  • Econometric studies rely on non-experimental data, analyzing variables like military participation and lifetime income across a sample population.
  • Non-experimental data analysis in econometrics faces challenges such as confounding factors like reverse causality, impacting the interpretation of causal effects.
Video description

This video describes the similarities and differences between econometrics and 'hard' science. The example given is attempting to quantify the effect of military spending on lifetime income. A great paper on this subject is given by Angrist (1990), who uses IV estimation to estimate the effect. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti