Alfonso has noticed that increases in unemployment insurance claims are associated with recessions, and therefore be advocates limits on unemployment insurance so as to prevent recessions. Mary has noticed that most drug addicts once attended schools, and therefore she advocates getting rid of schools so as to prevent drug addiction a. Mary's reasoning suffers from the reverse causality problem, and Alfonso's reasoning suffers from the omitted variable problem. b. The reasoning of both Alfonso and Mary suffers from the reverse causality problem c. The reasoning of both Alfons and Mary suffers from the omitted variable problem d. Alfonso's reasoning suffers from the reverse causality problem, and Mary's reasoning suffers from the omitted variable problem

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Business

Braeden Webb

18 September, 09:26

Alfonso has noticed that increases in unemployment insurance claims are associated with recessions, and therefore be advocates limits on unemployment insurance so as to prevent recessions. Mary has noticed that most drug addicts once attended schools, and therefore she advocates getting rid of schools so as to prevent drug addiction a. Mary's reasoning suffers from the reverse causality problem, and Alfonso's reasoning suffers from the omitted variable problem. b. The reasoning of both Alfonso and Mary suffers from the reverse causality problem c. The reasoning of both Alfons and Mary suffers from the omitted variable problem d. Alfonso's reasoning suffers from the reverse causality problem, and Mary's reasoning suffers from the omitted variable problem

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Jorden · Answer 1

c. The reasoning of both Alfons and Mary suffers from the omitted variable problem

Explanation:

The issue of omitted variables occurs as a result of mis-specification of a linear regression model, which could be either because the impact of the omitted variable on both the dependent variable is unclear, or the evidence was not accessible.

This causes you to omit the variable from your regression, resulting in over-estimation (upward bias) or underestimation (downward) of the influence of one of the other predictor variables.