CIP Session 9: Regression Analysis Flashcards

1
Q

correlation and causation

A
  • relation/association, nut no cause-effect
  • causation: one causes the other
    Strong correlation might suggest underlying causation
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2
Q

Regression Analysis

A

A statistical method of identifying the relationship between one or multiple independent variables (x) and a dependent variable (y)

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3
Q

Types of data

A
  • Experimental (laboratory) evidence:
  • Cross-sectional: data for many subjects at the same time
  • Time series data: Data collected for the same units (N) on one or multiple variables over time
  • Panel/Longitundinal data: collected for the same unit over a given time period
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4
Q

Ordinary Least Square (OLS) estimation and the 5 assumptions for unbiasdness, consistency and efficiency.

A

To select which fitted line fits the data best.
1. the parameters have a linear relationship
2. random sampling
3. there is variation in x
4. the mean of the error should be 0
5. Homoscedasticity; error does not depend on x

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5
Q

Unbiased, consistent and efficient estimator

A
  • Unbiasedness = when you calculate β for various samples, the average for each sample should be roughly the population average
  • Consistency = if you increase the sample size, your β should get closer to the population average Improve the estimator by increasing the sample size.
  • Efficiency = how many data points you need to have a reliable coefficient estimator for β. The more you need, the less efficient.
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6
Q

What can you use to evaluate the fit of a model to data?

A

R2 tells how well the model fits the data, between 0 and 1, 1 is perfect.

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7
Q

What is Omitted Variable Bias and what can you do to solve it?

A

When important variable that influence Y are omitted from the model. To solve use multiple linear regressions and add a sixth assumption: the independent variables must not be milticollinear.

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