The elusive causal relations
Our admiration for technical virtuosity should not blind us to the fact that we have to have a cautious attitude towards probabilistic inferences in economic contexts. We should look out for causal relations, but econometrics can never be more than a starting point in that endeavour since econometric (statistical) explanations are not explanations in terms of mechanisms, powers, capacities or causes. Firmly stuck in an empiricist tradition, econometrics is only concerned with the measurable aspects of reality.
The main reason why almost all econometric models are wrong
By: Lars Syll – Malmö University
…what causal relations?
To properly define causal problems, let alone solve them, requires a vocabulary that resides outside the language of probability theory. This means that all the smart and brilliant statisticians who used joint density functions, correlation analysis, contingency tables, ANOVA, Entropy, Risk Ratios, etc., etc., and did not enrich them with either diagrams or counterfactual symbols have been laboring in vain — orthogonally to the question — you can’t answer a question if you have no words to ask it.
A Statistician’s Reaction to The Book of Why
By: Judea Pearl – UCLA
“The quality of empirical work has increased substantially”
When you read an econometric study done after 2005, the probability that the researcher has failed to take account of an objection that a non-economist will think of is close to zero.
Henderson’s First Law of Econometrics
By: David Henderson – Hoover Institution
The way forward
Undergraduate econometrics instructions is overdue for a paradigm shift in three directions. One is a focus on causal questions and empirical examples, rather than models and math. Another is a revision of the anachronistic classical regression framework, away from explaining economic processes and towards controlled statistical comparisons. The third is an emphasis on modern quasi‐experimental tools.
Undergraduate Econometrics Instruction: Through Our Classes, Darkly
Authors: Joshua D. Angrist, Jörn‐Steffen Pischke
From:MIT, LSE
