Instrumental variable regression: TSLS explained (Excel)

Instrumental variables are a very common and well-established technique to perform causal inference from observational data. When an OLS estimation can be compromised by measurement error, omitted variable bias, or simultaneous causation issues, an instrumental variable (IV) regression can be a fitting solution. Today we are discussing the implementation of instrumental variable regression through two-stage least squares (TSLS), indirect least squares, and matrix algebra based on a famous Acemoglu, Johnson, and Robinson (2001) paper on causal effects of institutional quality on economic growth with log settler mortality as an instrument. We will also consider Excel estimation of IV regression, its assumptions and limitations, and the conceptual idea behind instrumental variables
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[@denizgoktas1535]

Econometrics with Python would be great.

[@drek273]

hello NEDL. can you teach ensemble methods like bagging or boosting in excel?

[@emmanuelsonan6919]

Hi I’m trouble understanding GMM theory can you explain me please

[@rajivshrestha3726]

Can you teach us modelling in Excel wrt. IFRS 9 ? I would be immensely grateful.

[@hyungkyoojeon9291]

Alway appreciate for your explanation. Would you mind to explain DSGE model briefly? Many thanks

[@user-dd4ie2cr1v]

Thank for your work in DCC-GARCH and GARCH model, do you have any interest about DCC-MIDAS and GARCH-MIDAS model? I recently working on that but I don't know how to figure it out

[@jackkeim6267]

Love your stuff! Do you think you could cover option volatility surfaces in python?