Read the summary and the most important questions on Modelling Statistical

• 1 Econometrics

• 1.1 Introduction

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• What is the difference between correlation and causation?

  • Correlation: when x increases, y changes by a consistent amount
  • Causation: indicates one event as the result of the occurence of the other event.

• 1.2 The simple regression model

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• What is the error term?

  Error term u, collects all unobserved influences

• How to interpret the conditional mean independence?

  Knowing the value of x, provides no information about u.

• What are fitted/predicted values?

  ^y, ^b,0 ^b1 etc

• How to deal with outliers?

  1. Recorded incorrectly? correct them/remove them
  2. Else leave them since they affect estimates so much.

• How to interpret a log(y)

  Change is in b1*100%

• What are unbiased estimators?

  If the conditional mean independence holds, b^0 = b0,

• 1.2.1 Multiple regression

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• Why do multiple regression models give a beter chance at uncovering causal relationships?

  Explicitly hold other factors constant

• Name two examples of multicollinearity

  1. log(x) + log(x^2) = log(x) + 2log(x)

  
  should become
  (b2+2b3) * log(x)
  
  

  1. Full dummy variable model:

  
  Leave out one of the dummy variables/ leave out the constant b0

• When can a multiple regression model explain a causal interpretation?

  When all variables are exogenous: exogeneity.