Causality between two variables:
Describe how we test for causality and why this might be important in conducting empirical analysis.
We have,
NO regression technique, NO statistical analysis at all can test a causal relationship. Causality is no property contained in the data. The ONLY way to adress causality is to perform a controlled experiment, where you know, a priory, that only the arbitrarily changed condition (and nothing else) can be responsible for a possible change in the response. If you have only observational data (what is often the case in econometrics), you can only speculate or hypothesize about causal relations.
In an empirical analysis to check causality between response variable and one or more independent varaible is addressed by using causality test.
Causality between two variables: Describe how we test for causality and why this might be important...
Causality between two variables: Describe how we test for causality and why this might be important in conducting empirical analysis.
3. Causality Define the following terms: Reverse Causality, Omitted Variable Bias, Measurement Error (Note: Give a general definition, not slides'example) Why are these things an issue when we want to make statements like "X causes Y" Two ways in which we try to overcome these issues is using Instrumental Variables and Regressions Discontinuity. Describe these and give an example of it from the lecture. a. b. c.
D ULIWPIHOOLDA2bqui4403T%2f%2fiviJC When the relationship between two or more independent variables needs to be tested, a common tool to use is a regression analysis. Take for example a study that shows the relationship between gaming and teen violence; or a study that shows a correlation between fast food eating habits and obesity. • Describe 2 - 3 combinations of independent and dependent variables that you could test using a regression analysis. What types of results could the regression analysis yield?...
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